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Electrochemical/Electroflotation Process for Dye Wastewater Treatment
Erick Benjamin Butler Cleveland State University
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ELECTROCHEMICAL/ELECTROFLOTATION PROCESS FOR DYE WASTEWATER TREATMENT
ERICK BENJAMIN BUTLER
Bachelor of Science in Environmental Science
Cleveland State University
December 2007
Master of Science in Environmental Engineering
Cleveland State University
December 2009
submitted in partial fulfillment of requirements for the degree
DOCTOR OF ENGINEERING
at
CLEVELAND STATE UNIVERSITY
AUGUST 2013
DEDICATION
This thesis work is dedicated to my Lord and Savior Jesus Christ. In addition, it is dedicated to memory of loving mother, Mrs. Patricia Butler.
This dissertation has been approved for the Department of Civil and Environmental Engineering and the College of Graduate Studies by
______Dr. Yung-Tse Hung, Dissertation Committee Chairperson Date Professor, Civil and Environmental Engineering Department
______Dr. Walter M. Kocher Date Associate Professor, Civil and Environmental Engineering Department
______Dr. Lili Dong Date Associate Professor, Department of Electrical and Computer Engineering
______Dr. Chung-Yi Suen Date Professor, Mathematics Department
______Dr. Sally Saiilai Shao Date Professor, Mathematics Department
ACKNOWELDGEMENTS
First and foremost, I would like to thank my Lord and Savior Jesus Christ for this opportunity that he has given me to be so intricately involved within this doctoral study— to Him be the Glory. I would also like to thank Him for putting my dissertation advisor,
Dr. Yung-Tse Hung, Ph.D., P.E., DEE, Professor of Civil and Environmental
Engineering, Cleveland State university. Dr. Hung has been a very integral part of all that has been produced within this research. I am eternally grateful for all of the long hours that he has put into me as a student, having known him since Fall Semester 2007.
I would also like to thank my committee members—Dr. Walter Kocher, Associate
Professor of Civil and Environmental Engineering, Dr. Lili Dong, Associate Professor of
Electrical and Computer Engineering, Dr. Chung-Yi Suen, Professor of Mathematics
Department, and Dr. Sally Shao, Professor of Mathematics Department, Cleveland State
University, for agreeing to serve on my committee, along with their suggestions for the improvement of my research conclusions.
I would like to give special thanks to Dr. Yen-Pei Fu, Professor of Materials
Science and Engineering Department, National Dong Hwa University, Taiwan, who is spending his sabbatical leave at Department of Civil and Environmental Engineering,
Cleveland State University during the academic year of 2012-2013, for providing the photo-catalysts for the photo-oxidation research, along with his guidance in not only the experiment process, but also for his assistance in the paper preparations. I would also like to thank his doctoral student, Mr. Ching-Cheng Chen, Department of Material Science and Engineering, National Dong Hwa University, Taiwan, for assisting with not only the preparation of the photo-catalysts, but also his assistance in the formation of the
Langmuir-Hinshelwood equation development. I would also like to thank Dr. Norbert
Delatte, Chairman of Civil and Environmental Engineering for his support and also Ms.
Diane Tupa, Secretary of Civil and Environmental Engineering for her assistance in ordering supplies necessary for the experiments. Also, I would like to thank Mr. Jim
Barker, Technician, Engineering College, Cleveland State University, Mr. Dave Epperly,
Technician, Engineering College, Cleveland State University, Mr. Ali Kaddah,
Technician, Mr. Mohammed Suleiman Al Ahmad, Doctoral student, Department of Civil and Environmental Engineering, Cleveland State University, for their assistance with the preparation of the equipment used within the experiments, along with Mr. Jim Lowe,
General Environmental Science, Inc., for provision of bacteria for experiments. Finally, I would like to thank Mr. Rachit Bhayani, Graduate student, Department of Civil and
Environmental Engineering, Cleveland State University, and Mr. Saketh Thanneeru,
Graduate student, Department of Civil and Environmental Engineering, Cleveland State
University for their assistance in data collection.
Special thanks to my wife, Mrs. Jennifer Butler, for all her patience that she has had with me during this doctoral study. Having her support has helped me daily continue to persevere through all of the difficulties that come with conducting dissertation research. I would also like to thank my father, Mr. Hiram Butler, my mother, late Mrs.
Patricia Butler, my sisters, Ms. Erin and Erionna Butler for their support and love during the research preparation.
I would also like to thank my church family at Richmond Heights Christian
Assembly for their prayers and words of support, especially my pastors, Rev. Michael
Marino, Emeritus, Pastor Bob Hengle, Assistance to the Pastor, and Pastor Alan
Schrader, Senior Pastor. I would also like to thank Mr. Josh Walker. In addition, I would like to those at Bank of America, Beachwood Site, Ms. Carrie McCoy, Site Lead, Mrs.
Deborah Brewer, Mr. Erik Buckland, Mr. Frank Fruhauf, Mr. Arthur Belviso, Mr.
Matthew Blanks, Ms. Angela Knoll, Mr. Matthew Eberwein, Mr. Kris Sobieski, Mr.
David Bowling, Ms. Jennifer Zaleski, and Ms. Rene Smith for their support during this time. Without the schedule flexibility at work, I would not have the ability to complete the research within this dissertation.
I would also like to thank Mr. Shane Bagnall and Jennifer, Zoo Med Laboratories,
San Luis Obispo, CA for their assistance in providing specifications on the UV-B bulb for photo-oxidation experiments. Also, I would like to thank Ms. Theresa Nawalaniec,
Chemistry, Engineering, Mathematics, Physics, and Religious Studies Librarian, Michael
Schwartz Library, Cleveland State University, for her assistance in answering questions concerning references and citation of published and submitted for publication works.
Finally, I also would like to thank the Cleveland State University Research
Council, as this research was carried out with support from the Cleveland State
University Research’s Council Doctoral Disseration Research Expense Award and
Fellowship (DDREAFP) Program.This award provided me the opportunity to purchase all of the necessary supplies needed to conduct the experiments in this dissertation.
ELECTROCHEMICAL/ELECTROFLORATION PROCESS FOR DYE WASTEWATER TREATMENT
ERICK BENJAMIN BUTLER
ABSTRACT
The use of dyes has become very significant across various industries such as textiles, paper, and clothing. The organic chemical composition of dyes is a major concern when discharging wastewater not only into the environment, but also within wastewater treatment plants. Dye effluent consists of high chemical oxygen demand (COD) and also color, components that require treatment before discharge. As a result, federal legislation has required industries that discharge high components in wastewater to undergo treatment within the plants. Within literature, authors have considered various biological, physical, and chemical methods of treating dye wastewater. Recently, electrocoagulation/electroflotation (ECF) has been an additional method of treatment that has been considered for the treatment of dye wastewater. Two separate studies are considered. First, Acid Yellow 11 (AY11) at a concentration of 25 mg/L (by weight) underwent treatment from three different coagulants (Alum, Ferric Sulfate, and Ferric
Chloride) , under three different strengths (5 mg/L, 10 mg/L, and 15 mg/L), and two different initial pH considerations (4 and 7) for the purpose of analyzing color removal.
Following the study, the results were collaborating into a response surface methodology,
vii developing an equation for the three different coagulants. In addition, a Box-Behnken design has been setup for the purpose of considering the effects of pH, dye concentration, dye type, coagulant type and strength on the efficiency of electrocoagulation. These values will be analyzed using statistical analysis, along with toxicity study done on the effectiveness of removing toxic contaminants from the wastewater. Finally, a photo- oxidation study was completed on Acid Orange 7 (AO7) synthetic dye wastewater for the purpose of determining the effects of photo-oxidation based on dye concentration, catalyst type and dose. Langmuir-Hinshelwood coefficients were developed based on the results of this experiment.
Keywords: Electrocoagulation; Electroflotation; Color; Dye; Wastewater Treatment; Response Surface Methodology; Photo-oxidation; Nanocatalysts
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TABLE OF CONTENTS
ABSTRACT ...... vii LIST OF TABLES ...... xiii LIST OF FIGURES ...... xiv CHAPTER 1. INTRODUCTION ...... 1 1.1 INTRODUCTION ...... 1 1.2 OBJECTIVES ...... 3 2. DYE WASTEWATER LITERATURE REVIEW ...... 6 2.1 DEFINITION OF DYE ...... 6 2.2 CLASSIFICATION OF DYES ...... 7 2.3 PROCESSES USED IN DYE PRODUCTION ...... 10 2.4 WASTEWATER CHARACTERISTICS ...... 11 2.5 HISTORY OF DYES ...... 12 2.6 LEGISLATION CONCERNING DYE WASTEWATER ...... 14 2.6.1 CLEAN AIR ACT (1970) ...... 14 2.6.2 CLEAN WATER ACT (1972)...... 15 2.6.3 RCRA (1976) ...... 15 2.6.4 EPCRA (1986) ...... 17 2.6.5 POLLUTION PREVENTION ACT (1990) ...... 17 2.7 DYE WASTEWATER TREATMENT METHODS ...... 18 2.7.1 BIOLOGICAL TREATMENT ...... 19 2.7.2 ADVANCED OXIDATION TREATMENT ...... 29 2.7.3 ACTIVATED CARBON ...... 34 2.7.4 MEMBRANE FILTRATION ...... 38 2.7.5 COAGULATION-FLOCCULATION ...... 41 2.7.8 OZONE ...... 42 2.8 CHAPTER CONCLUSION ...... 44 3. ELECTROCOAGULATION LITERATURE REVIEW ...... 46
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3.1 DEFINITION OF ELECTROCOAGULATION-FLOTATION (ECF) ...... 46 3.2 OPTIMIZATION ...... 49 3.3 MODELING ...... 51 3.3.1. KINETICS ...... 51 3.3.2. COMPUTER MODELING AND STATISTICS ...... 53 3.4 DECOLORIZATION...... 54 3.5 WASTEWATER TREATMENT ...... 59 3.5.1 DOMESTIC WASTEWATER TREATMENT ...... 59 3.5.2 INDUSTRIAL WASTEWATER TREATMENT ...... 60 3.5.3 HEAVY METALS ...... 65 3.5.4 ORGANIC AND INORGANIC REMOVAL ...... 68 3.5.5 PHOTOLYSIS DEGRADATION, ADVANCED OXIDATION ...... 69 3.5.6 ADSORPTION, MEMBRANES ...... 71 3.5.7 AEROBIC, ANAEROBIC PROCESSES ...... 73 3.5.8 DYE REMOVAL ...... 74 3.5.9 PRETREATMENT ...... 75 3.5.10 OTHER TREATMENT ...... 76 3.6 ANALYTICAL AND INSTRUEMTNATION ...... 77 3.6.1 EQUIPMENT ...... 77 3.6.2 TREATMENT EFFICIENCY ...... 78 3.6.3 ELECTRICAL PROPERTIES ...... 78 3.6.4 OPERATING PARAMETERS ...... 79 3.6.5 ENERGY REQUIREMENTS ...... 80 3.7 COMPARISON ...... 81 3.8 COST ANALYSIS ...... 82 3.9 CHAPTER CONCLUSION ...... 86 4. MATERIALS AND METHODS ...... 87 4.1 MATERIALS ...... 87 4.1.1 ELECTROCOAGULATION EXPERIMENT ...... 87 4.1.2 PHOTO-OXIDATION EXPERIMENT ...... 89 4.2 METHODS ...... 90 5. MODELING ...... 91
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5.1 THEORY OF KINETIC MODELS ...... 91 5.1.1 THEORY OF MASS BALANCE ...... 91 5.2 THEORY OF KINETIC RATE CONSTANTS ...... 93 5.3 DOE MODELING METHODS ...... 95 5.3.1 RESPONSE SURFACE METHODS (RSM) ...... 95 5.3.2 BOX-BEHNKEN ...... 96 5.4 RESPONSE SURFACE METHODOLOGY DESIGN ...... 98 5.4.1 MODEL SETUP ...... 99 5.4.2 RESULTS ...... 99 5.4.3 BOX BEHNKEN EXPERIMENT ...... 135 6. ELECTROCOAGULATION RESULTS ...... 165 6.1 RESULTS ...... 165 6.1.1 REPEATABILITY EXPERIMENTS ...... 165 6.1.2 ELECTROCOAGULATION RESULTS ...... 168 6.1.3 KINETIC RESULTS ...... 172 6.1.4 KINETIC RESULTS WITHIN LITERATURE ...... 176 6.2 DISCUSSIONS ...... 181 7. TOXICITY ...... 188 7.1 DEFINITION OF TOXICITY ...... 188 7.2 TYPES OF TOXICITY TESTING IN WASTEWATER TREATMENT ...... 189 7.3 APPLICATION OF BIOASSAYS DURING ELECTROCOAGULATION ...... 190 7.4 APPLICATION OF BIOASSAYS TREATING DYE WASTEWATER ...... 191 7.5 TOXTRAK ...... 193 8. PHOTO-OXIDATION ...... 195 8.1 INTRODUCTION ...... 195 8.2 EXPERIMENTAL SECTION ...... 199
8.2.1 DOPED TIO2 SAMPLES PREPARATION ...... 199 8.2.2 REACTOR SETUP ...... 199 8.2.3 SAMPLE PREPARATION ...... 200 8.2.4 SAMPLE COLLECTION AND ANALYSIS ...... 201 8.3 RESULTS AND DISCUSSION ...... 202
8.3.1 ANALYSIS OF CO-DOPED TIO2 AND FE-DOPED TIO2 ...... 202
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8.3.2 SAMPLE COLLECTION BASED ON NANOCATALSYT TYPE ...... 203 8.4 CONCLUSIONS ...... 209 9. CONCLUSIONS...... 210 9.1 CONCLUSION ...... 210 9.1.1 ELECTROCOAGULATION ...... 210 9.1.2 PHOTO-OXIDATION ...... 211 9.2 RECOMMEDNATIONS ...... 212 REFERENCES ...... 213 APPENDICES ...... 284 A. EXPERIMENTAL EQUIPMENT SET-UP ...... 285 B. RUN PROTOCOLS ...... 301 C. RESULTS FROM ELECTROCOAGULATION EXERIMENT WITH ACID YELLOW 11 ...... 317 D. RESULTS FROM PHOTO-OXIDATION EXPERIMENT ...... 415 E. PROCEDURES ...... 430 VITA ...... 440
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LIST OF TABLES
Table I. Run Protocol for RSM Model for Alum...... 302
Table II. Run Protocol for RSM Model for Fe2(SO4)3...... 306
Table III. Run Protocol for RSM Model for FeCl3...... 309 Table IV. Run Protocol for Acid Yellow 11...... 312 Table V. Run Protocol for Response Surface Methodology...... 314 Table VI. Run Protocol for Photo-oxidation experiments...... 316 Table VII. Color removal at each data point based on the created RSM for Alum...... 100 Table VIII. Estimation of Regression Coefficients from Minitab Output for Alum...... 103
Table IX. Results from experiments involving Fe2(SO4)3...... 112
Table X. Estimation of Regression Coefficients from Minitab Output for Fe2(SO4)3...... 115
Table XI. Results from experiments involve the addition of FCl3...... 124
Table XII. Estimation of Regression Coefficients from Minitab Output for FeCl3...... 127 Table XIII. Coding for Box Behnken experiments...... 136 Table XIV. Analysis of Variance (ANOVA) of the Response Surface Methodology...... 140 Table XV. Estimation of Regression Coefficients from Minitab Output...... 142 Table XVI. Summary results of the experiments...... 160 Table XVII. Kinetic Rate constants for various coagulants...... 406 2 Table XVIII. Comparison of Lagergren model variables (qe, k1, k2, r ) for various coagulants based on current densities (18.69, 24.92, 31.15 A/m2)...... 414 Table XIX. Kinetic Rate constant and Langmuir-Hinshelwood adsorption constant for each catalyst and dose...... 428 Table XX. Kinetic experimental rate constant (1/kexp), 1/kexp, and the rate of decolorization (r) for each catalyst, dose, and dye concentration of Acid Orange 7...... 429
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LIST OF FIGURES
Figure I. Electrocoagulation Reactor Setup...... 286 Figure II. Inside Electrocoagulant Reactor with electrodes...... 287 Figure III. New sacrificial Aluminum electrodes (left: anode; right: cathode)...... 288 Figure IV. Plexiglass panel...... 289 Figure V. YiHua DC Power Supply...... 290 Figure VI. Spent electrodes after treatment (left: cathode; right: anode). Notice that cathode has been corroded due to hydrogen gas, while the anode has been oxidized. Electodes have been used a maximum of 60 minutes treatment time...... 291 Figure VII. Photo-oxidation Reactor 1 beaker and UVB lamp...... 292 Figure VIII. Photo-oxidation Reactor 1 beaker and stirrer...... 293 Figure IX. Photo-oxidation Reactor 1 lamp ontop of reactor (light is turned off)...... 294 Figure X. Photo-oxidation Reactor 1 lamp ontop of reactor (light is turned on)...... 295 Figure XI. Photo-oxidation Reactor 1 beaker and UVB lamp...... 296 Figure XII. Photo-oxidation Reactor 2 beaker and sitrrer...... 297 Figure XIII. Photo-oxidation Reactor 2 lamp ontop of reactor (light is turned off)...... 298 Figure XIV. Photo-oxidation Reactor 1 lamp ontop of reactor (light is turned on)...... 299
Figure XV. Synthesized nanocatalysts (left: Fe-doped TiO2; right: Co-doped TiO2)...... 300 Figure XVI. Contour Plots of Square Interactions for Color Removal for Alum...... 104 Figure XVII. Surface Plot of Color Removal vs Current Density, Time for Alum...... 105 Figure XVIII. Surface Plot of Color Removal vs pH, Time for Alum...... 106 Figure XVIX. Surface Plot of Color Removal vs Dose, Time for Alum...... 107 Figure XX. Surface Plot of Color Removal vs pH, Current Density for Alum...... 108 Figure XXI. Surface Plot of Color Removal vs Dose, Current Density for Alum...... 109 Figure XXII. Surface Plot of Color Removal vs Dose, pH for Alum...... 110
Figure XXIII. Contour Plots of Color Removal for Fe2(SO4)3...... 116
Figure XXIV. Surface Plot of Color Removal vs Current Density, Time for Fe2(SO4)3...... 117
Figure XXV. Surface Plot of Color Removal vs pH, Time for Fe2(SO4)3...... 118
Figure XXVI. Surface Plot of Color Removal vs Dose, Time for Fe2(SO4)3...... 119
Figure XXVII. Surface Plot of Color Removal vs pH, Current Density for Fe2(SO4)3...... 120
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Figure XXVIII. Surface Plot of Color Removal vs Dose, Current Density for Fe2(SO4)3...... 121
Figure XXIX. Surface Plot of Color Removal vs Dose, pH for Fe2(SO4)3...... 122
Figure XXX. Contour Plots of Color Removal for FeCl3...... 128
Figure XXXI. Surface Plot of Color Removal vs Current Density, Time for FeCl3...... 129
Figure XXXII. Surface Plot of Color Removal vs pH, Time for FeCl3...... 130
Figure XXXIII. Surface Plot of Color Removal vs pH, Time for FeCl3...... 131
Figure XXXIV. Surface Plot of Color Removal vs pH, Current Density for FeCl3...... 132
Figure XXXV. Surface Plot of Color Removal vs Dose, Current Density for FeCl3...... 133
Figure XXXVI. Surface Plot of Color Removal vs Dose, pH for FeCl3...... 134 Figure XXXVII. Johnson Transformation for % Color Removal (Probability Plot for Original Data vs Probability Plot for Transformed Data). Anderson-Darling values and p-values (p- values <0.005 = data is not normal)...... 139 Figure XXXVIII. Contour Plots of Color Removal (Johnson Transformation)...... 144 Figure XXXIX. Surface Plot of Color Removal (Johnson Transformation) vs pH, Dye...... 145 Figure XL. Surface Plot of Color Removal (Johnson Transformation) vs Current Density, Dye...... 146 Figure XLI. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, Dye...... 147 Figure XLII. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Dye...... 148 Figure XLIII. Surface Plot of Color Removal (Johnson Transformation) vs Dye weight, pH...... 149 Figure XLIV. Surface Plot of Color Removal (Johnson Transformation) vs Dye weight, pH...... 150 Figure XLV. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, pH...... 151 Figure XLVI. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, pH...... 152 Figure XLVII. Surface Plot of Color Removal (Johnson Transformation) vs Current Density, Dye Weight...... 153 Figure XLVIII. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, Dye Weight...... 154 Figure XLIX. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Coagulant...... 155 Figure L. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Current Density...... 156 Figure LI. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, Current Density...... 157 Figure LII. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Dye Weight...... 158
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Figure LIII. Surface Plot of Color Removal (Johnson Transformation) vs Dye weight, Dye...... 159 Table XVI. Summary results of the experiments...... 161 Figure LIV. Main Effect Plot for Color Removal...... 162 Figure LV. Repeated experiments comparing spent and virgin electrodes (Control (spent) = 3
A after 5 A; Control (virgin); Coagulant (Fe2(SO4)3 (spent) = 3 A after 5 A; Coagulant
(Fe2(SO4)3 (virgin))...... 167 Figure LVI. Repeated experiments comparing Reactors 1 and 2 for photo-oxidation experiments...... 167 Figure LVII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =4, coagulant = Alum, Current Density = 18.69 A/m2)...... 318 Figure LVIII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =7, coagulant = Alum, Current Density = 24.92 A/m2)...... 319 Figure LIX. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Alum, Current Density = 18.69 A/m2)...... 320 Figure LX. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =4, coagulant = Alum, Current Density = 24.92 A/m2)...... 321 Figure LXI. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =7, coagulant = Alum, Current Density = 24.92 A/m2)...... 322 Figure LXII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Alum, Current Density = 24.92 A/m2)...... 323 Figure LXIII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =4, coagulant = Alum, Current Density = 31.15 A/m2)...... 324 Figure LXIV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =7, coagulant = Alum, Current Density = 31.15 A/m2)...... 325 Figure LXV. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Alum, Current Density = 31.15 A/m2)...... 326 Figure LXVI. Normalized plot Acid Yellow 11 Concentration vs Current Density at Treatment Time = 30 minutes (AY = 25 mg/L, pH = 4, coagulant = Alum)...... 327 Figure LXVII. Normalized plot Acid Yellow 11 Concentration vs Current Density at Treatment Time = 30 minutes (AY = 25 mg/L, pH = 7, coagulant = Alum)...... 328 Figure LXVIII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =4, coagulant = Fe2SO4)3, Current Density = 18.69 A/m )...... 329 Figure LXIX. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =7, 2 coagulant = Fe2SO4)3, Current Density = 19.38 A/m )...... 330 Figure LXX. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and 2 coagulant doses (AY = 25 mg/L, coagulant = Fe2(SO4)3, Current Density = 18.69 A/m )...... 331
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Figure LXXI. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =4, 2 coagulant = Fe2SO4)3, Current Density = 24.92 A/m ). [Please Note: Values at 10 minutes were not collected for these experiments]...... 332 Figure LXXII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =7, coagulant = Fe2SO4)3, Current Density = 24.92 A/m )...... 333 Figure LXXIII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and 2 coagulant doses (AY = 25 mg/L, coagulant = Fe2(SO4)3, Current Density = 24.92 A/m )...... 334 Figure LXXIV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =4, coagulant = Fe2SO4)3, Current Density = 31.15 A/m )...... 335 Figure LXXV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =7, coagulant = Fe2SO4)3, Current Density = 31.15 A/m )...... 336 Figure LXXVI. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and 2 coagulant doses (AY = 25 mg/L, coagulant = Fe2(SO4)3, Current Density = 31.15 A/m )...... 337 Figure LXXVII. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 4, coagulant = Fe2(SO4)3)...... 338 Figure LXXVIII. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 7, coagulant = Fe2(SO4)3)...... 339 Figure LXXIX. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =4, coagulant = FeCl3, Current Density = 18.69 A/m )...... 340 Figure LXXX. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 4, coagulant = FeCl3)...... 341 Figure LXXXI. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 7, coagulant = FeCl3)...... 342 Figure LXXXII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =7, coagulant = FeCl3, Current Density = 18.69 A/m )...... 343 Figure LXXXIII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and 2 coagulant doses (AY = 25 mg/L, coagulant = FeCl3, Current Density = 18.69 A/m )...... 344 Figure LXXXIV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =4, coagulant = FeCl3, Current Density = 24.92 A/m )...... 345 Figure LXXXV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =7, coagulant = FeCl3, Current Density = 24.92 A/m )...... 346 Figure LXXXV. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and 2 coagulant doses (AY = 25 mg/L, coagulant = FeCl3, Current Density = 24.92 A/m )...... 347 Figure LXXXVI. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH 2 =4, coagulant = FeCl3, Current Density = 31.15 A/m )...... 348 Figure LXXXVII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, 2 pH =7, coagulant = FeCl3, Current Density = 31.15 A/m )...... 349
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Figure LXXXVIII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and 2 coagulant doses (AY = 25 mg/L, coagulant = FeCl3, Current Density = 31.15 A/m )...... 350 Figure LXXXIX. Normalized plot of Absorbance for Acid Orange 7 vs Time for various pH 2 and coagulant doses (AO7 = 20 mg/L, coagulant = FeCl3, Current Density = 31.15 A/m )...... 351 Figure XC. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 352 Figure XCI. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 353 Figure XCII. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 354 Figure XCIII. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 355 Figure XCIV. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 356 Figure XCV. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 357 Figure XCVI. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 358 Figure XCVII. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 359 Figure XCVIII. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m3...... 360 Figure XCIX. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m3...... 361 Figure C. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 362 Figure CI. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 363 Figure CII. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 364 Figure CIII. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 365 Figure CIV. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 366 Figure CV. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 367
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Figure CVI. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 368 Figure CVII. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 369
Figure CVIII. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 370
Figure CIX. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 371
Figure CX. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 372
Figure CXI. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 373
Figure CXII. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 374
Figure CXIII. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 375
Figure CXIV. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 376
Figure CXV. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 377
Figure CXVI. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 378
Figure CXVII. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 379
Figure CXVIII. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 380
Figure CXIX. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 381
Figure CXX. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 382
Figure CXXI. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 383
Figure CXXII. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 384
Figure CXXIII. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 385
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Figure CXXIV. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 386
Figure CXXV. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 387
Figure CXXVI. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 388
Figure CXXVII. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 389
Figure CXXVIII. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2...... 390
Figure CXXIX. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 391
Figure CXXX. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 392
Figure CXXXI. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2...... 393
Figure CXXXII. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 394
Figure CXXXIII. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 395
Figure CXXXIV. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2...... 396
Figure CXXXV. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 397
Figure CXXXVI. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 398
Figure CXXXVII. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2...... 399
Figure CXXXVIII. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 400
Figure CXXXIX. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 401
Figure CXL. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2...... 402
Figure CXLI. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 403
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Figure CXLII. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 404
Figure CXLIII. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2...... 405 Figure CXLIV. Lagergren pseudo-first order model for best removal efficiencies with Alum added at various current densities (18.69, 24.92, 31.15 A/m2)...... 408 Figure CXLV Lagergren pseudo-second order model for best removal efficiencies with Alum added at various current densities (18.69, 24.92, 31.15 A/m2)...... 409 Figure CXLVI. Lagergren pseudo-first order model for best removal efficiencies with 2 Fe2(SO4)3 added at various current densities (18.69, 24.92, 31.15 A/m )...... 410 Figure CXLVII. Lagergren pseudo-second order model for best removal efficiencies with 2 Fe2(SO4)3 added at various current densities (18.69, 24.92, 31.15 A/m )...... 411
Figure CXLVIII. Pseudo first-order kinetics for best removal efficiencies with FeCl3 added at various current densities (18.69, 24.92, 31.15 A/m2)...... 412
Figure CXLIX. Pseudo second-order kinetics for best removal efficiencies with FeCl3 added at various current densities (18.69, 24.92, 31.15 A/m2)...... 413 Figure CL. Band gap energy (from top to bottom: undoped TiO2 , Fe-doped TiO2, and Co- doped TiO2)...... 416 Figure CLI (a). Normalized –ln(C/Co) vs time (mins) for Co-doped TiO2 with a dosage of 0.25 g/L...... 417 Figure CLI (b). Normalized –ln(C/Co) vs time (mins) for Co-doped TiO2 with a dosage of 0.50 g/L...... 418 Figure CLI (c). Normalized –ln(C/Co) vs time (mins) for Co-doped TiO2 with a dosage of 1 g/L...... 419 Figure CLI(d). Normalized –ln(C/Co) vs time (mins) for Fe-doped TiO2 with a dosage of 0.25 g/L...... 420 Figure CLI (e). Normalized –ln(C/Co) vs time (mins) for Fe-doped TiO2 with a dosage of 0.50 g/L...... 421 Figure CLI (f). Normalized –ln(C/Co) vs time (mins) for Fe-doped TiO2 with a dosage of 1.0 g/L...... 422 Figure CLII. Normalized concentration (mg/L) vs time (minutes) for Co-doped TiO2 and Fe- doped TiO2 with Acid Orange 7 dose concentration of 24 mg/L...... 423 Figure CLIII. Normalized concentration (mg/L) vs time (minutes) for Co-doped TiO2 and Fe- doped TiO2 with Acid Orange 7 dose concentration of 34 mg/L...... 424 Figure CLIV. Normalized concentration (mg/L) vs time (minutes) for Co-doped TiO2 and Fe- doped TiO2 with Acid Orange 7 dose concentration of 44 mg/L...... 425 Figure CLV. 1/kexp (min-1) versus concentration (mg/L) for each Co-doped TiO2 dose...... 426
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Figure CLVI. 1/kexp (min-1) versus concentration (mg/L) for Fe-doped TiO2 dose...... 427
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CHAPTER 1
INTRODUCTION
1.1 INTRODUCTION One component that everyone has everyday contact with regardless of geographical and cultural differences is dye. It can be estimated that 7 x 105 million tons of dye is available worldwide (1). Dyes are present in many essentials—fabrics for clothes and bags possess many textile dyes, while coloring dyes are used for food and beverages.
However, from an environmental aspect, dye wastewater production from manufacturing and textile industries are problematic.
The biggest problem that is prevalent in dye production is found in its high color and organic content (2). One can argue this point is due to the chemical characteristics of the dyes themselves. Many dyes consist of several aromatics such sulfo, nitro, amidocyanogen, chloro compounds (3). Another issue with dye production is because dye manufacturing processes produce various types of wastes, where the primary pollutants include volatile organic compounds, nitrogen oxides, HCl, and SOx. Additional properties of dye wastewater from the industrial industry involve high biochemical oxygen demand (BOD), chemical oxygen demand (COD), and suspended solids (SS). For example, vast dyes contain 25 kg/kg (or 25 ppm where 1 kg/kg = 1 x 106 mg/L using the
1 density of water as 1 kg/L), 85 ppm COD, while other dyes typically have a BOD of 6 ppm, COD of 25 ppm, and suspended solids of 6 ppm (4). Without proper regulation and treatment, environmental and human health will experience various potential dangers.
One of the reasons for the potential dangers involves the interactions between any discharged dyes and surrounding world. Expounding from the previous statistic, it can be stated that out of the 7 x 105 million tons available, only 2% of dyes are discharged as aqueous and 10% by means of coloration. The remaining 88% retain of discharged dyes retain their color due to sunlight, soils, bacteria, sweat, and degradation of microbes within the wastewater. An additional issue that is attributed to the environmental problem is that some dyes have long half-lives, for example, Reactive Blue-19 has a half-life of 46 years. Because of some dyes have very long-half lives, their impacts can further linger.
Some dyes can alter photosynthesis in plants (1), specifically in plants within water bodies because of the ceasing of light penetrating within receiving waters (5). Other impacts to the environment involve the health and vitality of stream, including undesired pollution, eutrophication, and toxicity (2).
It is very evident that as dye production has become very significant within human society, the characteristics of dye wastewater are the resultant of many issues for public health and the environment. Some of the issues include the toxicity, color, and biodegradability issues, halogenated organic compounds, heavy metals and surfactants, and high BOD (6).
While there is a plethora of literature that has attempted to individually describe the chemistry, classifications, legislation, and various tangible methods of dye treatment, the
2 purpose of this text is to overlay all of these significant pieces for the purpose of truly highlighting the background for one to conclude there is a need for proper treatment of dye wastewater. However, this text does not serve is as an all-extensive guide to describe all of the contemporary methods of treating dye wastewater. Nevertheless, this text hopes to provide a starting point for those looking for background and resources that will lead them to discover more within this field of study. While there are many industries that use dyes, the majority of focus will be primarily on what is being employed within the textile dye industry.
1.2 OBJECTIVES
If one were to allocate a statement in regards to this research it would be the following: modeling electrocoaglation treatment of dye wastewater is capable of not only indicating the setting necessary for treatment optimization, but also can predict how dye compositions affects treatment performance of the electrocoagulation treatment process.
In addition, considering toxicity can also assist in analyzing the overall effects of treatment for public health and awareness.
Supporting the previous thesis statement will include the following objectives:
1. Derive an equation to determine interactions between dye composition and
other optimal parameters Literature has documented several parameters that have
been analyzed within the treatment of dye wastewater by electrocoagulation.
However, dye composition is a parameter that has not been heavily considered.
Developing an equation that includes interactions between known optimal
3 parameters and dye composition will provide additional information on predicting the optimal treatment of dye removal as it is specific to a given dye.
2. Contribute additional results to response surface methodology (RSM) for electrocoagulation experiments. This experiment has chosen to use the Box
Behnken design to generate results. With dye composition being the focal parameter (variable) considered, Box Behnken design is ideal to attempt to generate results based on complete one replicate of data points. Also, the experiment will attempt to find curvature relationships (quadratic/cubic fits) in attempt to explain phenomena by using response surface methodology.
3. Consider secondary treatment by the use of photochemical oxidation. The use of a secondary treatment process, photochemical oxidation will be able to improve treatment following the use of electrocoagulation. Having a combination of photochemical oxidation and electrochemical treatment process for the purpose of reducing wastewater characteristics and strengthen the quality of effluent wastewater.
4. Determine the effectiveness of removing toxicity from toxic dyes.
Photochemical oxidation and electrocoagulation treatment efficiency is not mere the removal of total organic carbon (TOC) and transmissivity. It is also important to consider how effective the treatment processes are capable of removing toxic components by using bioassays for the purpose of analyzing toxicity.
5. Publish papers that will be found within a peer-reviewed journal. Currently, a literature review for electrocoagulation has been published within an online peer-
4 reviewed journal. The journal is based on literature from three years—2008 to
2010. The objective of this research is to publish results that would make the contributions notable within the scientific community.
5
CHAPTER 2
DYE WASTEWATER LITERATURE REVIEW
With a few omissions, this chapter has been taken from its entirety from a book chapter submitted for publication in 2012 with CRC Press book chapter : “Dye Wastewater
Trends and Treatment Processes Employed for the Treatment of Dye Wastewater
Highlighted in the Textile Industry,” in Advanced Industrial Wastewater by Erick Butler,
Dr. Yung-Tse Hung, ed (7).
2.1 DEFINITION OF DYE
A dye can be defined as “a colored substance which when applied to the fabrics imparts a permanent color and the color is not removed by washing with water, soap, or an exposure to sunlight (8).” Dyes are within a subcategory known as colorants, entities that emit light within the visible range (400-700 nm). Most colorants can be described as being inorganic and organic chemical structures, and also divided into two types—dyes or pigments. Pigments have been defined as a group of dyes that are combined with several compounds or substrates, while dyes are placed onto a compound or substrate (9).
6
In industry, it is important to recognize that the terminology is quite important.
Two terms that are necessary to define are dye baths and affinity. The places of application of dyes specifically within the textile industry are known as dye baths. The next section discusses the processes in which dye baths are used for implementation.
Affinity can be best described as the alignment of a particular dye with a given fabric.
For example, acid dyes have an affinity to (applied to) nylon and wool, typically because of the formation and also the formation of acid dyes and also the type of fabric wool and nylon are. Therefore, because of these entities, acid dyes are more susceptible to be applied to wool and nylon as compared to other components. One of the biggest problems with dyes is that contribute to a large production of waste. It has been estimated that 50-100% of all dye is based onto the particular entity, while the remained is discharged into the dyebath solution (10,11).
2.2 CLASSIFICATION OF DYES
There are two major classifications of dyes—chemical and waste generation. The
Environmental Protection Agency Office of Toxic Substances has classified fourteen different types based on chemical composition—acid, direct (substantive), azoic, disperse, sulfur, fiber reactive, basic, oxidation, mordant (chrome), developed, vat, pigment, optical fluorescent, and solvent dyes (12). This text will describe six of the fourteen major dye types.
First, acid dyes have been stated as being sodium salts from sulphonic or phenolic groups, where the negative ions determine the dye’s color. Acid dyes are dipped into a hot constituent of the dye present with either a salt or an acid. Examples of an acid
7 include maritus yellow, orange II, and naphthol yellow (8). In addition, acid dyes are a dye classification of higher molecular weight that resulted in the reaction between the dye and the fiber development of an insoluble color molecule on the fiber with processes at temperatures greater than 39 degrees Celsius. Acid dyes are further subdivided into three categories—azo, anthraqunione, and triarylmethane (12).
On the contrary, direct dyes are applied to a given fiber within a solution that combines the fabric with the dye using an aid of ionic salts or other electrolytes, where the physical characteristics of the dye have a high solubility in cold water (12). Direct dyes comprise of azo dyes, a series of manmade compounds that are adsorbed on cellulose (6).
The unique characteristic of absorption by the use of electrolytes, also known as substantivity, direct dyes are only applied to cellulose materials such as cotton, jute, viscose, or paper. Substantivity happens when the application of dye molecules increase in size within open cavity spaces in the fibers too substantial to be removed by the use of water or any agents and are permanently affixed to the fabric (13). Azoic dyes form insoluble color molecules on a fiber by means of combining two soluble solutions at temperatures between 16 to 27 degrees Celsius (12).
Disperse dyes are the fourth of fourteen dye classifications that has a characteristic of having low solubility combined to fabrics of entities such as nylon and polyester by means of being applied in a dye bath under the process of colloidal absorption. The process begins by using high temperatures to the dye for the purpose of transferring the dye from the solid to gaseous state for the purpose of embedding into the
8 fabric. Once a dye has been embedded into the fabric, it is then solidified through condensation as a colloid, and permanently made a part of the dye (12).
Sulfur dyes are a series of dyes that are applied to cotton and rayon fabrics by transferring the dye from reduced water soluble to an oxidized insoluble form. Fiber reactive dyes are a series of dyes that form covalent bonds with the fiber molecules (12).
In regards to waste generation, the Environmental Protection Agency has classified textile dye waste into four sections—dispersive, hard-to-treat, high-volume, and hazardous and toxic. When dyes have become integrated from various processes such as finishing, dye, printing, and preparation, machine cleaning, pastes, and solutions collaborated that were not used within the processing are known as dispersive wastes.
The solution to dispersive wastes is usually two-fold: first, a textile mill plant is capable of capturing the wastes either combined streams from the various processes or the physical removal from the machines used in the plant (12). The alternative solution is to develop pollution prevention methods. Later within the chapter Pollution prevention methods will be discussed.
Hard-to-treat wastes are a series of wastes that are very difficult to remove from textile mill plant processes where at times these wastes become a hindrance towards the production within the plant. The majority of hard-to-treat wastes are classified as being typically non-biodegradable or inorganic due to the fact that biological processes are incapable of reducing their concentrations. When these waste streams enter through a wastewater treatment plant, they will pass through activated sludge and other wastewater treatment processing units. Some of the major wastes such as color, metals, surfactants,
9 and toxic compounds are considered examples of hard-to-treat wastes. These major waste categories derive from dye and printing. High-volume wastes such as wastewater, salts, and knitting oils is the category of waste that is generated in the highest quantity (12).
2.3 PROCESSES USED IN DYE PRODUCTION
Regardless of industry, there are many different commonalities that are found similar for the application of dye to a particular substance. For example, if one looks at the textile industry, one will find that dyeing processes can happen at various stages of textile production, including the congregation of yarn, fabric, or garments. The process of dyeing occurs as a subset within a process known as wet processing. Wet processing is the period within textile industry where textiles will undergo a series of applications of water for the purpose of making the textile to be strengthen, last longer, and be more durable. Fabric within this stage is usually known as being unfinished or “griege goods,” as described within the industry. Situated within this particular process, dyeing is the second of three stages following the preparation stage, where dyes are developed for the purpose of receiving the application of dye to the fabric. Within these processes, there can be the application of dye either as a continuous process or within batch mode (10,11).
When one uses the batch process, the 100 to 1000 kilograms of the fabric is combined with the particular dye within a machine. There are three phases of dyeing— application, fixation, and washing. Dye fixation is quantitative amount of dye that is applied to the fabric during the interaction between the fabric and the dye by the use of either additional chemicals or heat within the dye bath that actually is transmitted directly into the fabric in a given period of time, either in a few moments or several hours. Having
10 had the application of dye affixed to the fabric, additional washing occurs for the purpose of removing any substances that have not fully been embedded within the fabric (10,11).
This dye process contrasts with batch dye application, because of it is expedience.
Instead of having weighted a particular amount of fabric, the continuous process is based on length, where application is completed at rates between 50-250 meters per minute, where the fabric will undergo similar processes as batch processes, with the exception of higher intensity, where one can easily process 2,000 meters of fabric within as little as 8-
40 minute minutes. This method is more preferred within the textile industry (60%) as compared with the batch method as it is more economical in nature (10,11).
2.4 WASTEWATER CHARACTERISTICS
From the textile industry, it has been estimated that 90% of wastewater or 90,000 tons that goes untreated, while only 10% is recycled. In addition, 10-15 percent of a worldwide 700,000 tons of dye production is discarded as waste. For the wastewater characteristics, it has been found that the majority of the wastes from the dyeing industry is from the various dyeing processes (continuous and batch), alkaline preparation, and also the constituents from dyeing such as salts included within some of the chemical mechanisms to develop dyes for the various processes in the industry (10).
Specifically, the characteristics of effluent included high biochemical oxygen demand (BOD), chemical oxygen demand (COD), where an estimated concentrations include the highest BOD was in wool scouring , complex processing, and carpeting finishing (2270, 420, and 440 mg/L respectively). In addition, chemical oxygen demand
(COD) has been observed as being 2 and 13 times more than the BOD concentrations—
11 as COD concentration was at 7030 mg/L as compared to 2270 mg/L for wool scouring wastewater production. Other components that have been witnessed include the presence of total suspended solids (TSS), where wool scouring reported 3310 mg/L and oil and grease (O&G) (11). When one considers dyeing wastewater specifically, the wastewater consists of metals, salts consisting of magnesium chloride and potassium chloride (2000 to 3000 ppm), surfactant, toxics, color, BOD, COD, sulfide, and solvents to name a few
(10).
Finally, toxicity of the wastewater has been observed as being varying based on the presence of constituents. From a test of 75 textile mills, it was observed that 38 or over 50% of the 75 textile mills had no toxicity, while approximately 9% had toxic components (9). Potential sources of toxicity include salts from the dyeing status, surfactants, metals from the dyes, organics. Specifically with dyes, it was found that 63% of 46 tested commercial dyes had a toxicity range using the lethal concentration (LC50) value, or the concentration required to kill 50% of a given population, measured greater than 180 ppm or having little toxicity, while only 2.2% were considered toxic (10).
2.5 HISTORY OF DYES
The presence and use of dyes has been prevalent since the beginning of time.
Archeologists have found linens in Egypt that can be traced back as early as 5000 BC.
Within the Bible, Moses describes to the newly freed Israelites the use of fibers and dyes for the purpose of preparing priestly garments for Aaron and the future line of priests. It also has been seen that dyes were used as extracts from various natural sources. For example, dye was created from brazilwood in Brazil, camine red was extracted from
12 female cochineal Mexican or Peruvian cactus, and indigo for blue dyes was from Indian vegetable leaves (14). However, author Dutfield stated that there are four major stages involved with the infant history of synthetic dyes. The first stage for the origin of dyes has been commonly traced to 1856 by a British student named William Perkin that accidentally discovered aniline purple, a coal tar dye having attempted to synthesize quinine. Following the formation of aniline purple, Francoise Verguin discovered aniline red in 1859 having patented the dye in both France and Britain. Coal tar dyes were replaced by azo dyes in 1856, when two German chemists Heinrich Caro and Carl
Maritus discovered both Manchester Yellow and Brown. Three years later, the natural dye alizarin was synthesized by German scientists Carl Graebe and Carl Libermann. The fourth major stage of synthetic dye was synthetic indigo, an important stage in dyestuff, where it began the modern development of the majority dyes present in the current dye industry (15).
However, dye presence within the United States did not happen until following
World War I when the German submarine Deutchland went into American ports trading military and money for dyes. Specifically, a major turning point in the presence of dyes was the transferring of dye patents to the Allies via the Alien Property Custodian.
Throughout the course of the twentieth century, it was documented that many companies, including DuPont, worked very meticulously in attempting to begin manufacturing in the
United States. In fact, it was noted that DuPont spent $43 million dollar prior to making a profit on dye synthesis. By the 1960s, 50 to 60% of all dye manufacturing produced in the United States was by four major companies (Allied Chemical, American Cyanamid
13
GAF, and Dupont), while during recent history it has been documented that there are no current standing major dye companies present in America (16).
It has been stated that whenever color is present in wastewater, it can cause problems, specifically high oxygen demand and suspended solids (17). There are many different dye wastewater types including textile wastewater because of high water presence and large quantities. One of the major components found in dye wastewater includes organic contaminants such as aromatics (18).
2.6 LEGISLATION CONCERNING DYE WASTEWATER 2.6.1 CLEAN AIR ACT (1970)
The Environmental Protection Agency has directed several federal mandates specifically designed for this particular type of pollution. While the Clean Air Act originally passed in 1970 required limitations on exposure to ambient air by industries from primary and secondary sources, one of the core pieces of the Clean Air Act was the inclusion of the National Ambient Air Quality Standard (NAAQs) to protect human health and the environment (19).
In 1990, Title III Section 112, known as the Clean Air Act Amendments were designated to control Hazardous Air Pollutants. The “National Emission of Hazardous
Air Pollutants” had listed a series of Amendments to include a list of 189 hazardous air pollutants (HAPs) that are necessary to be reduced within each industry, requiring the monitoring, assessing, reporting, and risks along with the potential planning (20). The
Amendments played a significant role in the dye and textile industry, as many of the raw materials that are present within dyes can be found on the list of hazardous air pollutants
14
(21). Specifically, the EPA created legislation for the textile processing industry. Known as Maximum Achievable Control Technology Standards (MACTS), the EPA required a control of hazardous air pollutants from textile processing plants having produced either
10 tons/yr or 25 tons/yr (22).
2.6.2 CLEAN WATER ACT (1972)
The Federal Water Pollution Control Act of 1972 (also known as the Clean Water
Act) was passed to regulate the amount of pollution that was discharged into surface waters. One key component of the Clean Water Act was the administration of the
National Pollution Discharge Elimination System (NPDES), which were put into place for the purpose of reducing effluent that was produced from point sources by means of using available technology to remove discharge from sources other than publicly owned treatment works (POTWs) or wastewater treatment plants (WWTPs). To highlight,
Section 301b was designated for proper control of waste production by the use of conventional treatment methods, such as secondary treatment (biological treatment) and the addition of any other advanced treatment from eight textile industries—wool scouring, finishing, process, woven and knit fabric finishing, and carpet mills to name a few, while Sections 304b and 306b required a presentation of discharge limits and performance matrices of these discharge limits respectively (23).
2.6.3 RCRA (1976)
The handling of solid and hazardous waste from the textile industry relies on the
Resource Conservation and Recovery Act (RCRA) of 1976 which coined the phrase of
“cradle to grave,” as it deemed responsibility over the life cycle in the production of 15 hazardous waste through its generation and production to its handling towards a final location following use. This was followed by 1984’s Hazardous and Solid Waste
Amendments (HSWA) which included legislation concerning underground storage tanks.
The requirements within these two major pieces of legislation included several requirements: for example, 40 CFR Part 262 put in place requirements for the generation of hazardous waste; 40 CFR Part 261 provided the proper terminology of hazardous and solid wastes, while 40 CFR Part 280 detailed the design of petroleum and hazardous waste underground storage tanks (10).
RCRA and HSWA certainly were significant for the textile industry as previous stated, many of the constituents found with dye and pigments are necessary for the purpose of treating and handling waste developed from this industry. In fact, this past
July the EPA has celebrated twelve years of legislation that recently highlighted the disposal of hazardous waste on land. These particular amendments were followed as a part of RCRA which highlighted three wastes as being hazardous whenever generated.
These wastes—azo, anthraquinone, triarylmethane, dyes, pigments, and Food and Drug and Cosmetic (FD&C) colorants, sludges from triaylmethane dyes/pigments, and wastewater treatment sludge of anthraquinone dyes and pigments. These three waste types have been identified damaging to human health and the environment (24,25).
Textile dye facilities are regulated under Section 313, Title III of the Superfund
Amendments and Reauthorization Act of 1986 (SARA), where it required started in
1987, a specific reporting of any chemical processing greater than 75,000 lbs, in 1988 and 1989 to the present reduce by 25,000 lbs each year to a minimum reporting requirement of 25,000 lbs of waste. This is required for all facilities that produce dyes
16 such as Disperse Yellow 3, Acid Green 3, Direct Blue 38, and dyes with copper, chromium, and cobalt-based compounds (26).
2.6.4 EPCRA (1986)
In 1986, the Environmental Protection Agency passed the Emergency Planning and Community Right-to-Know Act. This form of legislation resonates well with the textile and dye industry, specifically Section 313. This particular sanction requires manufacturing companies to publically release annual documentation on all chemical releases whether through annual activity or by accident. This was placed in conjunction with the Toxic Chemical Release Inventory rule into the Federal Registrar by February
1988. This legislation requires reporting under specific sanctions of manufacturing, using, or possess any chemicals conducting and operating with 10 or more full-time employees within a given threshold as stated in the Superfund Amendments of 1987
(SARA). The EPA provides a list of procedures for reporting and names for each chemical based on name and chemical abstract service (CAS) number (27).
2.6.5 POLLUTION PREVENTION ACT (1990)
An alternative measure in proper removal of waste would be in pollution prevention. This was stirred by the Pollution Prevention Act of 1990 which legalized a standard for controlling waste generation. The EPA has provided a valuable series of case studies that were compiled in a publication known as “Best Management Practices for
Pollution Prevention in Textile Industry.” A more recent adaptation of this publication,
“Profile of the Textiles” was developed in September of 1997 included many of the entities from this previous publication. The 1997 document lists several practices that a 17 textile manufacturer can do to reduce pollution. These have been consolidated into 11 practices (10,11):
1. Textile facilities should find methods of purchasing or receiving materials for
waste can that do not increase pollution.
2. Prescreen materials that have been purchased for various factors such as
environmental impacts, handling procedures, and emergency situations.
3. Purchase materials that come from reusable packages that can be resent to the
merchant.
4. Waste reduction can be successful by choosing chemicals that reduce the
amount of pollution and also the constituent waste.
5. Provide alternative methods outside of using chemical treatment.
6. Optimize and combine textile operation and processes.
7. Reuse dye and rinse baths.
8. Use automatic equipment that can be properly adheres to proper dye handling.
9. Use washers and ranges that consume less energy and water.
10. Have proper cleaning and housekeeping practices.
11. Provide training for workers.
2.7. DYE WASTEWATER TREATMENT METHODS
18
While the treatment used within this system is electrocoagulation-electrofloation, it is important to still consider alternative treatment methods that have been employed.
2.7.1 BIOLOGICAL TREATMENT
2.7.1.1 DESCRIPTION OF CONVENTIONAL AEROBIC TREATMENT
One of the more common methods of treatment is the use of biological treatment.
The attraction to this method is due to its capability of microorganisms of being able to mineralize organics naturally, i.e. providing a mutual benefit for both the treatment plants and also the organisms itself—waste is used as a food source, while it is converted into a form suitable for discharge with little or minimum cost. In general, the major difference in treatment processes depend on whether or not molecular organic is present as in aerobic treatment, or absent as in anaerobic. When deciphering between the two, one should consult the necessary treatment objectives simply because the two conditions differ based on sludge age and production, removal, and the production of additional compounds such as methane or other acids (28).
There are two major classifications of aerobic treatment—activated sludge and trickling filter. Activated sludge is the use of a suspended growth that provides intimate contact between microorganisms and organic constituents. The use of air, whether through natural ventilation or artificially by a mechanical device such as a blower, provides the mechanism that initiates contact between food and microorganisms. Ideally, the goal is to reduce the biochemical oxygen demand (BOD5) that is developed from the production of waste from various industries. The EPA has stated that within the textile
19 industry activated sludge can be achieved at efficiencies as high as 95%. For the purpose of developing nitrification or an increase in intimate contact, one can consider the use of extended aeration. Extended aeration is beyond the conventional 6-8 hours sludge retention time, where it can be extended up to three days. Advantages of this additional time increases the metabolism of organic compounds in the reactor where more than 75% of components can be effectively used and reduce the amount of waste generation. For effective treatment, it has been stated that if treatment has an N to BOD ratio of 3-4 lb
N/100 lb of BOD treatment and dissolved oxygen concentration is maintained around zero within aeration basins (28).
Attached growth, the antecedent to suspended growth, comes in the form of a trickling filter. Trickling filter provides media by which endorses microbial growth, where examples include crushed stone, slack, or other inorganic materials. When water enters from the top of the system, it makes contact with the media, whereby initiating growth of microorganisms and removal of organic materials. Because of the structure of the trickling filter, it is necessary that a trickling filter requires the use of recirculation or the recycling of the return of treated wastewater back into the filter for the purpose of maintaining sufficient aeration and also retains moisture of the media without compromising the loss of microorganisms. One can say that removal efficiency is integrated with the BOD loading rate. Within the textile industry, 10-90% of treatment can be achieved by a trickling filter (28).
Nevertheless, treatment made by using biological means has been criticized due to its limitation of biodegradability by microorganisms, particularly due to the xenobiotic
20 components that can be found within biological treatment processes (29). This was concluded by Zhao and Hardin which only saw a 50% removal of color (30).
However, it has been observed that the use of conventional biological methods is incapable of removing dye from wastewater due to the presence of many organic contaminants (13). This is due to the fact that organic compounds within dyes are very instable and have high resistance to organic matter decomposition (31). The chemical composition of the dye is very difficult for the bacteria to be able to degrade down which is why it is difficult for conventional biological treatment to be used for the treatment of dye wastewater (32). For example, experiments done by Sanayei et al. concluded that through a sequencing batch reactor for treatment of reactive dye Cibacron Yellow FN_2R could only achieve color removal within the range of 31 to 67% (33).
2.7.1.2 DESCRIPTION OF CONVENTIONAL ANAEROBIC TREATMENT
As stated earlier, the differences in biological treatment is contingent upon the presence or absence of molecular oxygen. With the absence of molecular oxygen, anaerobic conditions persist. Within this particular treatment condition persist, the presence of microorganisms that will use alternative sources of oxygen (such as sulfates, nitrates for example) and convert organics into organic acids and alcohols. Further conversion of these constituents would happen into methane and carbon dioxide.
Anaerobic treatment can be considered in many instances preferable over aerobic conditions due to its ability to reduce waste and produce an entity that can be used as a valuable resource (28).
21
Anaerobic lagoons are one of two major types of anaerobic conditions. An anaerobic lagoon is a depression that consists of a depth of 10 to 17 feet, a BOD loading rate between 15 and 20 lb BOD/1000 ft3, and a long sludge retention time. Wastewater typically flows from the bottom of the lagoon to ensure the proper entry and sustainability of food for anaerobes. An alternative system known as an anaerobic contact system consists of an equalizing tank, digester with equipment for mixing, gas stripping using air or vacuum, and clarifiers. Anaerobic digestion begins within the digester at moderately high temperature (95 to 100 degrees Fahrenheit) with BOD loadings ranging from 0.15-0.2 lb/ft3 for a period of 3 to 12 hours. This process follows entry into a gas stripper, settling within the clarifier, and then recycled back within the system. Anaerobic contact system is capable of achieving a 90-97% removal rate of BOD and suspended solids (SS) (28).
Anaerobic treatment processes are capable of producing higher treatment than aerobic. Color removals have been documented to being 65% (34, 35) to a maximum between 80 and 90% (36, 37,38).
The combination of anaerobic and aerobic treatment has found more success when attempting to treat dye wastewater. One of the reasons why is because anaerobic- aerobic treatment has the potential of being able to remove pollutants while producing methane for the purpose of being used for energy. Various literatures have observed more success when using this particular application than treatment using simply anaerobic and aerobic treatment by them. For example, Karatas et al. used anaerobic-aerobic treatment of Reactive Red 24, not only completely removing color, but also produce 563 mL/day of methane (39). Khehra et al. observed similar high color removals using an upflow
22 anaerobic sludge blanket (UASB) to remove 98% of color and 95% COD from C.I. Acid
Red 88 (40). Studies completed by both Panswad and Luangdilok and An et al. determined that the success of the treatment was dependent on the type of dye (41,42).
Overall, biological treatment processes can have higher treatment when applying aerobic and anaerobic conditions having provided opportunities to consider all bacterial types being employed in treatment. However, one of the biggest disadvantages of using biological treatment as a whole is in its long hydraulic retention times—approximately between 4 and 5.5 hours (43, 44) , 12 hours (45), 18 hours (46) to a days, where Cinar et al. completed treated as long as 24 hours (47), Karatas et al. employed 5.76 days for their treatment (39) and 128 days (38). In addition to long hydraulic retention times, treatment is applied at lower concentrations, usually no greater than 150 mg/L (46, 48, 49, 50), but in some raw wastewater can be higher. Nevertheless, the best method of using aerobic or anaerobic treatment is the hybrid with non-biological treatment methods—photocatalytic
(51), membrane filtration (52, 53, 54), ozone (55), and activated carbon (56).
2.7.1.3 ALTERNATIVE BIOLOGICAL TREATMENT METHODS
There are many alternative biological treatment methods which have the capability to replace the conventional methods. Sequencing batch reactor (SBR) is an alternative treatment method that been used for the purpose of treating wastewater since the early twentieth century (1914). One can describe a SBR treatment method as a fill- and-draw technique, or the process of filling the reactor and then withdraw following
23 treatment. Optimum SBR treatment can occur for flows that enter in at low or intermittent values (57).
The method of sequencing batch reactor is such that all processes of aeration, sedimentation, and clarification happen within the same reactor. One cycle involve five stages—fill, react, settle, draw, and idle. Within the fill stage, the reactor begins by applying the substrate or wastewater into the reactor until approximately 25% of the reactor volume has been achieved. Alternating presence of aeration occurs throughout this particular stage. Once the fill capacity has been achieved, the react stage pertains to the point the reactor where biological reactors happen; in order to retain aerobic conditions throughout this particular point in the cycle, dissolved oxygen concentrations must remain optimum to allow for the reactions to maintain themselves in the reactor.
The reactor spends approximately 35% of the treatment time within the react stage.
Following the react stage, the reactor shifts to the settle period where for one to two retention time solids are allowed to settle inside the reactor. The final two points inside the reactor, draw and idle, use a series of mechanical methods such as weirs to discharge clean water from the system, while idling prepares reactor transition points (58).
Some of the advantages of using sequencing batch reactors include the compartmentalization of the one container which allows for cost-savings. As stated previously, aeration, sedimentation, and clarification happen simultaneously (57). In addition, an advantage for high BOD such as dye wastewater, no return sludge, and capability to control undesired filamentous growth can allow sequencing batch reactor to be a favorable method (58). However, one must balance the potential of additional solids such as floatables to be discharged during the draw stage, increasing demands in
24 maintenance, and meticulous design specifications may make this method disadvantageous (57).
A membrane bioreactor (MBR) is described as the hybrid of a biological reactor combined with a membrane process. When considering the use of the membrane processes, the wastewater carrying the pollutant passing through the membrane. This membrane is cellulose based made up of 1 micron wide diameter pores, where the pollutants travel through the membrane for the purpose of further disposal of pollutants.
Because of this small diameter, any other additional components that cannot be filtered would require additional treatment. Treatment occurs by using a vacuum pressured system to push the wastewater across the membrane surface. The system also involves uses a backwashing technique, where air blows around the membrane for the purpose of reversing the water back through the membrane for the purpose of cleaning the membrane (59).
It has been recognized that there are two major types of configurations—hollow fiber bundles and plate membranes. Each of the two configurations involves connectivity to a manifold—the hollow fiber configuration is connected in bundles, while the plate membrane is connected within a series of plates to provide an opportunity to add several membranes together (59).
In regards to treatment, Yang et al. treated dye wastewater by the purpose of using a micro-filtration membrane that consisted of a particle-size range between 1 and
50 microns, with a range in diameter between 20 and 40 microns. The authors experienced removal of COD at 85%, TOC between 85 and 90% and a 70% color
25 removal (60). Hai et al. treated dye wastewater using Coriolous veriscolor for the purpose of using a micro-filtration membrane, where it was determined that the optimum treatment parameters were a TOC concentration of 2 g/L, dye concentration of 100 mg/L, temperature of 29 degrees Celsius, and a pH around 4.5, resulted in removal efficiencies of 97% TOC and 99% color during a 15 hour hydraulic retention time (HRT) and a rate flux of 0.021 m/d (61).
A final alternative to conventional biological treatment involves the use of fungi.
Fungi treatment is a viable treatment option due to its ability of its application to both live and dead cells (62).When fungi is live, it is advantageous because of its ability to produce enzymes such as laccases and manganese peroxidases due to the fact that they are capable of breaking down the difficult aromatic ring structures, catalyzing the reduction of the molecular structure (63). Ultimately, white rot fungi are capable of mineralizing these dye structures by means of applying towards enzymes (62). Alternatively, dead cells would be able to be used for biosoprtion, combining physico-chemical methods, including adsorption, deposition, and ion exchange (62). In order to optimally treat fungi, one must consider the following growth conditions--the particular type of medium, carbon sources, nutrients (nitrogen), oxygen, pH, incubation time, and temperature
(62).One of the drawbacks of using fungal bacteria is that activity has the disadvantage of inhibiting the degradation of the molecular structure of dyes. Also, fungal treatment may not be advantageous due to the rate of reaction which may be a slower process of treatment (63).
There have been various adaptations throughout literature of using fungal treatment. Wesenberg et al. studied the removal of dye wastewater by means of the use of
26 of Clitocybila dusenii and the laccases of Manganese peroxidase (MnP) (64). Fu and
Viraraghavan removed Congo Red by the means of NaHCO3 pretreatment along with the inclusion of Aspergillus niger, having considered the pH and kinetic and isothem studies, where the optimum pH is 6, following the Radke-Prausnitz model, and adsorption capacities were 13.80 mg/g for granular activated carbon and 16.81 mg/g for powdered activated carbon (62).
Cing et al. removed 95% of textile dye from wastewater by the use of
Phanerochaete chrysosporium during a treatment time of one day and optimum temperature of 30 degrees Celsius (65). Maximo et al. completed the use of Geotrichum sp. for the purpose of treating Reactive Black 5, Reactive Red 158 and Reactive Yellow
27. Treatment time was reduced by 3/4ths of the original 20 hour treatment time when applying this fungal species to 200 mg/L of the dyes, specifically Reactive Black 5 dye
(66). Using the strain Euc-1, Dias et al. was successful in decolorizing 11 of 14 azo dyes
(specifically completely decolorizing azo dye acid red 88) within optimum pH values of 4 and 5. Decolorization of white rot fungi Phanerochaete chrysosporium and applying a glucose concentration of
2 g/L (67).
Demir et al. was able to decolorize Remazol Red RR Gran where temperatures between
50 and 60 degrees for laccase activity (68). Shin used the white rot fungus Irpex lacteus for the purpose of decolorizing by shaking in 59% and stationary at 93% within 8 days
(69). Maximo et al. decolorized Reactive Black 5 by means of Geotrichum sp. CCMI
1019 within stirred tank reactors and two bubble columns, using porous plates and aeration tubes, where manganese peroxidases was found at high values within both
27 stirred tank reactors and aeration tube bubble columns (70). Fang et al. combined the use of microbial species combining various fungi and bacterium for the purpose of decolorizing Direct Fast
Scarlet
4BS using polyvinyl alcohol, where the optimum conditions were 5-8 in pH, temperature range between 25 and 40 degrees Celsius, and a dye concentration of 100 mg/L (71). He et al. followed by using white-rot-fungus and Pseudomonas treated Direct Fast Scarlet
4BS consented with this authors having a pH between 4 and 9, temperature range between 20 and 40 degrees C, and a dye concentration of 1000 mg/L (72). Amaral et al. used Trameters versicolor for the purpose of decolorizing textile dye by comparing looking at the application of glucose, where the decolorization was 97%. (73)
Park et al. decolorized Acid Yellow 99, Acid Blue 350, and Acid Red 114 by means of 10 various fungal strains, where the optimal fungal strain was Trameters versicolor KCTC 16781 (74). Yu and Wen removed Reactive Brillant Red K-2BP using
P. rugulosa Y-48 and Candida krusei G-1 at various concentrations. At 200 mg/L of dye concentration, 99% decolorization occured at 24 hours, and at 50 mg/L dye concentration
P. rugulosa Y-48 could be removed between 22-98% and C. krusei G-1 between 62-94%
(75).
Koseoglu and Ileri used three various fungi, P. chrysosporium, Trametes versicolor, and Pleurotus sajur-caju indicated various decolorization effciencies.
Decolorization was determined by the use of measuring at various wavelengths. For example, P. chrysosporium was decolorized between 66-77% at 436 nm, 64-79% at 525 nm, and 69-75% at 620 nm, while T. veriscolor achieved 71-84% at 436 nm, 72-85% at
28
525 nm, and 70-80% at 620 nm, and P. sajur-caju 74-80% at 436 nm, 75-81% at 525 nm, and 72-78% at 620 nm (76). Payman and Mahnaz were capable of removing textile wastewater by means of using Aspergillus niger and a fungal species from the Gorgan bay, removing 90-95% of color from dyes (77). Other dyes that have been used to decolorize dye wastewater include the use of white rot fungus Trametes versicolor (78),
Aspergillus niger for the treatment of Basic Blue 9 under various initial pH values (79), lignin peroxidase (LiP) from Phanerochaete chrysosporium was capable of decolorizing about 85% of dyes (80), and Soares et al. used commerical laccase for the purpose of treating Remazol Brilliant Blue R (RBBR) (81). Other authors studied Apergillus Niger in textile wastewater treatment (82).
2.7.2 ADVANCED OXIDATION TREATMENT
2.7.2.1 WET AIR OXIDATION
Wet air oxidation has been as a method of treating dye wastewater. In this advanced oxidation process, pure oxygen transforms constituents (pollutants) by oxidation, into carbon dioxide and water under high temperatures. The differences in the oxidation is determine based on the medium at which oxidation is completed. Most common mediums include air or oxygen, which uses pure oxygen as a medium where in this case, air has been used for the formation of oxidation products (83).
It was used to treat chemical oxygen demand and total organic carbon and have found that having the operative conditions of 150 and 290 degrees Celsius temperature range and a partial pressure of 0.375 to 2.25 MPa and a temperature of 25 degree Celsius
29 temperature for oxygen (83). Santos et al. combined the use of wet oxidation with the use of activated carbon Industrial React FE01606A for the purpose of treating Orange G,
Methylene Blue, and Brillant Green. Having prepared a solution of 1000 mg/L, using an oxygen flow rate of 90 mg/L, temperature of 160 degrees Celsius, and 16 bar, a three phased fix-bed reactor was able to reduce a value between 40 and 60% (84). Ma et al. used catalytic wet oxidation at 35 degrees Celsius and atmospheric pressure, with the purpose of using CuOMoO3-P2O5catalyst for the purpose of treating methylene blue, reaching a color removal of 99.26% within 10 minutes having an initial concentration and pH of 0.3 g/L and 5 respectively (85). Liu et al. developed a catalytic wet oxidation technique using Fe2O3-CeO2-TiIO2/gamma-Al2CO3 catalyst and capable of achieving a
COD removal of 62.23%, 50.12% color removal, and 41.26% TOC removal within 3 hours, increasing the BOD:COD ratio from 0.19 to 0.30 (86).
2.7.2.2 FENTON/PHOTO-FENTON
The Fenton process is the chemical interaction of ferrous iron Fe(II) with hydrogen peroxide (H2O2) forming a reduced ferric (III) ion, hydrogen ion (OK), and a hydroxide radical (*OH). The available hydroxide radical is capable of forming an additional Fe(III) ion when combined with available ferrous iron (II) (87). Equations (1-
2) summarizes this dark reaction:
Fe(II) + H2O2 → Fe(III) + OK + *OH (1) (87)
*OH + Fe(II) → Fe(III) + OK (2) (87)
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With the irradiation or the application of near UV-radiation and/or visible light develops the photo-Fenton reaction. The use of light emission greatly improves the removal of organics specifically enhancing the process of mineralization. There are three different type of reactions—the applications of photo-reduction of ferrihydroxalate
(Fe(III) OH2-) into ferric ions and hydroxide complex. As seen in equation 2, the availability of hydroxide combines with additional ferrous ion is important. This presence is increased within photo-Fenton as compared with the dark Fenton process. The second reaction type is ferric carboxylate complexes are irradiated to Fe(II), carbon monoxide, and radicals. Finally, photolysis of hydrogen peroxide (H2O2) combines light energy with hydrogen peroxide to form a hydroxide complex. Wastewater absorbance occurs at wavelength less than 300 nm (87). Equations 3-5 summarize these photo-Fenton processes:
Fe(lll)(OH)+2 + hv → Fe(ll) + *OH (3) (87)
+2 Fe(lll)(RCO2) + hv → Fe(ll) + CO +R* (4) (87)
H2O2 + light energy (hv) → 2*OH (5) (87)
It has been stated the Fenton processes are advantageous when completing oxidation at a lower cost and accessibility towards the constituents needed for the conducting of the process (88). In addition, the ultimate formation when using Fenton’s reagent develops lower molecular weight components and mineralized products, CO2 and
H2O (89).
High efficiency of treatment has been observed when using Fenton operated at a pH value between 2 and 5. Evidence of this has been seen throughout literature. For
31 example, Kuo et al. experimented with using Fenton's reagent when the pH value is less than 3.5 (90). Rathi et al. treated direct yellow 12 (DY12) using UV/H2O2/Fe+2 at a pH of 4 (91). Chen et al. treated bromopyeogallol red (BFR) used cobalt ions within the
Fenton at a pH of 5.2 (92). Hsieh et al. operated at an initial pH of 2 using a combined ultrasound/Fenton/nanoscale iron oxidation process (93).
Second, Fenton reaction requires an adjustment of the molar ratio to generate the
+2 desired treatment. Kos et al. reported that the ideal molar ratio is 1:1 for H2O2 and Fe respectively (88). However, it was found that contingent on the type of wastewater being treated, molar ratios may vary. Jonstrup et al. found optimum conditions when the molar
+2 ratio was 3:0.25 H2O2 and Fe respectively for the use of photo-Fenton to the treatment of Remazol Red RR (94). Su et al. concluded that removal was obtained when the ratio
+2 was 0.95:3.17 for Fe : H2O2 (95). Koprivanac and Vujevic had a Fe3+/H2O2 1:5 molar ratio when treating C.I. Direct Orange 39 (96). Acarbabacan et al. had a molar ratio of
+2 1:5 for Fe /H2O2 for the purpose of treating azo dyes (97).
Third, Fenton does not always use Fenton as a metal complex. Instead of using ferrous metal, Lim et al. used the combination of H2O2/pyridine/Cu(II) system by means of treating Terasil Red Dye (98). Dantas et al. developed a complex combining Fe2O3 and carbon (99).
Fourth, Fenton treatment processes have been recently combined with other treatment processes to create a high removal of wastewater components such as TOC and color. Generally, treatment is much higher when one applies a supplemental process with
Fenton. Wang et al. used electro-Fenton and activated carbon fiber cathode for the
32 purpose of the mineralization of Acid Red 14, removing 70% total organic carbon (100).
Kusic et al. applied a series of processes involving a two-stage Fenton process treating
C.I. Acid Orange 7, where 99.09% of TOC was removed (101). Chen et al. treated reactive brilliant orange using photo-Fenton and iron pillared vermiculite (Fe-VT) catalyst X-GN, decolorizing at 98.7% (102). Fongsatitkul et al. conducted a three-stage treatment by means of biological, chemical, and asecondary biological treatment method intricately fitting Fenton within the treatment process for a textile wastewater (103).
Lucas and Peres produced high removal of Reactive Black 5 using Fenton/UV-C and ferrioxalate/H2O2/solar light, where it was found that the treatment was 98.1% and
93.2% (104). Gonder and Balas combined Fenton with ion exchange (105).
2.7.2.3 OTHER ADVANCED OXIDATION METHODS
Hydrogen peroxide/pyridine/copper(II) [H2O2/pyridine/Cu(II)] is an advanced oxidation method that when that uses copper ions that are react both with a chealting agent (pryidine) for the purpose of forming anions and hydroxyl radicals. The copper ions are reduced to Cu(I) through this application. This treatment method is capable of being used for the purpose of decolorizing dye wastewater. The treatment method chemistry begins with the use of copper(II) which through the chelation of pyridine transforms in develops a pyridine copper (II) complex.
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Next, the reaction of hydrogen peroxide develops the formation of cuporus complex
(copper I) and the emission of hydrogen. The cuporus complex is then reacted with excessive hydrogen peroxide and will break-up the cuprous complex, forming copper (I), superoxide anions, and hydroxyl radicals. Through this oxidation process, dyes will be decolorized (105).
The biggest key is to transform the materials into harmless substances.
H2O2/pyridine/copper (II) system has been used by several authors in decolorization of wastewater where Lim et al. was capable of removing COD up to 73% when the application of H2O2 reached 0.059 M, pyridine at 0.087 M, and Cu(II) 0.017 (106). Bali et al. was capable of decolorizing 90% the majority of the dyes with the experiment, with the exception of Rifacion Yello He-4R and Levafix Yellow Brown E-3RL, only achieving
74% and 69% respectively (107). Nerud et al. used this system and was capable of decolorizing 89% phenol red, 58% tropaeolin 00, 95% Evans blue, 84% eosin yellows, and 92% Poly B-411 within
1 hour of complete treatment, maintaining the pH between 3 and 9 (108).
2.7.3 ACTIVATED CARBON
One of the more common methods of treatment is the use of activated carbon, where it has been observed that activated carbon can be formed by taken a given carbon source through a method such as pyrolysis and then conduct an activation process by means of oxidation. Pyrolysis plays an important role within the formation of activated
34 carbon as it is completed within a high temperature range (400 and 1000 degrees Celsius) without the presence of air (109).
A very important feature within activated carbon is the discussion of equilibrium isotherms. Equilibrium isotherms are used to identify the rate when the material remains within the pores of the activated carbon when reaching equilibrium. There are several different isotherms, where the more common are Langmuir, Brunauer-Emett-Teller
(BET), and Fruendlich. The following equations summarize the three isotherm equilibrium equations:
Langmuir describes conditions where molecules from the solute can only be adsorbed on a monolayer surface (109).
qe = QbCe/(1+ bCe) (6) (109)
where
qe is the ratio of solute that has been absorbed per gram of activated carbon
Q is the mass of solute adsorbed per gram of activated carbon within the monolayer
Ce is the solute concentration within the solution
b is a constant of net enthalpy of adsorption
BET extends the Langmuir towards various layers where the Langmuir isotherm applies to each individual layer, while Freundlich isotherm application is applied to a heterogeneous surface:
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(1/n) qe = KfCe (7) (109)
where
Kf is a sorption capacity constant
1/n describes adsorption favorability, where <1 is unfavorable, greater than 1 is
favorable, and 1 is linear.
One of the more common adsorbents is granular activated carbon (GAC) in the hope of further reducing heavy metal concentrations. In particular, activated carbon follows the use of adsorption, or the process of retaining particulates within the pore sizes of a given material. There are various types of granular activated carbons that are used for the purpose of applying towards wastewater. Fresh and spent carbon sources consist of the material that will compose of an activated carbon treatment process where wastewater is applied vertically by either the use of pressure or gravity applied into a fix-bed column which houses the carbon material. The carbon itself is contained at the bottom. When wastewater is applied to the reactor, organic constituents affix themselves to the walls of the pores, where the wastewater will remain within the column until the concentration reaches the water quality standards. Following such treatment, carbon is then removed and then oxidized for the purpose of removing the carbon present within the pores. An alternative to using thermal treatment is the process of applying backwash for the purpose of treating the wastewater (110).
There have been different types of application when it comes to treatment of dye wastewater by activated carbon. Literature within the last three years has suggested that either the adsorbent type has changed or the conjunction of activated carbon with another 36 treatment method has become integral in successfully using activated carbon for the purpose of treating dye wastewater.
For example, Kalathil et al. was successful in using a two treatment method— granular activated carbon microbial fuel cell (GACB-MFC) combining Nafion membrane and platinum catalyst for the purpose of treating COD, color, and toxicity. It was determined that color was recorded at 73% at the anode and 77% at the cathode, 71%
COD at the anode and 76% at the cathode, and that toxicity was reduced within the first twenty-four hours of the experiment (111). Dong et al. was capable of completely rejecting orange G dye wastewater using the combination of ultrafiltration (UF) along with the use of powdered activated carbon (PAC), where treatment increased by 2.29% times higher using UF and PAC as compared with using UF (112). Zhao et al. found success in removing Acid Orange 7 when using a treatment system that combined the use of an electrode reactor, developing an anode consisting of activated carbon fiber
(ACF)/Fe, and an ACF/Ti cathode (113), while Li et al. used Fe-doped TiO2 combined with activated carbon has also been used for the purpose of treating dye wastewater
(114).
The conventional carbon compounds have been replaced with other forms of activated carbon. Derris leaf powder was used to adsorb 93% of the dye Grey BL when the initial concentration was 25 mg/L and temperature was 300 K (115). Others have been successful using the combination of red mud (RM) along with magnesium chloride to treat dyed as well. In fact, it was considered as being as a reliable system as one combining powdered activated carbon and sodium hydroxide (116). Bentonite was proven as a successful adsorbent when attempting to treat Acid Red 18 and Acid Yellow
37
23 (117). However, one of the most unique adsorbents used was P. oceanica, a plant within the Mediterreanan Sea was capable of adsorbing CI Acid Yellow 59 within 600 minutes at concentrations as high as 100 mg/L, having strong retention as powdered activated carbon (PAC) (118).
Waste from other productions such as fly ash has been a material that has been significant in treatment processes. Methylene blue was removed using municipal solid waste incineration (MSWI) fly ash as an adsorbent to reduce color, total organic carbon, and follow Langmuir isotherm modeling techniques (119). Biomass fly ash was used to treat Reactive Black 5, where it was found that optimum pH must range between 8.2 and
10.4, while Langmuir best described the treatment process (120). Also, Li et al. identified optimum carbonization temperature and time of 300 degrees and 60 min, activation temperature and time of 850 degrees Celsius and 40 minutes respectively was capable of completely removing methylene blue (MB) by means of sludge-based activated carbon
(SAC) (121). Wood-shaven bottom ash combined with either water (H2O) or sulfuric acid
(H2SO4) reported half the adsorption capacity of activated carbon when treating synthetic
Red Reactive 141 wastewater (122).
2.7.4 MEMBRANE FILTRATION
Membrane Filtration (UF, MF, NF, RO) is the process of using various techniques to filter desired constituents. There are two major types of divisions within the category of membrane filtration—sieve filtration and pressure filtration. Sieve filtration can be described as the limitation of the average pore size that is capable of removing objects
38 that range from as small as suspended solids to as large as other higher molecular weight organic matter such as viruses and other pathogens. The key indicator within the filtration process is the diameter of the pore size for which materials can be sieved from the particular media, which in this case would be wastewater (123).
Ultrafiltration is one of three major types of membrane filtration processes. The filtration system consists of the solvent flux which is determined by using the quotient of the volume and the unit area multiplied by the unit time and solute rejection, or the difference of 1 and the ratio of the downstream and upstream concentrations (permeate and feed respectively) of the particular entity attempting to be removed. For ultrafiltration, it can be stated that the range of pore size is between 0.1 and 0.2 micrometers, while in microfiltration, the average range is 0.01 and 0.05 micrometers
(124).
On the contrary, reverse osmosis and nanofiltration or pressure filtration, are processes that heavily rely on the use of applying pressure to reverse the natural course of constituents within a semi-permeable membrane. In a traditional osmotic situation, constituents gravitate towards traveling from high concentration to low concentration.
The application of a pressure (osmotic pressure), allows for the reverse effect. By doing so, the process of materials that naturally desire to gravitate to lower concentration to maintain homeostasis is reverted. For nanofiltration and reverse osmosis, the pressures required to achieve this state varies based on the concentration and type of constituent attempting to be removed (123).
39
Reverse osmosis and nanofiltration membrane are that are capable of being made from either cellulose acetate or polyamide material. The use of cellulose acetate limits the range (specifically the pH) at which the processes can be performed due to the biodegradability of such materials; however, the use of polyamides would allow for a more suited range of pH values that do not have the limits at what type of treatment can be performed (123).
Some of the more considerable equations necessary include the definition of system flux and recovery which will be shown within the following equations 8 and 9:
J = Qp/Am [8] (123)
Where
J = flux (gfd)
Qp = filtrate flow (gpd)
2 Am = membrane area (ft )
R =Qp/Qf [9] (123)
R = recovery of the membrane unit (percent)
Qp = filtrate flow (gpd)
Qf = feed flow (gpd)
Within membrane technologies there has been some activity in regards to the success of treatment. When it came to removing color from the wastewater, it was
40 observed that color removal was greater than 90% (125, 126, 127, 128). When it came to removal of COD, it was observed that membrane technologies were capable of ranging removal between 70 and 80% (125, 60).
2.7.5 COAGULATION-FLOCCULATION
Antithetical to electrocoagulation is the use of coagulation-flocculation. This method involves the use of various coagulants, traditionally alum (aluminum sulfate), ferric chloride (FeCl3), or ferrous sulfate (Fe2SO4) which can be very expensive depending on the volume of water treated. When applying the coagulant, the coagulant, neutralizing the charge of the particulates, thereby allow them to agglomerate and settle at the bottom of the tank. Chemical coagulation/flocculation is concerned with the pH, mixing and time (129).
Within recent studies, it is very difficult to find conventional use of chemical coagulation/flocculation techniques as many have resorted towards the use of combining treatment methods for better enhanced treatment (130, 131, 132). One of the possible problems with using chemical coagulation/flocculation alone is the difficulty of being able to reduce solubility enough for components to be able to form flocculants to be removed from the wastewater (129).
Coagulation/flocculation has been combined with almost every type of treatment method current available to treat wastewater. Harrelkas et al. was capable of combining with activated carbon, specifically powdered activated carbon to drive an 80% color removal (133), Hassani et al. applied granular activated carbon (134), electrocoagulation
(135), a natural coagulant such as chitosan drove 99% color removal (136), where pretreatment application of ozone reduced turbidity by 95% (137). 41
2.7.8 OZONE
Ozonation is a process that is formed by the dissociation of one diatomic oxygen molecule forming two oxygen atoms, where one combines a fully-intact diatomic oxygen molecule to form ozone. While ozone occurs by means of sunlight passing through air to cause such a disruption forming this molecule, this process can be easily replicated within a laboratory environment by either applying an electric current of low or high frequency or passing high energy radiation through either air or oxygen. One of the biggest issues with the formation of ozone is that its formation has high energy costs. It has been estimated that approximately 90% of all ozone production is used for electrical energy, while less than 10% that remains can be considered a consumable product (138). The following two equations show the formation of ozone by the dissociation of a diatomic oxygen molecule:
O2 ↔ 2O [10] (138)
2O + 2O2 ↔ O3 [11] (138)
One must consider various parameters when determining the amount of output ozone production for consumption desired--the electrical energy needed to run the generator, the influent gas rate, power. For example, increasing the influent gas is proportionate to generation of ozone generation and use of ozone for electrical energy, while developing a disproportion of gas that is produced for consumption within the effluent. When using ozone for treatment, one must consider the ozone contactor. There
42 are four different types of contactors--spray towers, packed columns, plate columns, spargers, and agitators or mixers. Each of these five has their various advantages and disadvantages depending on the type of constituents treated (138).
Treatment of using ozone membrane contactors for the purpose of treating dye wastewater is advantageous because of its capabilities to decrease color, chemical oxygen demand (COD), and overall increase biodegradation (139). There have been many applications of the use of ozone, specifically in the measurement and formation of mass transfer, as it is very significant in determining optimum treatment parameters when attempting to remove various components of wastewater. The reason for analyzing mass transfer for this particular treatment method is because one must consider that the application of the concentration of ozone dissolved within the aqueous solution differs between the application made at the equilibrium point of contact between gas and liquid.
Some of the components that must be thought of include pH, type of dye, ozone dosage, dye concentration, and temperature to name a few will ultimately affect how effective the treatment is (140).
Ozone is a very popular method of treatment for dye wastewater. Consider the analysis of mass transfer has been made within the treatment of dye such as C.I. Reactive
Red 120 (141), Direct Red 23, Acid Blue 113, and C.I. Reactive Red 120 (142), Acid
Yellow 17 (138), Reactive Blue 19 (143). However, while using ozone has been used solely for the purpose of treating dye wastewater, nevertheless, the more common use of ozone is typically in conjunction with another treatment method. Treatment methods such as ultrasound (144), catalytic/wet catalytic (145, 85), UV/H2O2 (146, 91), coagulation
43
(147, 148), biological application (149, 150), and electrochemical processes (151) have been observed by means of using this treatment technology.
The treatment of dye wastewater has been very successful when using ozone.
Treatment efficiencies have range depending on the optimum parameters. For example, one can treat dye wastewater at 10 minutes with a COD removal ranged between 56-
59.8% and 87.5-91.4% for color (152); complete color removal was accomplished at 90 minute (153) and 25 minutes (154), 80% color removal (155), 60% COD (156) for the purpose of using ozonation. When using combination of treatment methods, 20% COD removal was combined with an aerated biological filter (157); precipitation using bicarbonate, hydrogen peroxide, and powdered activated carbon achieved 99% color
(158), while color ranged between 50-60% but only 60% COD when combining the process with coagulation (159). Therefore as seen from the text, literature has found ozonation to remove a higher percentage of color, but low percentages of COD, where looking at a surface level of observation, COD maxed out at around 60%.
2.8. CHAPTER CONCLUSION
This chapter is a microcosm of the amount of treatment technologies that certainly are available and have been used for the purpose of treating dye wastewater. There are many additional treatment options that have not been discussed as it would encompass an entire book, where one could potentially write one chapter on each treatment process in itself.
However, the study and research of literature sources have made it apparent that all dye wastewater treatment processes have both advantages and disadvantages individually.
With that having been said, research has now been swayed to combine treatment
44 processes together for the purpose of being able to combine the strengths of each treatment process to counterbalance the weaknesses that each possess when used alone.
When considering what is available, one cannot merely suggest one treatment process over another, as it would depend on the various parameters that one desires to remove.
Nevertheless, the opportunity to observe various treatment processes has led to potential success in deviating from some of the specific treatment methods available and venturing towards others. Future research and publications will be able to be the indicator of whether dye wastewater treatment will advance on a much higher level than what is currently available at this time.
45
CHAPTER 3
ELECTROCOAGULATION LITERATURE REVIEW
With a few additions, this chapter has been taken from its entirety from a literature review published in 2011 in the journal Water by Dr. Hung, Dr. Mohammad Suleiman Al
Ahmad, and myself titled: "Electrocoagulation in Wastewater Treatment" (160).
3.1. DEFINITION OF ELECTROCOAGULATION-FLOTATION (ECF) Electrocoagulation-electroflotation (ECF) technology is a treatment process of applying electrical current to treat and flocculate contaminants without having to add coagulations. Shammas et al. stated that coagulation occurs with the current being applied, capable of removing small particles since direct current applied, setting them into motion. Also electrocoagulation could reduce residue for waste production (161).
Electrocoagulation consists of pairs of metal sheets called electrodes, that are arranged in pairs of two—anodes and cathodes. Using the principles of electrochemistry, the
46 cathode is oxidized (loses electrons), while the water is reduced (gains electrons), thereby making the wastewater better treated. When the cathode electrode makes contact with the wastewater, the metal is emitted into the apparatus. When this happens, the particulates are neutralized by the formation of hydroxide complexes for the purpose of forming agglomerates. These agglomerates begin to form at the bottom of the tank and can be siphen out through filtration. However, when one considers an electrocoagulation- flotation apparatus, the particulates would instead float to the top of the tank by means of formed hydrogen bubbles that are created from the anode. The floated particulates can be skimmed from the top of the tank. The following are chemical equations that describe this process, using aluminum (Al) as the metal:
Cathode:
Al+3 → Al + 3e- [12] (162) +3 - Alkaline (pH~5-8), Al + 3OH → Al(OH)3 [13] (162) +3 + Acids, Al + 3H2O → Al(OH)3 + 3H [14] (162)
Anode:
- - 2H2O + 2e = 2OH + H2 [15] (162)
To consider how effective the ECF reactor can be, one must consider the following inputs or variables—wastewater type, pH, current density, type of metal electrodes
(aluminum, steel, iron), number of electrodes, size of electrodes, and configuration of metals. These variables would affect the overall treatment time, kinetics, and also the removal efficiency measured.
47
The pH is very important in regards to the how efficient the current flows through the system. This is because of the fact that this determines the presence of hydroxide compounds that are formed inside of the wastewater having made contact using the electrodes. Depending on the conditions of pH will determine what will form within the water. Whenever the pH is less than 5, aluminum hydroxide (Al(OH)3) species will form, as compared to a pH greater than 5, aluminum cations form (Al+3). In addition, when dye species are acidic, the pH increases, while when treating alkaline species the pH decreases. In addition, the presence of salt as the electrolyte also undergoes various chemical reactions as well as seen from equations 15-18:
- - 2Cl + 2e → Cl2 [16] (162, 163)
- + Cl2 + H2O → HOCl + Cl + H [17] (162, 163)
HOCl → OCl- + H+ [18] (162, 163)
Electrocoagulation-flotation is an alternative method to classic chemical coagulation for many reasons. ECF is capable of reducing the need for chemicals due to the fact that the electrodes provide the coagulant. However, many individuals still use chemical coagulants to attempt to enhance treatment. Traditionally, chemical coagulation involves the use of alum (aluminum sulfate), ferric chloride (FeCl3), or ferrous sulfate (Fe2SO4) which can be very expensive depending on the volume of water treated. When applying the coagulant, the coagulant performs a similar function as the electrodes, neutralizing the charge of the particulates, thereby allow them to agglomerate and settle at the bottom of the tank. In addition, electrocoagulation-flotation is capable of reducing waste production from wastewater treatment and also reduces the time necessary for treatment.
48
3.2. OPTIMIZATION
By considering the Box-Behnken design of surface response analysis for color removal within distillery spent wash, Krisna Prasad et al. found that 95% color removal was obtained with a current density of 31 mA/cm2, dilution of 17.5%, and 4 hour electrolysis design. At optimum conditions, the treatment efficiency was at 93.5% (164).
Chavalparit and Ongwandee concluded that removal of 55.43% COD, 98.4% oil and grease, and 96.59% suspended solids was obtained using a pH of 6.06, applied voltage of
18.2 V, and reaction time of 23.5 minutes when using the Box-Behnken design for biodiesel wastewater (165). Kaparal et al. was able to determine the dye removal by the
Taguchi method, by using an initial dye concentration of 100 mg/L, pH of 3, current
2 density of 0.5 mA/cm , CaCl2 concentration of 2.5 mM for the treatment of Bompalex
Red CR-L dye. Experimental design involved an orthogonal array using 5 simultaneous parameters (166). Tchamango et al. used electrocoagulation for artificial wastewater with milk powder to simulate dairy effluents, COD was reduced by 61%, phosphorus by 89%, nitrogen 81%, and 100% turbidity. In addition with low conductivity and neutral pH, treated water would be possible reused, as reagent required was lowered for the aluminum anode for treatment (167). Körbahti and Tanyolaç concluded that 100% pollution load, 61.6% COD, 99.6% color removal, and 66.4% turbidity were accomplished by an electrochemical reactor, where optimum conditions for conducting the experiment were at temperature of 30 degrees Celsius, 25 g/L electrolyte concentration,
8 V electrical potential, with a 35.5 mA/cm2 current density. This was accomplished to treat simulated textile dye wastewater with NaCl electrolyte based on response surface methodology (168).
49
Hammami et al. concluded that electrochemical oxidation of chromium(III) from chromium(VI) was accomplished with titanium-platinum anodes for the purpose of treating tanning bath effluent. The Doehlert design optimized Cl ions, temperature in degrees Celsius, pH, intensity of current, time of electrolysis. From the results, the authors observed that current intensity, COD (chemical oxygen demand), TOC (total organic carbon), and electrochemical oxidation were major parameters (169). Olmez studied hexavalent chromium removal with stainless steel electrodes with electrocoagulation by response surface methodology and concluded that complete treatment could be accomplished by the elctrocoagulator with 7.4 A current and 33.6 mM electrolyte concentration (NaCl), a 70 minute application time and FeSO4 × 7H2O as a coagulant. The authors considered the use of a Central Composite Design for the optimization (170). Arslen-Alton et al. concluded that the central composite design was used to optimize CI Acid Blue 193 treatment by electrocoagulation. The central composite design is capable of achieving maximum color, COD, TOC, by manipulating the COD, pH, electrical current density, and treatment time by means of a response surface quadratic model (171). Cora and Hung were able to remove metallic ions between
90 and 99% after 30 minutes of treatment using an electrocoagulation/electrofiltration with a pH of 9.5 and cadmium chloride for the metallic ions (172). Aleboyeh et al. concluded that Acid Red 14 had a 91% removal rate when current density reach 102
A/m2, electrolysis time of 4.47 minutes, and a pH of 7.27. This treatment was obtained within an electrocoagulation batch reaction under a 23 full factorial central composite face center design, where a second-order regression model was used (173).
50
Zodi et al. derived a statistic analysis using a Box-Behken design for surface response analysis using electrochemical sedimentation. Having considered current density, pH, and electrolysis design, the authors were capable of studying the effects of COD, turbidity,
TS removal, and sludge settling with aluminum electrodes (174). Vasudevan et al. considered using mild steel as anode and cathode, removing 98.6% arsenate at a current density of 0.2 A/dm2, and a pH of 7. Kinetics determined that the removal was within 15 minutes, following a second order rate absorption. Finally, Langmuir adsorption isotherm describes appropriately this condition (175).
3.3. MODELING 3.3.1 KINETICS
Balasubramanian et al. modeled adsorption isotherm kinetics for arsenic removal from aqueous solutions by means of electrocoagulation through response surface methodology
(176). Thakur et al. concluded that COD and color removal of 61.6% and 98.4%, respectively, were capable of treating bio-digester effluent within an electrocoagulator.
This was a result of a bio-digester plant followed by two-stage aerobic treatment. When considering a second-order regression model for this phenomena—pH, current density, inter-electrode distance, and electrolysis time as parameters, the model concluded an r2 value of 0.9144 for COD and 0.7650 for color (177). By producing a mathematical model, Canizares et al. determined that electrocoagulation can treat kaolin suspension by determining the total aluminum concentration and pH, along with reactivity and pollutant concentration. The authors noted the neutralization of kaolin particles and enmeshing particles with precipitation. The results of the model could produce an r2 value of 0.92
(178). Canizares et al. determined that a model comprised with Ericohrome Black T and
51 oil/water emulsion where the primary mechanism operated were aluminum hydroxide precipitate and charge neutralization of Eriochrome Black T, while one drop of aluminum precipitate was applied for oil/water emulsion removal. When applying this model, 96% of Erichrome Black T was removed, while 92% oil/water was emulsified (179).
Zaroual et al. concluded that 91% removal efficiency was capable for treating chromium (III) with aluminum anodes for electrocoagulation. Additionally, a mathematical model was established using central composite design, using a pH of 4.23, electrical potential of 9.14 V, 10 minute reaction time, and 27.5 °C temperature.
Treatment efficiency of 91% could be completed with an energy consumption of 3.536 kWh/m3 (180). Arslan-Alaton et al. was able to model treatment of Acid Blue 193 by
Central Composite Design. According to the model, COD, TOC, and color removal were chosen. Removal efficiency of 96% color, 82% COD, and 51% TOC was established for
2+ Fe concentration is 3 mM, H2O2 concentration is 25 mM, a reaction time of 10 minutes, pH of 3, and a COD of 245 mg/L were obtained for Fenton treatment, compared to 50
A/m2, with a reaction time of 15 minute, pH of 7, and initial COD of 245 mg/L (181).
Saravanan et al. concluded that by using Acid Blue 113 with electrocoagulation was capable of removing 91% COD under 3 A/dm2 of current density, pH of 6.5, and 2 g/L electrolytes concentration. The authors determined that this relationship resembled a pseudo-first order kinetic model (182).
Gadd et al. concluded that treatment efficiency was related to the electrode area, along with coagulant and bubbles, functions of electrode area, current density, and efficiency.
This operation was completed using a vertical plate electrocoagulation treating molasses process wastewater (183). Rodrigo et al. developed model for wastewater pollution
52 considering hydrodynamic conditions using chemical reaction of reagents and pollutants, where a multivariable modeling of anodes was described. The model combined a macroscopic/maximum gradient approach for all processes with pseudo equilibrium
(184).
3.3.2 COMPUTER MODELING AND STATISTICS
Aber et al. concluded that an artificial neural network was capable of producing an r2 value of 0.976 as compared to experimental data for treating synthetic and real electrocoagulation. Parameters included 30 minutes electrolysis time, pH between 5 to 8, the use of NaCl for better Cr(VI) removal (17.1 mg/L), and the use of iron electrodes as compared to Al (95% vs 15% efficiency) (185). Bhatti et al. determined that performance for treatment of Cr (VI) from 100 mg/L using Al-Al electrodes, would notice a 100 cm2 surface area, and 15 mm electrode distance. Electrocoagulation could reduce Cr (VI) by
90.4% at pH 5, 24 V electrical potential, 24 minute hydrolysis time, and 13.7 kWh/m3 electrical energy. The results for optimization were compared using coefficients of determination, where 0.8873 was produced with voltage x time and 0.9270 for amperage x time, while energy consumption was related to voltage x time (0.89490) and amperage x time is 0.9400 (186). Salari et al. considered the process of peroxi-coagulation for the purpose of decolorization of C.I. Basic Yellow 2, when using a sulfate electrolyte media at 3.0 and a gas-diffusion electrode (GDE) as cathode. According to the results, 90% decolorization occurred within 30 minutes, while the artificial neural network (ANN) was proven to show how decolorization could be efficient under various constraints (187).
Zarei et al. considered the treatment of four dyes within an aqueous solution—C.I. Basic
Blue 3, Malachite green, C.I. Basic red 46, and C.I. Basic Yellow 2 at pH 3 using a 53 carbon nanotube polytetrafluoroethleyene (CNT-PTFE) as cathode. From the experiment,
90% decolorization was determined within 10 minutes through modeling using the artificial neural network model. From the model, an r2 value of 0.989 was produced for decolorization. Also, TOC for C.I. Basic Yellow was removed at 92% and mixed dyes at
93% within 6 hours. Compared with the real wastewater, 95% removal of Basic Yellow 2 and mixed dyes had a 90% removed within 40 minutes (188).
Hu et al. concluded that current density, initial pH, electrolyte species, initial concentration of dye produce the effects upon the removal of C.I. Reactive Red 241 by electrocoagulation in regards to decolorization. From the results, it was determined that dye removal was a first order reaction, where the COD removal could be determined using the artificial neural network (ANN) and response surface method (RSM) models
(189). Cai and White prepared a model for simulation of reducing Cr(VI) by ferrous iron anode through electrocoagulation using electrochemical and homogenous reactions. The parameters considered including material feed velocity, support electrolyte concentration, and cell potential on Cr(VI) conversion (190). Zhu et al. concluded that a homemade reactor known as coagulation-electrocoagulagtion technology for thrice oilfield sewage where removal is 69.3% COD with pH is 7, aluminum sulfate dosage is 300 mg/L, rotational speed at 500 r/min for 30 minutes, current density of 12.5 A/m2, and temperature 40 Delta DGC (191).
3.4. DECOLORIZATION
Kuleyin and Balcioglu concluded that by removing crystal violet by electrocoagulation under various pH values, 99% color was removed at pH within 10
54 minutes electrolysis time. When current density was increased from 5.8 to mA/cm2, there was a 40% increase in removal. Also, color was removed with an efficiency of 95% for an initial concentration of 90 mg/L, while 55% removal was observed when the concentration was 570 mg/L (192). Zhang et al. determined that 97% color removal was obtained after 10 minutes electrolysis time, with an electrical potential of 20 V, current of
0.4 A, electrode distance of 2.5 cm, concentration of 500 mg/L, KCl concentration of 0.5 g/L , and a pH of 3.0 for the purpose of treating methyl orange simulate dye wastewater by electrocoagulation. The authors were able to construct a model in which coagulation was determined for CODCr removal followed by oxidation (193). Kabdaşlı et al. noticed that color abatement can use stainless steel electrodes for electrocoagulation of reactive dyebath effluent. The most effective treatment was using Na2CO3 for color and COD removals, while NaCl concentration solved the problems when using Na2CO3 by better enhancing color and COD removal efficiencies when pH was above 11 for coagulation and adsorption (194). Jain et al. concluded that azo reactive dye, a component from color paper, plastic, food, and pharmaceutical products are difficult to treat with conventional treatment methods due to water solubility and polar compounds (195). Yang determined that decolorization of reactive dye was high affected by current density, pH, temperature, dye concentration, and NaCl was optimal at 88%, pH between 4 and 9, and NaCl was the major factor within decolorization (196).
Ghosh et al. observed a 99.75% crystal violet removal by electrocoagulation when initial treatment concentration was 100 mg/L, current density 1,112.5 A/m2, solution conductivity of 1.61 S/m, pH of 8.5, and 1 hour of electrolysis time. It was also noticed that the cost for optimum treatment was 0.2141 US$/m3 (197). Sengil and Ozacar
55 removed 98% of color at dye concentration of 100 mg/L, pH of 5, current density of
45.75 mA/cm2, salt concentration of 3,000 mg/L, temperature of 20 degrees Celsius, and inter electrode distance of 2.5 cm. Also, results showed an energy consumption of 4.96 kWh/kg dye using a first-order equation (198). Chen et al. completed a laboratory scale experiment considering dyestuff by pulse electrocoagulation, having considering the parameters of pulse duty factor, frequency, current density, and electrolysis time. It was determined that energy and electrode consumption was improved over direct current
(DC) electrocoagulation (199). Bellebia et al. discovered that Marine Blue Erionye MR dye and Brilliant Blue Levafix E-BRA (reactive dye) could be removed successfully
3 under 7.46 and 1.49 F/m loading and abatement at a concentration of 200 mg/L. Reactive dye Brilliant Blue Levafix E-BRA was completely removed during adsorption of 700 mg/L granular activated carbon (GAC) (200). Ahlawat et al. determined that cotton blue acid dye by means of electrocoagulation using aluminum electrodes could be removed at a 97% efficiency, provided pH was 6, electrolysis time of 15 minutes, and an initial concentration of 100 mg/L, and applied voltage of 11. In addition, the authors determined that electrocoagulation was capable of degrading sludge suitable for disposal (201). Liu et al. determined by using electrocoagulation, Eriochrome Black T simulated dye wastewater was degraded considering the following parameters—space of plates, electrolysis time, electrolysis concentration, current density, and pH. Optimum conditions are plates at 2.5 cm, NaCl 1.0 g/L, density of 5 mA/cm2, and pH 5.5. It was observed that
98% decoloration was determine, where the energy consumption was 2.76 kWh/kg (202).
Murthy and Raina found that navy blue-3G by means of electrocoagulation considered the following parameters—concentration, type of electrode, turbidity, voltage, pH, and
56 time. Decolorization removal was 95% and 93% using aluminum and iron electrodes respectively (203). Maghana et al. concluded that BOD, COD, and conductivity were capable of being removed by electrocoagulation within tea effluent. Using effluent from the Chemomi tea factory in Rift Valley, Kenya, the authors were capable to reduce COD by 96.6%, BOD by 84.0%, and conductivity by 31.5% and increase pH by 10.32%. The optimum parameters were electrical potential of 24 V, interelectrode distance of 5 mm, effluent volume ratio of 18.2 m2/m3, and a pH of 6. Electrocoagulation oxidized phenol tea color pigments that were ionization of iron in the pigments, form radicals, and phenols of long chains (204). Song et al. determined that 96% of colored and 80% TOC was removed by an ozone electrocoagulator with an optimum pH of 10, dye concentration of 100 mg/L, current density of 10 mA/cm2, salt concentration of 3,000 mg/L, temperature of 30 degrees Celsius, ozone flow rate of 20 mg/L, and electrode distance of 3 cm (205). Sengil et al. were able to decolorize 98% of Reactive Black 5 from synthetic wastewater by using electrocoagulation with iron electrodes. Optimum conditions for treatment include dye concentration of 100 mg/L, pH of 5, current density of 4.575 mA/cm2, salt concentration of 3,000 mg/L, temperature of 20 degrees Celsius, and inter electrode distance of 2.5 cm. The authors also observed electrical energy consumption of 4.96 kWh/kg dye (206).
Zidane et al. concluded that by testing various NaCl concentrations as an electrode by a factor of ten (1.5 and 1) from 10−3 to 10−2, electrocoagulation could treat CI Red
Reactive 41 at best between 41 and 96% removal efficiency, where the absorption was at
540 nm through 60 minutes of treatment, where concentration ranges between 100 and
400 mg/L. At the 400 mg/L concentration, 88% absorption occurs within 10 minutes, as
57 compared to 60% when using direct coagulation using an electric potential of 10 to 15 V.
However, 100% removal could be achieved when using electrochemical treatment (207).
Kalyani et al. determined that COD removal was 95% and color removal was 92% when using mild steel, and 89% color and COD removal for aluminum electrodes when attempting to treat pulp and paper industrial effluent. When it was combined using electrocoagulation with a sequential batch reactor, Langumir and Radke-Paushitz isotherm models were used for an adsorption isotherm (208). Merzouk et al. concluded that COD removal was greater than 80% and color 85% when considering synthetic textile wastewater using aluminum electrodes, a COD 2,500 mg/L dye concentration was reduced to less than 200 mg/L, a pH between 6 and 9, residence time of 14 minutes, current density of 31.25 mA/cm2, and water conductivity of 2.4 mS/cm at an electrode distance of 1 cm (209). Essadki et al. concluded that 80% COD and color as a function of current density. Additional efficiency specific energy and electrode related to current, electrode gap, and conductivity. This can be achieved by using pollutant flotation and recirculation with H2 microbubbles by water electrolysis. This was achieved using red dye from Moroccan textile using a
20 L external loop air-reactor with a batch electrocoagulator (210). Hanafi et al. compiled a study on olive mill wastewater treatment considering modifying COD, polyphenols, dark color removal, and pH. Through an optimum time of 15 minutes, 2 mg/L of Cl2
2 concentration, pH of 4.2, and density of 250 A/m , polyphenols were reduced by 70%, electrode composition is 0.085kg Al/kg CODremoced, and energy concsumption 2.63 kWh/kg CODremoved (211).
58
Balla et al. studied the efficiency of an electrocoagulation/electroflotation had a 90% removal rate of color with synthetic mixture as compared to 78–90% removal of color in textile wastewater by providing mixture of Red S3B 195, Yellow SPD, Blue BRFS,
Yellow Terasil 4G, Red terasil 343, and Blue terasil 3R02. In addition, electrical energy for mixture dyes was high energy requirement than the two other dyes (212). Animes et al. concluded that application of an electrocoagulation process, color was removed between 90 and 98% when operating the apparatus for one hour treatment of trypan blue, orange G with electrodes made from mild steel and aluminum electrodes. Additional effects involved higher density currents and pH (213).
3.5. WASTEWATER TREATMENT 3.5.1 DOMESTIC WASTEWATER TREATMENT
Yang et al. studied the electrocoagulation electroflotation processes and noted that high COD removal could be achieved; however, suspended solids and color removal was not conducive for secondary sewage treatment; nevertheless, electrocoagulation could be used for small scale, decentralized municipal domestic sewage treatment (214). Illhan et al. concluded that COD and SS could be removed at 60 and 70%, respectively, from domestic wastewater at the Istanbul-Yenikapi Domestic Wastewater Treatment Plant with electrocoagulation using iron-iron electrodes. Operating parameters included 0.6 W electrical power, electrolysis time of 15 minutes for heavy load (380 mg COD/L) and 8 minutes for weakly loaded (260 mg COD/L). It was determined that electrical charge conditions were 0.4 kWh/m3 for heavy loads, and 0.2 kWh/m3 for weakly loaded. Sludge production was between 1.5 and 2%. (215). Bukhari was capable of removing 95.4% TSS treatment efficiency with a current of 0.8 A and a contact time of 5 minutes using an
59 electro-coagulator with stainless steel electrodes. The pattern is noticed was a sweep-floc coagulation where soluble ferrous ions were changed to insoluble ferric ions by oxidation with chlorine. Also, the BOD had a profound effect on the TSS removal in the presence of particulates (216). Rodrigo et al. is capable of removing ionic phosphorus and COD, when using conductive-diamond electrochemical oxidation and electrocoagulation for persistent organic consumption, specifically regeneration of urban wastewater. The study stated that energy consumption is capable of removal at values lower than 4.5 kWh/m3
(217).
3.5.2 INDUSTRIAL WASTEWATER TREATMENT
Zongo et al. determined that by using electrocoagulation for textile industry wastewater with aluminum and iron electrodes, the authors concluded that that the important parameters—energy consumption where COD, turbidity abatement, electrode material, current efficiency, and cell voltage. Absorbance and COD had similar variations along the treatment, where a model could relate metal dissolution and pollution substance
(218). Linares-Hernandez et al. determined that 99% COD, 100% color, and 100% turbidity was removed by a two-step process—electrocoagulation with iron electrode and electrooxidation with a boron dipped diamond electrode (219). Augustin determined that electrocoagulation aws capable of reducing turbidity, acidity, BOD, COD, and heavy metals within palm oil mill effluent from Chumporn Province in Thailand using aluminum electrodes and NaCl as electrolyte. Also, electrocoagulation was determined to have a strong recovery in these various components (220). Wang and Chou concluded that COD concentration could be reduced to value greater than 90% by electrocoagulation, below the Taiwan discharge standard of 100 mg/L, provided that the 60 concentration of chemical mechanical polishing wastewater was 200 mg/L NaCl, electrical potential of 20 V, and temperature of 25 degrees Celsius. With a 90% removal, it was noted that the water could be capable of being for possible reuse. Also, the kinetic study would reflect a pseudo-first kinetic model (221). Espinoza-Quinones et al. concluded that by treating leather-finishing processes using an electrocoagulation process using aluminum electrode plates under a pH of 7.6 and an electrolysis time between 30 and 45 minutes, the treatment efficiency for COD, turbidity, total suspended solids (TSS), total fixed solids (TFS), total volatile solids (TVS), and chemical effluent concentration.
This was confirmed through analysis of variance (ANOVA) with a 95% confidence level
(222).
Zhang et al. concluded that considering the use of organiochlorine pesticide contamination and found that a presence of pesticides within those soils. In addition, the authors suggested that human population within the vicinity of the soils were under a threat to being exposed to those pollutions, as it required treatment technologies to rid those pesticides from the soils (223). Asselin et al. concluded that total suspended solids
(TSS) was removed at 89%, turbidity 90%, BOD 86%, and oil and grease 99%, when completing electrocoagulation by combining mild steel or aluminum electrodes for treating slaughterhouse wastewater. In addition, it was identified that the total cost of treatment is 0.71 USD/m3 treated poultry slaughterhouse (PS) effluent, particularly including energy and electrode consumption and chemical and sludge disposal (224). El-
Naas et al. concluded that through batch experiments it was proven that the most effective treatment for petroleum refinery wastewater was using aluminum electrodes.
Factors that were discussed included current density and initial concentration of the
61 wastewater, where the temperature was 25 degrees Celsius and pH of 8 (225).
Khansorthong and Hunsom were able to reduce color and COD by 91% and 77%, respectively, with operating costs of 0.29 USD/m3 wastewater when treating pulp and paper mill by electrocoagulation in batch mode with 6 iron mate pieces. For continuous electrocoagulation, 91% color and 77% COD reduction was completed in 2.15 hours were first order kinetic was the model for filtering of choice. Also total BOD, COD, TSS,
TDS, pH, and color were acceptable for Thai Government Standards (226).
Chatzisyneon et al. concluded that by using electrochemical oxidation of olive mill wastewater (OMW) with a TiO2 anode, it was noticed that the oxidation of OMW at 43
Ah/L, 80 degrees Celsius, and 5 mM NaCl can completely remove color, phenols, ecotoxicity, and low 30% COD removal with a 50 A/cm2 current density (227). Oelmez-
Hanci et al. concluded that COD in olive mill wastewater was reduced by 30% and 20%
TOC when using UV254 and UV280 analysis. Effects were noticed based on pH and coagulant/polymer dosages, Fenton treatment is based on pH and Fe(II) concentrations, steel electrodes at various concentrations, and current densities (228). Raju et al. concluded that COD would be reduced from 1,316 mg/L to 42.9 mg/L when using electrocoagulation for the purpose of treating textile wastewater. Treatment was completed using graphite and RuO2/IrO2/TaO2 with titanium electrodes. Overall, electrooxidation noticed the effects of electrolyte type within relation to
Cl− ions (229).
Espinoza-Quniones et al. concluded that pollutant removal was completely accomplished for COD, turbidity, and concentrations of chromium, provided that pH is neutral and electrocoagulation ranges between 30 and 45 min. In addition, experimental
62 design is necessary to be a factoral fraction 23, for a leather finishing industrial process wastewater for organic and industrial pollutant removal (230). Zaied and Bellahkal determined that using a pH of 7, electrolysis time of 50 minutes, current density of 14 mA/cm2, treatment of black liquor by elecrocoagulation removed 98% COD, 92% polyphenols, and 99% color (231). Zaleska-Chrost et al. determined that laboratory conditions for electrocoagulation was a better treatment than chemical coagulation, having identified COD, turbidity, suspended solids, and color, where crude sewage is contingent on the pollutant removal efficiency (232). Tezcan Un et al. concluded that electrocoagulation with aluminum electrodes was capable of successfully treating vegetable oil refinery wastewater with aluminum electrodes. Within the study, the authors considered optimum conditions for pH, poly-aluminum chloride (PAC) and
Na2SO4 dosage. The concluding value was that 98.9% COD was removed in 90 minutes, where the current density was 35 mA/cm2 and energy consumption of 42 kWh/kg COD removed (233). Sengil et al. determined that COD (82%), sulphide (90%), and oil-grease removal (96%) from tannery liming drum wastewater by electrogoaluation. Optimum parameters for treatment were 35 mA/cm2, 10 minutes electrolysis time, pH of 3, near energy consumption of 5.768 kWh/m3 COD, 0.524 kWh/m3 sulfide, and 0.00018 kWh/m3 oil-grease. Kinetics from the experiment produced a pseudo-second order rate equation
(234). Desphande et al. concluded that bulk drug industry wastewater COD could be removed at 34%, with a BOD5/COD ratio of 0.581 at 120 treatment time, when using the electrochemical method. The optuimum parameters include 95.83 kWh/kg energy consumption and an anode efficiency of 5.76 kg COD/Am2h (235). Linarez-Hernandez et al. observed that the combination of electrocoagulation and electroxidation was capable
63 of successful at complining treatment—electrocoagulation coagulates and removes particulates, while electrocoagulation oxidizes what remains. Overall, the process is capable of reducing COD, BOD5, color, turbidity, and coliforms in 2 hours (236).
Wang et al. was capable of removing 62% COD when using ultrasound for electrocoagulation. Nevertheless, higher removal efficiency of COD is capable with optimum conditions of 5 V electrical potential, chlorine contact less than 2,500 ppm, two aluminum electrodes and a 999 mg/dm3 kWh amount per joule, where the treatment was contingent on the amount of aluminum plates present (237). Wang et al. concluding that by using iron and aluminum electrode pair, 200 ppm NaCl, 30 V of electricity, silica particles and turbidity reduction is feasible from oxide chemical mechanical wastewater with a particle size produced was between 520 and 1,900 nm as the time range was between 10 and 20 minutes (238). Merzouk et al. determined that 85.5% SS, 76.2% turbidity, 88.9% BOD, 79.7% COD, and 93% color could removed by the combination of electrocoagulation-electroflotation after ensuring optimum conditions for 300 mg/L silica, current density of 11.55 mA/cm2, pH of 7.6, conductivity of 2.1 mS/cm, treatment time of 10 minutes, and electrode gap of 1 cm. The study was to consider for the treatment of textile wastewater having studied the above optimum parameters (239).
Katal and Pahlavanzadeh determined that by using aluminum and iron electrodes for electrocoagulation, optimum pH between 5 and 7, current density of 70 mA/cm2 was capable of efficiently treating the wastewater at a low cost. In addition, temperature relationship also poorly affects the performance (240).
Meas et al. determined that by using an electrocoagulator with sacrificial electrodes, where COD (95%), color (99%), and turbidity (99%) can be reduced when testing
64 fluorescent penetrated liquid for non-destructive testing of parts, where the water was reused 4 times (241). Aoudj et al. determined that decolorization can be achieved at 98% under the optimum condition of a pH of 6, 1.875 A/cm2 current density, inter-electrode distance of 1.5 cm, and NaCl electrolyte when removing Direct Red 8 from synthetic wastewater treatment (242). Monghadam and Amiri concluded that by using a current density of 75 A/m2, pH of 4, and conductivity of 3 mS/cm were capable of removing
TOC from a phenol-formaldehyde resin manufacturing wastewater by electrocoagulation with aluminum electrodes (243).
3.5.3 HEAVY METALS
Bazrafshan et al. determined that Cr(VI) reduction from synthetic chromium solution could be under legal limits as long as treatment was between 20 and 60 minutes, a range for electric potential of 20 and 40 V, and a pH of 3. Also, the authors determined that chromium removal efficiency was better with iron electrodes than aluminum (244). Bing-
Fang discovered that by using a room temperature of 25 degrees Celsius, iron and stainless steel electrodes, a voltage and a pH of 4, and a Na2SO4 concentration of 0.7 mg/L, and an electrolysis time of 30 minutes, electrocoagulation could treat a simulated wastewater with Cu+2 and Cr6+ removal of 93% and 98.91 effectively (245). Hansen et al. concluded that a 1 L airlift electrocoagulator could reduce arsenic concentration by 96%
(1000 mg/L to 40 mg/L) by iron electrodes. Results indicated that the oxidation of Fe+2 to
Fe3+ determined arsenic removal efficiency for arsenic concentration greater than 500 mg/L, whereas 98% arsenic removal was obtained for arsenic concentration of 100 mg/L.
Also, the rate of removal was 0.08–0.1 mg As/C, where Fe-to-As ratio (mol/mol) was about 486 (246). Qui et al. concluded that having a pH of 4, voltage 2.5 V, hydraulic 65 retention time of 15 minutes, current density of 25 A/m2, removal rate could be achieved at 99.5%, when treating electroplating wastewater by pulse electrocoagulation (247).
Belkacem et al. concluded that BOD5 removal was 93.5%, COD 90.3%, turbidity
78.7%, suspended solids, and color greater than 93% using electrofiltration with aluminum electrodes, where parameters involved electrical potential of 20 V, distance of
1 cm, and electrolysis time less than 20 minutes. Also, the kinetics was less than 5 minutes with a removal of 99% (248). Rayman and White concluded that by using a parallel-plate electrochemical reactor, the reduction of Cr(VI) using Fe(II) as the anode, the space velocity must remain at 0.02 s−1. It was also imperative to increase the current density by means of the current potential, the supporting electrolyte, decrease the distance between the electrodes for proper conversion; however, if one were to decrease the current density, the specific energy requirement increased (249). Heidmann and Calmano was capable of treating galvanized wastewater by successfully reducing heavy metals of
Cr and Cu by over 99% and 90% of Ni, as long as optimum conditions of a PH were greater than 5, 0.2 A for Fe electrodes, 1.5 A for Al electrodes, and a power consumption of 9.0 kWh/m3 (250). Deniel et al. determined that by using iron and hybrid Al/Fe electrodes for electrocoagulation, the electrodes were capable of reducing the arsenic concentration by 99%, as the current density was increased from 0.0082 to 0.0816 mA/cm (251).
Nouri et al. concluded that electrocoagulation had a treatment time between 20 and 60 minutes, 40 V electrical potential for the removal of Cr(VI) ions by using iron electrodes, and a pH of 3 (252). Wu et al. compared the use of electrocoagulation with aluminum and iron electrodes in combination with UV/TiO2 and ozone. It was determined that
66 decolorization efficiency could be increased by combining UV/TiO2 or UV/O3, and it was able to reduce power requirement to 8 W. Also, pH was increased to 7.4 as well.
What was also observed was that treatment followed pseudo-first order kinetics (253).
Heidmann and Calmano reported that the parameters affecting electrocoagulation process included the chromium concentration, charge loading, and current concentration. Cr concentration decreased slightly by coagulation time at high currents (1.0–3.0 A), whereas at low currents (0.05–0.1 A), 10 mg/L Cr was removed completely from the solution after 45 min (254). Thella et al. concluded that electrode, water current, and gap affected the overall efficiency in treating for arsenic and chromium by a batch electrocoagulation. Optimum conditions found were that the value of pH was 4.0 for arsenic removal and 2.0 for chromium, current density of 75 A/m2 for arsenic and 50
A/m2 for chromium and a stirring rate value of 100 rpm (255). Petsriprasit et al. determined that Cu, Cr, Pb, and Zn from billet industry wastewater was capable of being removed by 99%, where it was found that density current is 98 A/m2, pH of 5, and 30 minutes electrolysis time. It was noticed that within 120 minutes, pH of 3, and flow rate of 55 mL/min could obtain similar values (256).
Shafaei et al. was capable of removing Mn2+ ions by electrocoagulation with aluminum electrodes under an optimum pH of 7.0. Factors concluded by the authors were the density and electrolysis time, along with initial concentration were capable of determining successful removal rates (257). Vansudervan et al. was capable of removing arsenate by electrocoagulation with aluminum alloy as anode and stainless steel as a cathode. The removal efficiency was 98.4%, current density of 0.2 A/dm2, and a pH of
67
7.0. Arsenate adsorption could be fitted into a Langumir adsorption isotherm with first and second order rate equations (258).
3.5.4 ORGANIC AND INORGANIC REMOVAL
Mahvi et al. concluded that sulfate removal was best removed whenever the electrical potential was 30 V, reaction time 60 minutes, and pH of 11 when using a six-plate aluminum electrode electrocoagulator. Initial concentration was also an important factor as the authors considered treatment at 350 and 700 mg/L concentrations (259). Kongjao et al. determined that chromium and pollutants could be removed within 95% by considering tannery wastewater with a one-step electrocoagulation process. Additional parameters included a pH between 7 and 9, current density of 22.4 A/m2, flow of 3.67
L/min, and 20 minute electrolysis time. Energy consumption was reported as being 0.13 kWh/m3 and a first order kinetics model for COD removal (260). Kumar et al. concluded that COD and color removal was 50% and 95.2%, respectively, at optimum conditions for treatment of bio-effluent wastewater using electrocoagulation. Parameters to consider included current density between 44.65 and 223.25 Am/cm2, pH between 2 and 8, inter- electrode distance of 1 and 3 cm, and electrolysis time between 30 and 150 minutes. It was determined through ANOVA analysis the r2 value was 0.8547 (261). Barrea-Diaz et al. concluded that COD (92%), BOD (89%), color (92%), turbidity (95%), and total coliform (99%) when adding hydrogen peroxide to electrocoagulator using aluminum and iron electrodes to treat a complex wastewater consisting of organic compounds (262).
Bhaskar Raju et al. determined that COD was removed between 90 and 93% by graphite, 54% by RuO2/IrO2/TaO2 coated titanium electrodes, when using various
68 electrochemical methods for treating textile wastewater. The treatment method was electrocoagulation by steel electrodes for suspended solids and electroxidation for COD removal for the purpose of pretreatment method to reverse osmosis (263). Yilmaz et al. studied the removal of boron from synthetic wastewater having considered pH as an optimum at 8.0. After looking at the resin/boron solution, boron concentration, stirring speed, and temperature, 99% removal occurred, where the boron removal rate was affected by stirring speed and temperature—increasing the speed, decreased the floc formulation and removal; increasing the temperature increased the boron removal. The authors developed a pseudo-second order model equation based on the heterogeneous fluid-solid reaction (264). Xu et al. determined that by using multi-staged electrocoagulation, 99% removal from boron concentration was completed after the fifth stage taking the concentration from 500 mg/L to less than 0.5 mg/L under a current density of 62.1 A/m2. In addition, the authors noticed that arsenic removal was successful in reducing the concentration from 15 mg/L to 0.5 mg/L (265). Hansen and Ottosen suggested that reason electrocoagulation is a suitable treatment for arsenic removal because of its ability to precipitate hydroxide-arsenic compounds. However, Ca(OH)2, prior to successful removal of arsenic required additional treatment (266). Khatibikamal et al. determined that pH between 6 and 7, when using electrocoagulation with aluminum electrodes was optimal for treatment, where the pH would be reduced over time, number of plates had no effect, and second rate kinetics model was concluded for absorption.
Fluoride was reduced from 4 to 6 mg/L to 0.5 mg/L (267).
3.5.5 WITH PHOTOLYSIS DEGRADATION, ADVANCED OXIDATION
69
Boroski et al. concluded that electrocoagulation filtration combined photocatalysis using a UV/TiO2/H2O2 systems improved the biodegradable index (BOD/COD) from
0.48 after electrocoagulation to 0.89 with the use of the hybrid system, using 6 hours photocatalysis irritation, provided a 30 minute EC/Fe0, 153 A/m2, pH of 6.0. A COD reduction was 88% in the system, provided 50 mmol/L H2O2 and an additional NaCl concentration of 5.0 g/L reduced electrolysis time from 30 to 10 minutes (268). Apaydin and Gonullu concluded that COD and sulfide were capable of being removed at 46% and
90% during electrocoagualtion, whereas electro-Fenton was efficient in removing 54% and 85%, when treating tannery wastewater using iron electrodes. Operating parameters for the treatment process included electrical current of 33.3 mA/m2, electrical
2 2− consumption of 1.5 kWh/m COD removal and 8.3 kWh/kg SO4 removed (269). Parga et al. developed a technique by using electrocoagulation as a method was very important as the photocatalysis degradation of cyanide by TiO2 required particle fineness; so using electrocoagulation for recovery has a 93% recovery rate. Also it was determined that the
Langmuir isotherm procedures developed included (free energy value) ∆G0 = −37
0 0 kK/mol, enthalphy (∆H = −54 kJ/mol) and entropy (∆S = 0.524 kJ/mol), where TiO2 is
93% in 30 minutes electrolysis time and using a 50 W halogen lamp (270). Rodrigues et al. observed 91% turbidity and 86% COD removal, going from 1,753 to 60 mg/L using strictly electrocoagulation, while reduction was capable at 50 mg/L when using electrocoagulation combined with photocatalysis using titanium dioxide for treating pharmaceutical wastewater. Optimum conditions included iron electrodes, current density of 763 A/m2, 90 minute treatment time, and pH of 6, whereas photocatalysis
70
(UV/TiO2/H2O2) consisted of a pH of 3,45 irritation, 0.25 g/L TiO2, 10 mmol/L H2O2
(271).
Tezcan Un et al. concluded that by using a hybrid electrocoagulation with iron and aluminum electrodes and Na2SO4 and a pH of 7.8, the treatment process was capable of removing 94.4% COD of an initial concentration of 1,200 mg/L. The optimum parameters included a polyaluminum coagulation with 0.75 g/L PAC concentration. The treatment met slaughterhouse wastewater treatment standards in Turkey. Also, combined with the Fenton process, 81.1% COD was removed with 9% H2O2 (272). Canizares et al. concluded that the use of a boron doped diamed (BDD) anode for soluble organic matter mineralization was capable of breaking down metalworking wastewater by physio- chemical processes using an electrochemical reactor (273). Li et al. concluded that oil removal rate from oilfield wastewater using an electrocoagulator with sacrificial aluminum electrodes was capable of removing 89.6%, where the current intensity is 1 A, plate spacing of 10 mm, initial oilfield concentration is 500 mg/L, pH 7.2, and electrolysis time of 20 minutes (274). Zhang et al. decolorized CI Acid Red 2 on a platinum rotating disc resulting in a 98% reduction within 40 minutes by using an electrogenerated iron hydroxide from eelctrogenerated ferrous irons, where decolorization was combined with electrocoagulation electro oxidation procedures (275).
Hernandez-Ortega concluded that turbidity by color at 90% and COD by 60% when using a combined electrocoagulation-ozonation process with wastewater from 140 factories. This process was suitable to treat industrial wastewater prior to the biological process of wastewater treatment (276).
3.5.6. ADSORPTION, MEMBRANES 71
Narayanan and Ganesam observed that chromium (VI) removal can be achieved by the combination of electrocoagulation and granulation activated carbon (GAC) at a pH of
8. The optimum conditions would be considered when the current density has been increased to 26.7 mA/cm2, operating time of 100 minutes for the electrocoagulation. The authors noticed that with GAC added to the systems it dramatically reduced the concentration of chromium (277). Yüksel et al. determined that sodium doecyl sulfate
(SDS) removal was 81.6% for peroxi-electrocoagulation, where the optimum conditions included 60 mg/L, 0.5 mA/cm2, 10 minutes electrolysis time, and an energy consumption of 1.63 kWh/kg SDS. In addition, pseudo-second order equation was observed (278).
Kumarasinghe et al. concluded that for a model wastewater consisting of copper, lead, and cadmium, the removal was contingent and electrolysis time, current density, and solution pH when using a hybrid electrocoagulation-ultradfiltration system. The authors concluded that removal was greatly affected by maintaining a non-acidic pH (279).
Aouni et al. discovered that COD, turbidity, and color could be removed from textile wastewater using electrocoagulation with nanofiltration. The parameters considered included current densities and experimental tense. For each treatment process, electrochemical treatment was for color and COD, while nanofiltration for color, COD, alkalinity, conductivity, and total dissolved solids (TDS) (280). Yang and Tsai concluded that electrocoagulation/electrofiltration treatment with carbon filters and carbon/alumina tubular composite membranes was successful in treating chemical mechanical polishing of the copper layer (Cu-CMP) wastewater with pore size between 2 and 20 mm and nominal pore size of 3.5 mm. The treatment led to an 82 to 91% TS removal, TOC
72 removal, Cu, and Si removals (281). Chang et al. combined electrocoagulation-activated carbon adsorption-microwave generation. Through electrocoagulation, 39% COD removal occurred with pH of 8, electrolysis time of 8 minutes, and a density of 277 A/m2, and a NaCl contact of 1 g/L. The study produced favorable results with 100 g/L GAC that removed 82% of Reactive Black 5 (RB5) and with 20 g/L GAC that removed the remaining 61% of COD (282). Chou et al. used electrocoagulation for removal of COD in oxide CMP wastewater, where it was determined that COD could be reduced by 90%.
Also, the authors determined that this process followed pseudo-second order under the
Freundlich adsorption isotherm model at various densities and temperatures (283).
Lakshmanan et al. concluded that arsenic was removed by 98% when using NaCl, and was removed by 75% when using sodium sulfate and nitrate during a 5 minute appearance and an initial concentration of wastewater of 10 mg/L within the electrocoagulator. Adsorption was affected by several factors, including magnetic, particle size, and surface properties of the precipitate; solid waste from the treatment was non-hazardous (284).
3.5.7 AEROBIC, ANAEROBIC PROCESSES
Yetilnezsoy et al. discovered a 90% COD and 92% color efficiency by electrocoagulation from an upflow aerobic sludge blanket reactor with aluminum and iron electrodes. It was also determined that aluminum electrodes were more efficient for treatment. Optimum conditions would be a pH of 5, current density of 15 mA/cm2, and an electrolysis time of 20 minutes (285). Barrea-Diaz et al. concluded that COD was removed at 68% when combining with electrochemical and biological treatment for the removal of complex industrial wastewater using aluminum reactor. These treatment 73 efficiencies improved recalcitrant concentrations in the wastewater as compared to biological whereas COD only reduced treatment by 30%. Also, the authors noticed that if aluminum polyhydroxychloride (PAC) coagulant was added at a rate of 4 mL/dm3 along with a batch electrochemical reactor, it would greatly improved COD and color results
(286). Basha et al. proved that the combination of electrochemical degradation and biological oxidation was capable of reducing COD by 80% from 48,000 mg/L to 17,000 mg/L from the organic industrial wastes. The microorganisms used were Baciliu subtillis,
Pseudomones aeruginosa, and Proteus vulgaris. It was concluded that the water could be reused following experiments (287). Desphande et al. concluded that using a combined electrocoagulation and anaerobic fixed film reactor, COD, BOD, and color could be removed at 24%, 35%, and 70%, respectively, with conditions of pH at 7.2, current density of 80 A/m2, and electrolysis time of 25 minutes for mere electrocoagulation.
However, when combined with the anaerobic fixed film reaction, removals increased to
80-90% COD, 86-94% BOD, at 0.6 to 4.0 kg COD/m3s organic loading rate (288).
Phalakornukule et al. concluded that treatment of Reactive Blue 140 and Direct Red
23 required electrical energy of 1.42 and 0.69 kWh/m3, respectively, with color (99%),
COD (93%), and TS (89%) removal, when using a continuous electrocoagulation. In addition, the authors were able to harvest hydrogen (289). Moises et al. conducted study to remove color (94%), turbidity (92%), COD (80%) for industrial wastewater at a flow rate of 50 mL/min (290).
3.5.8 DYE REMOVAL
74
Wang et al. determined that high removal efficiency of orange G simulated dye could be achieved, when the pH was 4.5, NaCl concentration was 0.75 g/L, space between electrodes was 10 mm, treatment time 10 minutes. The authors observed no relation with the applied voltage (291). Rahgu and Basha removed 100% COD and 92% color by the use of Ti/RuO2/IrO2 as anode and stainless steel as cathode within an electrochemical membrane for the purpose of treating textile dyebath and generate caustic soda, where the caustic soda generation went from 40 to 210.28 g/L (292). Phalakorkule et al. reported a study for treating Reactive Blue 140 reactive dye and disperse dye II. Results indicated that color was reduced by 95% with an energy consumption of 1 kWh/m3 and a dye concentration of 100 mg/L during synthetic treatment (293). Mollah et al. removed
94.5% of orange II dye from 10 ppm at density of 160 A/m2, pH of 6.5, conductance of
7.1 mS/cm, flow rate of 350 mL/min, and NaCl concentration of 4.0 g/L (294).
3.5.9 PRETREATMENT
Arsten-Alaton and Turkoglu concluded that using aluminum and stainless steel electrodes for color and COD removal for disperse dyebath, treatment was able to remove color and COD instantaneously at a pH of around 7.0 for complete color removal and
61% COD removal for conditions of using 2,000 mg/L NaCl electrolyte and an electrical current density of 44 mA/cm. This process was compared to coagulation process which used aluminum sulfate as a coagulation between 200 and 2,000 mg/L, under a pH between 3.5–11.5 (295). Khoufi et al. concluded that elctrocoagulation as pretreatment was very successful in treating olive mill wastewater by reducing toxicity and improved the performance of methanization as long as the oxygen loading rate was between 4 and
7.5 COD/day. Because of electrocoagulation, anaerobic digestion and particularly 75 methanization improved COD removal to 80%. Aerobic treatment removed COD by
78.7%, and extraction of ethyl acetate was at 90% recovery (296).
3.5.10 OTHER TREATMENT
Koparal et al. concluded that removal efficiency for humic substances in synthetically prepared wastewater within electrocoagulation using aluminum plate electrodes was related to the initial pH at 5 due to the presence of a gel layer formation on the anode surfaces. This was observed at high concentrations, where the concentration is greater than 120 mg/L (297). Panizza and Cerisola was successful in removing 75% chemical oxygen demand (COD) from carwash wastewater by combining electrocoagulation with iron anodes and electrochemical with boron-doped diamond anode. What was determined was a 2 mA/cm2, pH of 6.4, electrolysis time of 5 minutes. What was noticeable was that energy consumption is 0.4 kWh/m3 (298). Yu et al. determined that chemical oxygen demand (CODcr) and turbidity was removed by 57.8% and 88.2% ,respectively, as it was observed that CODcr reduced from 144.15 mg/L to 60.96 mg/L, where turbidity was reduced from 39.06 NTU to 4.61 NTU. Operating parameters included 25 V electric potential, electrolysis time of 10 minutes, and a pH between 7 and 7.5 for carwash wastewater treated by electrocoagulation-flotating-contact filtration process (299).
Liu et al. concluded that removing chlorophyll-a and UV-254 was 81% and 56%, respectively, while turbidity was recorded at a value less than 2.6 NTU when using an electrocoagulation-flotation treatment system for landscape wastewater (300). Mao et al. observed that the treatment of bathing wastewater was most effective in using electrocoauglation-air flotation, Biological Activated Carbon, Membrane Bioreactor, and
76 filtration/ultrafiltration-biological activated carbon processes (301).
Arslan-Alaton et al. was capable of achieving 100% color using current between 33 and
65 mA/cm2 and the time within the elctrocoagulator using aluminum electrodes between
10 and 15 minutes. Also, it was noted that the electrical energy consumption was only 5 kWh/m3 within an electrocoagulator for treating real reactive dyebath effluent. This was contrary to using stainless steel electrodes which consumed 9 kWh/m3 of electrical energy (302). Cano Rodriguez et al. concluded that using a photoremediation technique of having Myriophyllum aquaticum along with electrocoagulation and a current density of 45.45 A/m2, and pH of 8 couild remove COD (91%), color (97%), and turbidity (98%) from mixed industrial wastewater (303).
3.6 ANALYTICAL AND INSTRUMENTATION
3.6.1 EQUIPMENT
Moreno et al. studied green rust significance within electrocoagulation by considering measuring pH at locations near iron electrodes and observed that electrocoagulation was related to components such as solubility. Having analyzed components such as metal and non metal removal, suspended solids, organic compounds, COD (chemical oxygen demand) and BOD (biochemical oxygen demand), the authors observed that iron electrodes were more successful than aluminum electrodes for durability and cost (304).
Eyvaz et al. used electrocoagulation with parallel aluminum electrodes in batch mode for comparing alternative pulse current (APC) and direct current (DC) for treating Dianix
Yellow XCC and Procium Yellow. It was determined that the alternating pulse current was better in removal of TOC and dye treatment as compared with the DC power supply
77
(305). Wang et al. found that using a pulse frequency between 500 to approximately
2,000 Hz had an effect on treatment performance. Overall, pulse current was found to be better than direct current, reducing power consumption by 40 to 50% (306). Wang et al. concluded that by using DC electrocoagulation, in a treatment time of 25 minutes, decolorization and COD removal was 75.45% and 84.62%, respectively. The authors discovered a relationship between an increase of pH and alkalinity with increase in temperature, and electrolysis time, while voltage was related to current (307).
3.6.2 TREATMENT EFFICIENCY
Hu et al. concluded that there was a relationship between flow rate and suspended solids in a continuous electrocoagulation-flotation system with aluminum electrodes— removal efficiency was decreased when flow rate was greater than 800 mL/min, increased at 200 mL/min. The authors concluded that the rG/S (gas-solid ratio) under 0.1
L/g was ineffective for flotation, while rG/L (gas-liquid ratio) over 0.4 discontinued the suspended solids removal, and in fact, increased suspended solids (308). Abdelwahab et al. concluded a relationship between current density and pH for removal of phenol from oil refinery wastewater. It was determined that pH 7 and electrolysis time of two hours with electrocoagulation, that 97% of phenol was removed down to 30 mg/L. For petroleum wastewater, 94.5% of phenol was removed within 2 hours (309).
3.6.3 ELECTRICAL PROPERTIES
Sasson et al. reported that ferric anode was dissolved in a pH range between 5 and 9, electric current between 0.05–0.4 A, where rates were dependent on pH and oxidation rates of iron. The values calculated were theoretical based on Faraday’s law and what 78 was observed was different because with the calculation of non-dissolution without current, additional electrons present with reactions outside the anode were placed in the calculation as important parameters (310). Arslan-Altan and Turkoglu determined that alum was effective to remove color by 100% and COD by 64%. It was found that at optimum electrochemical conditions were 2,500 NaCl, pH 7, and current density is 44 mA/cm2.100% color and 58% COD were removed for Aluminum electrodes where total treatment time of 30 minutes, as compared with 100% color and 45% COD removal using 2,000 mg/L NaCl, pH of 7.3, and a current density of 44 mA/cm2 with a 60 minute treatment time. The most optimum treatment for coagulation was alum as compared with ferrous sulfate and ferric chloride (311). Mouedhen et al. concluded that electrocoagulation with iron and aluminum electrodes with hexavalent chromium by abatement with Cr(III), Fe(II), and Fe(III), where various anode/cathode configurations
Fe/Fe, Pt Ti/Fe, Al/Al, and
Pt Ti/Al were studied. Based on the results, Fe electrodes affected chromium removal by less than 5%, Fe(II) assisted in the removal where acid pH predominated (312). Zongo et al. considered treatment of Cr(VI) by electrocoagulation using Al or Fe electrodes with a discontinuous system. The results showed that COD removal was not affected by Cr(VI) for aluminum electrodes, where for Fe electrodes there was delay ine COD removal.
Also, it was determined that Cr(III) precipitation was due to Fe(OH)3 compound.
However, removal could be through electrogenerated Fe(II), air oxygen, and reduction at the ion cathode (313).
3.6.4 OPERATING PARAMETERS
79
Arslan-Alaton concluded that complete color removal and partial COD removal could be accomplished by using an electrocoagulation by using both aluminum and stainless steel electrodes were optimized. In fact, the authors noticed that electrical energy and sludge production rate was lower with stainless steel (8 kWh/m3 and 700 g/m3) as compared to aluminum (17 kWh/m3 and 8,200 g/m3) (314). Zodi et al. determined several parameters considered when treating industrial wastewater. First, using electrocoagulation then settling, one can consider suspended solids, high turbidity, and
COD. Next, sludge data was compared with models, and then determined based on sludge volume index (SVI) for the optimum conditions (315). Valero et al. determined that treatment of textile dye Remazol Red RB 133 by an electrocoagulator with a photovoltaic array configuration affected the power generated, where the major parameter flow ratio was controlled (316). Canizares et al. concluded that pH had an overall effect on the treatment efficiency for electrocoagulation for synthetic oil-in-water emulsion and effluent from a door manufacturing facility (317).
3.6.5 ENERGY REQUIREMENTS
Sasson and Adin concluded that using electroflotation with a current of 0.4 A, followed by slow-mixing, and filtration to treat pure water with silica-chemical mechanical polishing was able to reduce energy requirements for filtration by 90%. Also, pH must remain above 7 since the permeate change colors due to iron residuals (Fe2+ to
Fe3+) (318). Sasson and Adin considered silica-CMP suspensions were pretreated by electrocoagulation at an electric current of 0.4 A, slow mixing, and filtration. Filtration energy was reduced by 90% whenever the pH was between 6 and 6.5, having noticed foul mitigation was on intensity and mechanisms, suspension pH and electroflocculation time 80
(319). Chou et al. concluded that by using an iron/aluminum electrode pair for electrocoagulation, 100 mg/L NaCl, 20 mg/L initial wastewater concentration, and 20 V voltage application, indium (III) can be successfully removed. In addition, removal kinetics followed a pseudo second-order reaction (320). Chou et al. concluded that COD and turbidity were removed by 90% and 98%, respectively, within real oxide-chemical mechanical polishing wastewater by means of a batch electrocoagulator. The authors noted that additional conditions that were optimum included 200 mg/L of NaCl, 20 V application of voltage and 12 min electrolysis time (321). Terrazes et al. determined turbidity removal was 92% with an energy consumption of 0.68 kWh/m3 by using micro- electrolysis and macro-electrolysis electrocoagulation for tissue paper wastewater treatment (322).
3.7 COMPARISON
Canizares et al. determined that using electrocoagulation consisted of lower costs for small coagulant requirement, as compared with coagulation, whereas higher requirement may favor conventional coagulation for removal of pollutants (323). Khataee et al. developed a hybrid study using Fenton, electrocoagulation, UV/Nano-TiO2, Fenton-like, and Electro Fenton to remove C.I. Acid Blue 9, where 98% color was removed when the solution contained 20 mg/L, pH of 6, an electrolysis time of 8 minutes, and a current density of 25 mA/m2. Electrocoagulation was the second highest in decolorization efficiency, behind Fenton in decolorization efficiency (324).
El-Ashtoukhy and Amin concluded that electrocoagulation was capable of removing
87% of acid green dye 50, as compared COD removal of 68% for electrochemical
81 oxidation. Energy consumption was lower in energy consumption (2.8 to 12.8 kWh/kg dye, versus 3.31 to 16.97 kWh/kg dye) (325). Kılıç and Hoşten compared electrocoagulation and coagulation and stated that coagulation could be more efficient around 5-8, as compared to electrocoagulation using aluminum hydroxide as a precipitate. Electrocoagulation was second order kinetics on less than 10 minutes (326).
3.8 COST ANALYSIS
Having many treatment process options for wastewater treatment, it is necessary for electrocoauglation to be cost-effective. Kobya et al. found that the treatment of cadmium and nickel from electroplating rinse water could be achieved at 99.4% for cadmium,
99.1% for nickel, and 99.7% for cyanide. The cost for treatment was $1.05/m3 for cadmium and $2.45/m3 for nickel and cyanide provided that the treatment maintained optimum conditions (327). Kobya et al. also studied Remazol Red 3B decolorization using iron electrodes and found that 99% decolorization was possible under optimum conditions. The authors found that energy consumption could achieve 3.3 kWh/kg dye at a cost of 0.6 euro/m3 (328). Meas et al. concluded that aluminum electrodes are capable of treating fluorescent penetrant liquid for non-destructing testing part of aircraft industry. Having used electrocoagulation, the treatment present found 95% of chemical oxygen demand (COD), 99% color, and 99% turbidity. With this high level of treatment, the cost were able to have a return of 17 weeks (329). Asselin et al. experiment and analyzed oil bilge wastewater [OBW] at laboratory scale used iron and aluminum electrodes using bipolar (BP) and monopolar (MP) configuration. Using optimum conditions, treatment of oil bilge wastewater by electrocoagulation 93% biochemical oxygen demand, 95.6% oil and grease, 99.8% total suspended solids, and 98.4% 82 turbidity. From this analysis, it was determined that the costs was $0.46/m3 of oil bilge treatment was for energy and electrode consumption, chemicals, and sludge disposal
(330).
Ghosh et al. used electrochemical using aluminum electrodes for the purpose of removing iron [Fe(II)] removal from tapwater having considered amorphous aluminum hydroxides, current densitites, and electrode density. From the experiment, the authors found that when treating a concentration of 15 mg/L Fe(II) concentration, it would cost
$6.05 USD/m3 of tapwater (331). Drogui et al. used electrocoagulation with mild steel electrodes treating agro-industry (meat processing, cereal, and food beverages) wastewater. Considering chemical oxygen demand (COD), 82% removal was achieved with treatment costs between $0.95 and $4.93 USD/m3, where the costs included electrical power, chemical, and electrode consumption (332).
The application can be extended to the shipping industry. When using electrocoagulation-floccuation in this industry, Drogui et al. was capable of removing
80% turbidity, 56% chemical oxygen demand (COD), 90% oil and grease, and 89% biochemical oxygen demand (BOD) having used bipolar electrode arrangements. The cost including energy and electrode consumption and sludge disposal ranged between
$1.54 to $2.40 CAN/m3 of ship waste effluent (333). Khansorthong and Humson used electrocoagulation to treat wastewater from the pulp and paper mill industry using parallel iron electrode comparing current density, pH, and flow rate. When using the optimum conditions, it was found that to remove color (97%) and COD (77%) it would cost $0.29 USD/m3 of wastewater (334). Orori et al. took sample a five locations from a
Kraft pulp and paper mill effluent—primary settling tank, two aerated lagoons, a
83 stabilization, and at the discharge comparing treatment efficiency using graphite electrodes and aluminum electrode with wood ash. Overall treatment with aluminum electrodes was better (60% BOD and 58.8% COD), but was more expensive than
3 3 graphite ($0.0006 to 0.0008 USD/m of wastewater) versus ($8.34 to $31.74 USD/m of wastewater) (335).
In addition, authors Yuksel et. al discussed the operation costs of electrocoagulation
(EC) by defining a linear relationship considering various parameters, such as electricity, electrodes, coagulant, maintenance, and labor (336). Equation 19 is shown below:
Total operation costs ($/m3) = Ax + By + Cz + Dt + E + F [19] (336)
where A = electricity price ($/kWh)
x = energy consumption/m3 of dye (kWh)
B = price of aluminum electrode/chemical costs
y = electrode consumption/m3
C = price for sludge transportation and disposal
z = sludge formation/m3
D = chemical price
t = chemical consumption/m3
E = cost of maintenance
F = cost of labor
84
Secula et. al discuss two major equations involving both electrical costs and overall treatment costs for electrocoagulation. First, in electrical costs, there are many parameters, evaluating voltage, current density, time, initial concentration of dye, and the volume of solution. Second, the costs of electrocauglation has been discussed as being the electrical energy and price of electricity (337). Equations 20 and 21 provide equations for making the evaluation:
UED = ∫(I *U dt)/(1000*V*Co*(Yt/100) [20] (337)
UED = energy demand (kWh/jg)
U = cell voltage (V)
I = current intensity (A)
t = time (h)
Co = initial concentration of dye (kg/m)
V = volume of solution (m^3)
Yt = efficiency removal at a given time (%)
EOC - EEC + MC - UED*EEP + MC [21] (337)
where
EOC = operation costs of electricity ($/kg)
EEC = consumption of electrical energy ($/kg dye)
85
EEP = price of electrical energy ($/kWh)
For the sake of perspective, the major operating costs that can be concluded involve the costs of aluminum sheet metal or any other electrode, electrolytes (sodium chloride,
NaCl), handling, treatment, and storage of not only wastewater following treatment, along with electricity (337) but also spent electrodes.
3.9. CHAPTER CONCLUSION
Electrocoagulation is a treatment process that is capable of being an effective treatment process as conventional methods such as chemical coagulation. Having observed trends over the last three years, it has been noted that electrocoagulation is capable of having high removal efficiencies of color, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and achieving a more efficient treatment processes quicker than traditional coagulation and inexpensive than other methods of treatment such as ultraviolet (UV) and ozone. Unlike biological treatment which requires specific conditions, therefore limiting the ability to treat many wastewaters with high toxicity, xenobiotic compounds, and pH, eletrocoagulation can be used to treat multifaceted wastewaters, including industrial, agricultural, and domestic. Continual research using this technology will not only improve its efficiency, but new modeling techniques can be used to predict many factors and develop equations that will predict the effectiveness of treatment.
86
CHAPTER 4
MATERIALS AND METHODS
4.1 MATERIALS 4.1.1 ELECTROCOAGULATION EXPERIMENT
4.1.1.1 DYE WASTEWATERS Dyes that were used for this experiment were purchased from Fisher Scientific. The following lists the dyes that were used and their weight as purchased:
1. Orange II (Acid Orange 7) 2. Acid Yellow 11 3. Naphthol Green B
4.1.1.2 REACTOR DIMENSIONS
For the Acid Yellow 11 experiments, the reactors used in the experiments were small totes with dimensions according to the manufacturer 11.43 cm x 13.97 cm x 16.51 cm
(338, 339), with an average electrode spacing of 13.10 cm apart. A total of three reactors were used for this experiment. However, one reactor was removed from commission and only two were used for the remaining experiments. For the Box Behnken experiments, only two reactors were used of identical dimensions, but the distance between electrodes was approximately 12.86 cm apart from each other. Electrodes were created from 87 aluminum sheet metal that was purchased from the local hardware store. An average electrode surface area was considered by measuring over 100 electrodes used during the experiments. It was determined that the average area was 16.05 cm2. This was the value used to calculate the current densities used in the experiment. Figures I-XV provide photographs of the experimental equipment for both electrocoagulation and photooxidation. Tables I- VI provide the experimental runs and the conditions that were conducted.
4.1.1.3 LAB ANALYSIS MATERIALS
4.1.1.3.1 TRANSMITTANCE MATERIALS
1. Milton Roy Spectronic 20D Spectrophotometer (for Acid Yellow 11 experiments)
2. Thermo Spectronic Genesys 20 Spectrophotometer (for Box Behnken Design and
Photooxidation Experiments)
3. Spectrophotometer Sample Vials
4. Wastewater Samples
5. Distilled Water
4.1.1.3.2 pH MATERIALS (340, 341)
The determination of pH will be using a pH meter with a glass electrode. The following materials will be used for the pH meter lab analysis methods:
1. Oakton Waterproof PC Tester 35 Multi-parameter pH Meters
2. Oakton—Buffers (pH 4,7,10 buffers) 3 x 500 mL
- 3. Acros Organics Nitric Acid (NO3 )
4. Fisher Scientific Magnesium Hydroxide, 1N
88
5. Distilled Water
6. Wastewater samples
7. Magnetic Stirrer
4.1.1.2.3 TEMPERATURE MATERIALS
The temperature was taken in between the time of sample collection and analysis. The following was used for temperature determination:
1. Oakton Waterproof PC Tester 35 Multi-parameter pH Meters
2. Distilled Water
3. Wastewater samples
4.1.2 PHOTO-OXIDATION EXPERIMENT
4.1.2.1 REACTOR SET-UP
The reactors were purchased from a local pet store. The following materials were used for the set-up:
1. Zoomed Powersun UV Bulbs (2)
2. Zoomed Deep Dome Lamp Fixture (2)
3. Magnetic Stirrers
4. The dimensions of the reactors were 21 ½”x 19 ¾” x 15 ¼” and 25”x 18 ½” x 13 ¼”
4.1.1.2 LAB ANALYSIS MATERIALS
1. Thermo Spectronic Genesys 20 Spectrophotometer (for Box Behnken Design and
Photooxidation Experiments); Spectronic 21D Miltron Roy Spectrophotometer was used for AY11 experiments
2. International Equipment Company HN-Centrifuge
3. Spectrophotometer Sample Vials
89
4. Wastewater Samples
5. Distilled Water
6. Anatase Titanium Dioxide (TiO2)
7. 250 mL glass beaker (2)
8. NORM-JECT (12 mL) syringes
9. Tisch Scientific 0.2 um syringe filters
4.2 METHODS
4.2.1 SPECIAL NOTES
Please note that due to the limitations of the Shimadzu TOC Analyzer, total organic carbon analysis was not completed as prescribed during the original thesis. In addition, it was discovered that during the experiments, that the methods for dye analysis were limited to only Acid Yellow 11 (AY11) due to the high removal of materials by the use of vacuum for separation of solids and liquids by the filtration system. Also, several different syringe types were used throughout the course of the experiment—ranging from
100 um pipettes and also 20 mL pipettes due to the limitations of equipment. Finally, some samples underwent filtration two-three times depending on the removal of solid particles when attempting to measure transmissivity.
One can see the procedures for all of our experiments within Appendix E.
90
CHAPTER 5
MODELING
5.1 THEORY OF KINETIC MODELS
5.1.1 THEORY OF MASS BALANCE
When one describes the efficiency of treatment, it can be stated that this change can be expressed by using kinetic models. In order to understand kinetic modeling, it must begin with the idea of mass balances. The idea behind mass balance is such that the rate of accumulation is expressed as the difference in rate of change mass entering and exiting the system. This rate of change is expressed in the units of a mass to a unit of time
(for example, in SI units, mass flow rate can be equal to kg/s; for English system of units, lb/s). Equations 22 and 23 mathematically show the relationship between the two entities:
Mass rate of accumulation = mass of input in – mass rate of output [22] (353)
dM/dt = dMin/dt – dMout/dt [23] (353)
91
The above equations provide details of a system in which there is a no reaction that takes place. Most engineering text books have an additional letter, r, which represents the rate of transformation or the kinetic constant. In the electrocoagulation- electroflotation treatment process, one could suggest that there is a reaction. This is because in most conditions the mass flow rate entering and exiting the system has been altered or changed by means of the mechanism within the ECF process. This would help explain why decolorization or the removal of organic carbon occurs when using this treatment process to reduce the color inside the synthetic wastewater. As stated, modifications of equations 22 and 23 can be expressed with the addition of the transformation rate, known as coefficient r. This will form equations 24 and 25:
Mass rate of accumulation = mass of input in – mass rate of output+/transformation rate [24] (353)
dM/dt = dMin/dt – dMout/dt + r [25] (353)
And knowing that mass can be derived from the product of the volume and the concentration, M = CV, therefore the change of mass equals the change of the product of the change of concentration and the volume (dCV). In our case, the volume does not change when we complete our experiments, therefore we can substitute VdC with dM, we would generate equation 26:
VdC/dt = dMin/dt – dMout/dt + r [26] (353)
Having understood this, one can now consider the relationship between mass balance and kinetics. Previously stated, we have come to the point of deriving equation
26. It can also be stated that from the descriptions of the previous paragraph, one can 92 substitute this conclusion for all M’s within the equation. If one were to do this and divide everything by V, one would generate equation 27:
dC/dt = dCin/dt – dCout/dt + r [27] (353)
Since we are using a batch reactor, there is no inward and outward flow; in other words, (dCin/dt = dCout = 0). This means that equation 28 will be the simplification of the right hand side of equation 29:
dC/dt = r [28] (353)
Finally, if we multiply both sides by dt, take the integral of both sides, where the range of the left hand said is t to 0, and the right hand side is concerned with the difference between the concentration at a given t at the initial concentration we will formulate equation 29:
rt = -Ct [29] (353)
Dividing both sides by t will develop C/t, where 1/t is known as k, or the kinetic rate constant. This rate constant has units of time-1 or equation 30:
n r = -kC t [30] (353)
This is the most common form of the rate constant. In many cases, the Ct within equation 28 always precedes by a subscript n which is known as the order of the reaction.
The important of n is such that when taking the integral of both sides, develops the uniqueness of each reaction order.
5.2 THEORY OF KINETIC RATE CONSTANTS 93
There are three common reaction rates—zero (n=0), first (n=1), and second (n=2).
A zero order reaction is derived from combining equation 26 and 28 to generate equation
31:
dC/dt = -k
[31]
(352, 353)
When one divides both by Ct and multiplies both by dt, it will generate equation 32:
dC = -k dt [32] (352)
Equation 33 is established by integrating both sides and using the range for the left-hand side from Co to Ct and the right hand side from 0 to t:
C – Co = kt [33] (352)
First order reaction:
dC/dt = -kC [34] (353, 354)
Transforms into the equation 35 using similar mathematical tactics as the zero order:
-kt Co = Ce [35] (353)
Second order reaction:
dC/dt = -kC2 [36] (353)
Equation 36 transforms into 37:
1/C – 1/Co = kt [37] (353)
When attempting to describe data significance, the most common form is to transform the data into a straight line. This would mean transforming the data in
94 acceptable forms. Kinetics is very important in regards to determining how efficient treatment is occurring.
5.3 DOE MODELING METHODS
5.3.1 RESPONSE SURFACE METHODS (RSM)
Response surface methodology (RSM) is a statistical method that reduces the amount of experiment runs necessary by using a modified amount of runs that still provides a sufficient amount of data to be statistically successful (355). Response surface methodology is very successful in the fact that it is very effective in predicting biochemical reactions (356). There are several examples of methods—Box-Behnken and
Central Composite. Overall, the objective of response surface methodology is to produce curvature, either cubic or quadratic, while limiting the amount of runs necessary to compare the various factors involved within a given experiment (357). Equation 38 denotes the typical equation that indicates a quadratic model:
2 2 2 y = b0 + b1x1 + b2x2 + b3x3 + b12x1x1 + b13x1x3 + b23x2x3 + b11x1 + b22x2 + b33x3 [38]
(357)
where
y = the resultant variable
xn = the factors considered within the experiment
b = coefficients
It has been argued that with the exception of few fields of study, the majority of engineering interactions are usually lower order variable interactions—the interactions among two variables are the variables themselves squared. One can even refer this theory
95 to Occam’s razor, which states that the best model is the most simplistic, when considering the choice between the simplest or more complex models (358). This is why many consider quadratic and cubic interactions to be of interest.
As a result of completing a response surface analysis, one will be able to graphically produce imagery that summarizes the results from the experiment. There are two types of graphic images that are produced by typical statistical software programs— contour and also response surface plots. Contour is very similar to a cartographer’s map as such that it is capable of indicating maximum or minimum points within the given data. The typical setup of a contour plot is that it a three-dimensional data given in two- dimensional space. One will consider looking at the abscissa and ordinate along with the contour elevations to determine the maximum points for a given set of data. Ultimately for the sake of dye treatment, using these plots would determine an optimize value for treating a given amount of a particular entity when considering a given factor.
The data for these particular plots has been derived from authors Balasubramian et al. which used response surface methodology to conclude the effects of current density and effluent concentration on arsenic removal using steel anodes in an electrocoagulator
(359). In addition, response surface plots can also predict what interactions are significant based on the presence of curvature. These graphics can tell a story of what has occurred in the experiment and also can provide an indicator in future experiments.
5.3.2 BOX-BEHNKEN
One can define Box-Behnken as a more efficient alternative to the 3k design.
Using 3k design, Box-Behnken consists of three treatments—low medium and high. Box
96
Behnken consists of using constituents where the design runs are the same distance as the center runs, implying this is a spherical design method (360). One unique consideration for Box-Behnken is that it removes any factorial or fractional factorial design from its development (361). The benefits of using Box-Behnken design is such that is has the capability of being able to be cost-efficient as it does not take into account factorial design as one would observe in central composite design and also it is ideal for entities that use three treatment levels and more than two factors considered within the experiment. The order of operations for Box-Behnken revolves around alternating between high (+1), low (-1), and center points (0), specifically having several runs consisting of simply central points (362), the three factors of Box-Behnken design. The formation of the high, low, and center points is the process of coding data. The values of the experiment are determined by using the Equation 39:
x = X – Xcenter/(ΔX) [39] (173,363-367)
where
x = coded data value (-1, 0, or 1)
X = uncoded data point
Xcenter = center point
ΔX = the distance between the two points
Box-Behnken design has been used previously by various authors to optimize decolorization of dye wastewater treatment using various treatment methods. Zhang and
Zheng was able to consider finding optimal conditions for acid green 20 by ultrasound
97 irridation with hydrogen peroxide having considered ultrasound power density, pH value of dye and hydrogen peroxide concentration (368). Sharma et al. also considered Box-
Behnken design for Disperse Yellow 211 decolorization by means of Bacillus substilius, comparing temperature, pH, and initial dye concentration (369). Annadurai et al. used photocatalytic decolorization of Congo Red using zinc oxide (ZnO), designing the experiment for Box-Behnken design, using dye colorization, catalyst weight, pH, and time as its factors (370). Annadurai et al. used the Box-Behnken design by means of a second-degree polynomial regression model for the purpose of treating Rhodamine 6G using chitosan and activated carbon adsorbents (371). Fazli et al. also used Box-Behnken to determine decolorization for the purpose of observing white rot fungi species
Ganodermo sp. for its ability to decolorize Reactive Blue 19 considering glycerol concentration, temperature, and pH (372).
In regards to the presence of Box-Behnken design in electrocoagulation, color removal has been optimized using iron anodes having considered current density, treatment time, and wash dilution (373). Chaualparit and Onwandee found Box-Behnken useful for electrocoagulation optimization of diesel wastewater having considered pH, applied voltage, and reactive time to predict chemical oxygen demand (COD), oil and grease (O&G), and suspended solids (SS) (374). Zodi et al. observed that Box-Behnken experimental design is capable of being used to determine the optimized industrial wastewater treatment by electrocoagulation having current density, pH, and treatment time its factors (375). Prasad conducted analysis using copper anodes for color removal considering current intensity, dilution, electrolysis time as major parameters (376).
5.4 RESPONSE SURFACE METHODOLOGY DESIGN 98
5.4.1 MODEL SETUP
A custom response surface methodology (RSM) was designed based on the results of the experiment for each coagulant—Alum (AlSO4), Ferric Sulfate (Fe2(SO4)3), and Ferric
Chloride (FeCl3). Two treatments were considered for coagulant (5 mg/L and 15 mg/L), pH (4 and 7), treatment time (0 and 30 minutes), and current density (18.69 and 24.92
A/m2). All data was entered into the Minitab data for the purpose of developing separate equations from the experiments. In addition, it is imperative to consider comparing data that was calculated from the equations, as compared with the recorded data from the experiment.
5.4.2 RESULTS
5.3.2.1 Alum Equation
Collection of each data point for all Alum experiments is presented in Table VII.
The development of the Alum equation based on coded data will be presented in two manners, based on Table VIII. First, an equation with all of the variables [40], followed by an equation that will only consider statistically significant variables [41]:
2 y = 0.511082 + 0.469311x1 + 0.0695x2 – 0.01215x3 –0.01709x4 – 0.08625x1 –
2 2 0.018188x2 – 0.00821x4 – 0.026962 x1x2 – 0.01811x1x3 – 0.00176x1x4 + 0.023936x2x3 –
0.00762 x2x4 + 0.014559 x3x4 [40]
2 y = 0.511082 + 0.469311x1 + 0.0695x2 – 0.01215x3 –0.08625x1 – 0.026962 x1x2 +
0.023936x2x3 [41]
99 where
x1 = Time (mins)
2 x2 = Current Density (A/m )
x3 = pH
x4 = Dose (mg/L)
When it came to Alum, it would be found that the treatment time (p =0.00), current density (p=0.00), were the more important parameters in regards to having the most effective treatment from the electrocoagulation experiment for linear interactions. It can be said that pH (p = 0.057) is an important factor, but since p>0.05, it cannot be considered statistically significant. In addition, the only significant quadratic interaction beyond time2 (p=0.00) would be the relationship between current density and pH (p =
0.019).
Current %Color Time Density pH Dose Removal StdOrder 0 18.69 4 5 0.00% 1 5 18.69 4 5 8.45% 2 10 18.69 4 5 26.24% 3 15 18.69 4 5 53.64% 4 20 18.69 4 5 76.38% 5 30 18.69 4 5 93.29% 6 0 18.69 4 10 0.00% 7 5 18.69 4 10 20.82% 8 10 18.69 4 10 26.48% 9 15 18.69 4 10 45.76% 10 20 18.69 4 10 69.41% 11 30 18.69 4 10 83.29% 12 0 18.69 4 15 0.00% 13 5 18.69 4 15 0.86% 14 10 18.69 4 15 24.29% 15
100
15 18.69 4 15 56.00% 16 20 18.69 4 15 59.71% 17 30 18.69 4 15 89.71% 18 0 18.69 7 5 0.00% 19 5 18.69 7 5 3.22% 20 10 18.69 7 5 26.02% 21 15 18.69 7 5 36.26% 22 20 18.69 7 5 42.98% 23 30 18.69 7 5 58.48% 24 0 18.69 7 10 0.00% 25 5 18.69 7 10 13.51% 26 10 18.69 7 10 23.90% 27 15 18.69 7 10 41.30% 28 20 18.69 7 10 58.18% 29 30 18.69 7 10 84.16% 30 0 18.69 7 15 0.00% 31 5 18.69 7 15 4.86% 32 10 18.69 7 15 10.00% 33 15 18.69 7 15 33.43% 34 20 18.69 7 15 61.14% 35 30 18.69 7 15 78.00% 36 0 24.92 4 5 0.00% 37 5 24.92 4 5 9.46% 38 10 24.92 4 5 23.78% 39 15 24.92 4 5 65.33% 40 20 24.92 4 5 87.68% 41 30 24.92 4 5 95.13% 42 0 24.92 4 10 0.00% 43 5 24.92 4 10 2.17% 44 10 24.92 4 10 16.18% 45 15 24.92 4 10 49.03% 46 20 24.92 4 10 64.73% 47 30 24.92 4 10 91.06% 48 0 24.92 4 15 0.00% 49 5 24.92 4 15 4.96% 50 10 24.92 4 15 39.72% 51 15 24.92 4 15 40.90% 52 20 24.92 4 15 52.01% 53 30 24.92 4 15 86.05% 54 0 24.92 7 5 0.00% 55 5 24.92 7 5 8.75% 56
101
10 24.92 7 5 27.41% 57 15 24.92 7 5 50.73% 58 20 24.92 7 5 72.59% 59 30 24.92 7 5 79.88% 60 0 24.92 7 10 0.00% 61 5 24.92 7 10 3.86% 62 15 24.92 7 10 55.37% 63 20 24.92 7 10 80.99% 64 30 24.92 7 10 88.98% 65 0 24.92 7 15 0.00% 66 5 24.92 7 15 13.46% 67 10 24.92 7 15 30.08% 68 15 24.92 7 15 47.23% 69 20 24.92 7 15 56.99% 70 30 24.92 7 15 84.70% 71 0 31.15 4 5 0.00% 72 5 31.15 4 5 19.22% 73 10 31.15 4 5 49.87% 74 15 31.15 4 5 70.39% 75 20 31.15 4 5 78.70% 76 30 31.15 4 5 94.81% 77 0 31.15 4 10 0.00% 78 5 31.15 4 10 12.68% 79 10 31.15 4 10 34.58% 80 15 31.15 4 10 66.28% 81 20 31.15 4 10 66.86% 82 30 31.15 4 10 82.13% 83 0 31.15 4 15 0.00% 84 5 31.15 4 15 13.16% 85 10 31.15 4 15 34.43% 86 15 31.15 4 15 64.25% 87 20 31.15 4 15 82.02% 88 30 31.15 4 15 94.96% 89 0 31.15 7 5 0.00% 90 5 31.15 7 5 14.85% 91 10 31.15 7 5 40.85% 92 15 31.15 7 5 66.31% 93 20 31.15 7 5 77.19% 94 30 31.15 7 5 91.78% 95 0 31.15 7 10 0.00% 96 5 31.15 7 10 14.53% 97
102
10 31.15 7 10 42.74% 98 15 31.15 7 10 71.51% 99 20 31.15 7 10 84.36% 100 30 31.15 7 10 94.69% 101 0 31.15 7 15 0.00% 102 5 31.15 7 15 14.87% 103 10 31.15 7 15 36.44% 104 15 31.15 7 15 61.81% 105 20 31.15 7 15 68.51% 106 30 31.15 7 15 91.25% 107
Table VII. Color removal at each data point based on the created RSM for Alum.
Term Coef SE Coef T P Constant 0.511082 0.020024 25.523 0 Time 0.469311 0.012237 38.352 0 Current Density 0.0695 0.011414 6.089 0 pH -0.01215 0.008184 -1.485 0.141 Dose -0.01709 0.009982 -1.712 0.09 Time*Time -0.08625 0.019094 -4.517 0 Current Density*Current Density 0.018188 0.018212 0.999 0.321 Dose*Dose -0.00821 0.016952 -0.484 0.629 Time*Current Density 0.026962 0.015282 1.764 0.081 Time*pH -0.01811 0.012045 -1.504 0.136 Time*Dose -0.00176 0.014744 -0.119 0.905 Current Density*pH 0.023936 0.010046 2.383 0.019 Current Density*Dose -0.00762 0.012303 -0.619 0.537 pH*Dose 0.014559 0.009692 1.502 0.136 R2 = 94.42%, R2(adj) = 93.64% Table VIII. Estimation of Regression Coefficients from Minitab Output for Alum.
Using the contour and surface plots from the data from the experiment (Figures XVI-
XXII), indicated that color removal efficiency would be at the highest current density and also treatment time, for the interactions—current density*time, pH*time, and dose*time.
103
However, this trend does not happen within the various interactions—pH and current density, dose*current density, and dose*pH found color removal between 20 and 40%.
Contour Plots of Color Removal
-1 0 1 C urrent Density *Time pH*Time Dose*Time 1.0 %Color Removal 0.5 < 0.0 0.0 – 0.2 0.0 0.2 – 0.4 0.4 – 0.6 -0.5 0.6 – 0.8 > 0.8 -1.0 Hold Values pH*C urrent Density Dose*C urrent Density Dose*pH 1.0 Time 0 Current Density 0 0.5 pH 0 Dose 0 0.0
-0.5
-1.0 -1 0 1 -1 0 1
Figure XVI. Contour Plots of Square Interactions for Color Removal for Alum.
104
Surface Plot of Color Removal vs Current Density, Time
Hold Values pH 0 Dose 0
100.00%
% Color Removal 50.00%
0.5 0.00% 0.0 -0.5 Current Density -1 0 -1.0 T ime 1
Figure XVII. Surface Plot of Color Removal vs Current Density, Time for Alum.
105
Surface Plot of Color Removal vs pH, Time
Hold Values Current Density 0 Dose 0
75.00%
% Color Remova50l .00%
25.00% 1 0.00% 0 pH -1 0 -1 T ime 1
Figure XVIII. Surface Plot of Color Removal vs pH, Time for Alum.
106
Surface Plot of Color Removal vs Dose, Time
Hold Values Current Density 0 pH 0
75.00%
% Color Remova50l .00%
25.00% 1 0.00% 0 Dose -1 0 -1 T ime 1
Figure XVIX. Surface Plot of Color Removal vs Dose, Time for Alum.
107
Surface Plot of Color Removal vs pH, Current Density
Hold Values Time 0 Dose 0
55.00% % Color Removal 50.00%
45.00% 1
40.00% 0 pH -1.0 -0.5 0.0 -1 0.5 Current Density
Figure XX. Surface Plot of Color Removal vs pH, Current Density for Alum.
108
Surface Plot of Color Removal vs Dose, Current Density
Hold Values Time 0 pH 0
60.00%
55.00% % Color Removal
50.00% 1 45.00% 0 Dose -1.0 -0.5 0.0 -1 0.5 Current Density
Figure XXI. Surface Plot of Color Removal vs Dose, Current Density for Alum.
109
Surface Plot of Color Removal vs Dose, pH
Hold Values Time 0 Current Density 0
54.00%
% Color Remova52l .00%
50.00% 1
48.00% 0 Dose -1 0 -1 pH 1
Figure XXII. Surface Plot of Color Removal vs Dose, pH for Alum.
5.4.2.2 Ferric Sulfate (Fe2(SO4) 3) Equation
Table VIII presents the results of the experiment using Fe2(SO4)3. The development of the Fe2(SO4)3 equation, based on coded units is presented below. Similar to the Alum equation, the full-equation [42] will be developed, followed by an equation involving statistically significant values [43]. The coefficients are reflecting in Table IX.
2 Y = 0.49367 + 0.439537x1 + 0.106158x2 + 0.012772x3 – 0.01239x4 – 0.124491x1 +
2 2 0.124128x2 – 0.01931x4 + 0.00756x1x2 + 0.023041x1x3 – 0.00018x1x4 – 0.00316x2x3 –
0.01403x2x4 + 0.045698x3x4 [42]
2 2 Y = 0.49367 + 0.439537x1 + 0.106158x2 – 0.124491x1 + 0.124128x2 + 0.045698x3x4
[43] 110
where
x1 = Time (mins)
2 x2 = Current Density (A/m )
x3 = pH
x4 = Dose (mg/L)
However, unlike what was found in Alum applications, it was determined that the use of
Fe2 (SO4)3 had different significant values. The results found that when applying Fe2
(SO4)3 to treat Acid Yellow 11 at a concentration of 25 mg/L, it can be found that treatment time (p = 0.000), current density (p = 0.000), and the quadratic interactions between pH and dose, current density2, and time2 (p = 0.000). Unlike what was found when Alum, it was discovered that pH (p = 0.177) was not statistically significant. It was also found that many quadratic interactions were also not statistically significant.
111
Current %Color Time Density pH Removal Dose StdOrder 0 18.69 4 0.00% 5 1 5 18.69 4 18.88% 5 2 10 18.69 4 35.97% 5 3 15 18.69 4 47.70% 5 4 20 18.69 4 62.50% 5 5 30 18.69 4 92.60% 5 6 0 18.69 4 0.00% 10 7 5 18.69 4 10.97% 10 8 10 18.69 4 34.20% 10 9 15 18.69 4 61.10% 10 10 20 18.69 4 77.02% 10 11 30 18.69 4 87.99% 10 12 0 18.69 4 0.00% 15 13 5 18.69 4 15.50% 15 14 10 18.69 4 29.24% 15 15 15 18.69 4 46.20% 15 16 20 18.69 4 50.29% 15 17 30 18.69 4 84.80% 15 18 0 18.69 7 0.00% 5 19 5 18.69 7 15.26% 5 20 10 18.69 7 29.21% 5 21 15 18.69 7 40.26% 5 22 20 18.69 7 54.47% 5 23 30 18.69 7 81.58% 5 24 0 18.69 7 0.00% 10 25 5 18.69 7 7.71% 10 26 10 18.69 7 29.71% 10 27 15 18.69 7 58.00% 10 28 20 18.69 7 72.29% 10 29 30 18.69 7 92.00% 10 30 0 18.69 7 0.00% 15 31 5 18.69 7 5.26% 15 32 10 18.69 7 25.15% 15 33 15 18.69 7 59.06% 15 34 20 18.69 7 75.44% 15 35 30 18.69 7 83.33% 15 36 0 24.92 4 0.00% 5 37 5 24.92 4 16.44% 5 38 15 24.92 4 35.31% 5 39
112
20 24.92 4 50.94% 5 40 30 24.92 4 78.44% 5 41 0 24.92 4 0.00% 10 42 5 24.92 4 16.13% 10 43 15 24.92 4 21.09% 10 44 20 24.92 4 44.42% 10 45 30 24.92 4 65.26% 10 46 0 24.92 4 0.00% 15 47 5 24.92 4 11.50% 15 48 15 24.92 4 24.06% 15 49 20 24.92 4 47.59% 15 50 30 24.92 4 58.82% 15 51 0 24.92 7 0.00% 5 52 5 24.92 7 15.26% 5 53 10 24.92 7 29.21% 5 54 15 24.92 7 40.26% 5 55 20 24.92 7 54.47% 5 56 30 24.92 7 81.58% 5 57 0 24.92 7 0.00% 10 58 5 24.92 7 7.71% 10 59 10 24.92 7 29.71% 10 60 15 24.92 7 58.00% 10 61 20 24.92 7 72.29% 10 62 30 24.92 7 92.00% 10 63 0 24.92 7 0.00% 15 64 5 24.92 7 13.37% 15 65 10 24.92 7 31.55% 15 66 15 24.92 7 62.57% 15 67 20 24.92 7 77.54% 15 68 30 24.92 7 84.76% 15 69 0 31.15 4 0.00% 5 70 5 31.15 4 36.24% 5 71 10 31.15 4 77.63% 5 72 15 31.15 4 92.17% 5 73 20 31.15 4 95.75% 5 74 30 31.15 4 98.66% 5 75 0 31.15 4 0.00% 10 76 5 31.15 4 28.57% 10 77 10 31.15 4 54.39% 10 78 15 31.15 4 72.18% 10 79 20 31.15 4 84.46% 10 80
113
30 31.15 4 88.97% 10 81 0 31.15 4 0.00% 15 82 5 31.15 4 17.46% 15 83 10 31.15 4 38.59% 15 84 15 31.15 4 53.52% 15 85 20 31.15 4 77.75% 15 86 30 31.15 4 93.24% 15 87 0 31.15 7 0.00% 5 88 5 31.15 7 9.92% 5 89 10 31.15 7 40.97% 5 90 15 31.15 7 69.97% 5 91 20 31.15 7 79.90% 5 92 30 31.15 7 86.01% 5 93 0 31.15 7 0.00% 10 94 5 31.15 7 24.03% 10 95 10 31.15 7 59.17% 10 96 15 31.15 7 79.33% 10 97 20 31.15 7 81.14% 10 98 30 31.15 7 93.54% 10 99 0 31.15 7 0.00% 15 100 5 31.15 7 34.93% 15 101 10 31.15 7 54.65% 15 102 15 31.15 7 69.58% 15 103 20 31.15 7 86.76% 15 104 30 31.15 7 91.55% 15 105
Table IX. Results from experiments involving Fe2(SO4)3.
114
Term Coef SE Coef T P
Constant 0.49367 0.023048 21.419 0
Time 0.439537 0.01391 31.598 0
Current Density 0.106158 0.013018 8.155 0 pH 0.012772 0.009377 1.362 0.177
Dose -0.01239 0.011472 -1.08 0.283
Time*Time -0.12449 0.02183 -5.703 0
Current Density*Current Density 0.124128 0.021105 5.882 0
Dose*Dose -0.01931 0.019327 -0.999 0.321
Time*Current Density 0.00756 0.017342 0.436 0.664
Time*pH 0.023041 0.013691 1.683 0.096
Time*Dose -0.00018 0.016766 -0.011 0.992
Current Density*pH -0.00316 0.0114 -0.277 0.783
Current Density*Dose -0.01403 0.013962 -1.005 0.317 pH*Dose 0.045698 0.011163 4.094 0
R2 = 92.69%, R2(adj) = 91.65%
Table X. Estimation of Regression Coefficients from Minitab Output for Fe2(SO4)3.
In addition, surface and contour plots were prepared from the developments of the results from the experiment and the input of the data into Minitab (Figures XXIII-XXIX):
115
Contour Plots of Color Removal
-1 0 1 C urrent Density *Time pH*Time Dose*Time 1.0 %Color Removal 0.5 < 0.0 0.0 – 0.2 0.0 0.2 – 0.4 0.4 – 0.6 -0.5 0.6 – 0.8 > 0.8 -1.0 Hold Values pH*C urrent Density Dose*C urrent Density Dose*pH 1.0 Time 0 Current Density 0 0.5 pH 0 Dose 0 0.0
-0.5
-1.0 -1 0 1 -1 0 1
Figure XXIII. Contour Plots of Color Removal for Fe2(SO4)3.
116
Surface Plot of Color Removal vs Current Density, Time
Hold Values pH 0 Dose 0
100.00%
% Color Remova50l .00%
0.5 0.00% 0.0 -0.5 Current Density -1 0 -1.0 T ime 1
Figure XXIV. Surface Plot of Color Removal vs Current Density, Time for
Fe2(SO4)3.
117
Surface Plot of Color Removal vs pH, Time
Hold Values Current Density 0 Dose 0
75.00%
% Color Remova5l0.00%
25.00% 1 0.00% 0 pH -1 0 -1 T ime 1
Figure XXV. Surface Plot of Color Removal vs pH, Time for Fe2(SO4)3.
118
Surface Plot of Color Removal vs Dose, Time
Hold Values Current Density 0 pH 0
75.00%
50.00% % Color Removal 25.00% 1 0.00% 0 Dose -1 0 -1 T ime 1
Figure XXVI. Surface Plot of Color Removal vs Dose, Time for Fe2(SO4)3.
119
Surface Plot of Color Removal vs pH, Current Density
Hold Values Time 0 Dose 0
70.00%
% Color Remova6l0.00%
1 50.00%
0 pH -1.0 -0.5 0.0 -1 0.5 Current Density
Figure XXVII. Surface Plot of Color Removal vs pH, Current Density for Fe2(SO4)3.
120
Surface Plot of Color Removal vs Dose, Current Density
Hold Values Time 0 pH 0
70.00%
% Color Remova6l0.00%
50.00% 1
0 Dose -1.0 -0.5 0.0 -1 0.5 Current Density
Figure XXVIII. Surface Plot of Color Removal vs Dose, Current Density for
Fe2(SO4)3.
121
Surface Plot of Color Removal vs Dose, pH
Hold Values Time 0 Current Density 0
52.00%
48.00% % Color Removal
44.00% 1
40.00% 0 Dose -1 0 -1 pH 1
Figure XXIX. Surface Plot of Color Removal vs Dose, pH for Fe2(SO4)3.
Considering the contour plots for Current Density*Time (Figure XXIV), it was found that the treatment efficiencies were based on the increase of treatment time and also increase in current efficiency, where the majority of the treatment time was between 40 and 60%. For the quadratic interaction, pH*Time, it was found that treatment efficiency was between 60 and 80% for pH below 6, where at the pH of 7 it was found treatment was greater than 80%. For the contour plot, Dose*Time (Figure XXVI), it was found that the color removal efficiency would end up being between 60 and 80% once the treatment time increased, despite the coagulant dose. On the contrary, for pH*Current Density and
Dose*Current Density (Figure XXVIII), it was found that as the current density was greater than 30 A/m2, it was determined that the color removal was between 60 and 80%,
122 while Dose*pH regardless of the pH value or coagulant dose, this interaction concludes that the color removal is between 40 and 60%.
5.4.2.3 Ferric Chloride (FeCl3) Equation
Table XI presents the results of FeCl3. The development of the FeCl3 equation, based on coded data is presented below, including all variables [44] and eliminating those that are not statistically significant [45]. The coefficients are presented in Table XII:
2 Y = 0.537034 + 0.446544x1 + 0.058745x2 – 0.00955x3 – 0.01721x4 – 0.1141x2 –
2 2 0.01367x2 + 0.009823x4 + 0.043167x1x2 – 0.01867x1x3 – 0.01878x1x4 + 0.024727x2x 3–
0.01089x2x4 + 0.002496x3x4 [44]
2 Y = 0.537034 + 0.446544x1 + 0.058745x2 – 0.1141x2 + 0.043167x1x2 +0.024727x2x 3
[45] where
x1 = Time (mins)
2 x2 = Current Density (A/m )
x3 = pH
x4 = Dose (mg/L)
However, when one considers the coefficients within the experiment, it is imperative to consider the p-values that are present within the system. According to the p-values
(where statistically significant can be defined as p<0.05), these results found that when
123 applying FeCl3 to treat Acid Yellow 11 at a concentration of 25 mg/L, it can be found that treatment time (p = 0.000), current (p = 0.000), and the interactions between time and current (p = 0.004), time2 (p = 0.000) and current and pH (p = 0.011). Also, it can be noted that there may be some small statistical significance with pH as its p-value is 0.08.
The table below provides a summary of the estimated regression coefficients for the removal of color by the electrocoagulation experiment:
Current % Color Time Density pH Removal Dose StdOrder 0 18.69 4 0.00% 5 1 5 18.69 4 10.11% 5 2 10 18.69 4 25.28% 5 3 15 18.69 4 62.92% 5 4 20 18.69 4 63.20% 5 5 30 18.69 4 76.12% 5 6 0 18.69 4 0.00% 10 7 5 18.69 4 22.67% 10 8 10 18.69 4 35.20% 10 9 15 18.69 4 49.07% 10 10 20 18.69 4 66.93% 10 11 30 18.69 4 82.67% 10 12 0 18.69 4 0.00% 15 13 5 18.69 4 21.92% 15 14 10 18.69 4 31.51% 15 15 15 18.69 4 51.23% 15 16 20 18.69 4 66.85% 15 17 30 18.69 4 83.29% 15 18 0 18.69 7 0.00% 5 19 5 18.69 7 3.61% 5 20 10 18.69 7 15.96% 5 21 15 18.69 7 37.35% 5 22 20 18.69 7 56.93% 5 23 30 18.69 7 86.75% 5 24 0 18.69 7 0.00% 10 25 5 18.69 7 10.99% 10 26 10 18.69 7 15.38% 10 27 15 18.69 7 18.41% 10 28
124
20 18.69 7 55.22% 10 29 30 18.69 7 76.92% 10 30 0 18.69 7 0.00% 15 31 5 18.69 7 18.90% 15 32 10 18.69 7 29.32% 15 33 15 18.69 7 38.36% 15 34 20 18.69 7 45.75% 15 35 30 18.69 7 61.64% 15 36 0 24.92 4 0.00% 5 37 5 24.92 4 14.46% 5 38 10 24.92 4 26.93% 5 39 15 24.92 4 65.09% 5 40 20 24.92 4 83.54% 5 41 30 24.92 4 85.04% 5 42 0 24.92 4 0.00% 10 43 5 24.92 4 9.16% 10 44 10 24.92 4 24.35% 10 45 15 24.92 4 37.70% 10 46 20 24.92 4 73.30% 10 47 30 24.92 4 79.06% 10 48 0 24.92 4 0.00% 15 49 5 24.92 4 4.68% 15 50 10 24.92 4 36.95% 15 51 15 24.92 4 60.10% 15 52 20 24.92 4 72.17% 15 53 30 24.92 4 78.82% 15 54 0 24.92 7 0.00% 5 55 5 24.92 7 24.54% 5 56 10 24.92 7 42.82% 5 57 15 24.92 7 65.05% 5 58 20 24.92 7 76.16% 5 59 30 24.92 7 87.27% 5 60 0 24.92 7 0.00% 10 61 5 24.92 7 18.14% 10 62 10 24.92 7 37.53% 10 63 15 24.92 7 59.19% 10 64 20 24.92 7 72.54% 10 65 30 24.92 7 82.87% 10 66 0 24.92 7 0.00% 15 67 5 24.92 7 23.15% 15 68 10 24.92 7 35.47% 15 69
125
15 24.92 7 52.96% 15 70 20 24.92 7 60.34% 15 71 30 24.92 7 77.83% 15 72 0 31.15 4 0.00% 5 73 5 31.15 4 17.54% 5 74 10 31.15 4 41.62% 5 75 15 31.15 4 76.44% 5 76 20 31.15 4 86.91% 5 77 30 31.15 4 92.41% 5 78 0 31.15 4 0.00% 10 79 5 31.15 4 12.93% 10 80 10 31.15 4 33.25% 10 81 15 31.15 4 60.16% 10 82 20 31.15 4 85.75% 10 83 30 31.15 4 94.72% 10 84 0 31.15 4 0.00% 15 85 5 31.15 4 4.02% 15 86 10 31.15 4 31.58% 15 87 15 31.15 4 62.23% 15 88 20 31.15 4 80.50% 15 89 30 31.15 4 83.90% 15 90 0 31.15 7 0.00% 5 91 5 31.15 7 24.87% 5 92 10 31.15 7 39.15% 5 93 15 31.15 7 52.65% 5 94 20 31.15 7 67.72% 5 95 30 31.15 7 88.89% 5 96 0 31.15 7 0.00% 10 97 5 31.15 7 24.74% 10 98 10 31.15 7 44.07% 10 99 15 31.15 7 61.86% 10 100 20 31.15 7 76.03% 10 101 30 31.15 7 92.27% 10 102 0 31.15 7 0.00% 15 103 5 31.15 7 17.34% 15 104 10 31.15 7 35.29% 15 105 15 31.15 7 64.40% 15 106 20 31.15 7 74.92% 15 107 30 31.15 7 94.12% 15 108
126
Table XI. Results from experiments involve the addition of FCl3.
Term Coef SE Coef T P
Constant 0.537034 0.018455 29.1 0
Time 0.446544 0.011571 38.592 0
Current Density 0.058745 0.010779 5.45 0 pH -0.00955 0.007711 -1.238 0.219
Dose -0.01721 0.009443 -1.823 0.071
Time*Time -0.1141 0.018006 -6.337 0
Current Density*Current Density -0.01367 0.017048 -0.802 0.425
Dose*Dose 0.009823 0.01588 0.619 0.538
Time*Current Density 0.043167 0.014451 2.987 0.004
Time*pH -0.01867 0.011388 -1.639 0.105
Time*Dose -0.01878 0.013948 -1.346 0.181
Current Density*pH 0.024727 0.009499 2.603 0.011
Current Density*Dose -0.01089 0.011634 -0.936 0.352 pH*Dose 0.002496 0.009168 0.272 0.786
R-Sq = 94.45% R-Sq(adj) = 93.68%
Table XII. Estimation of Regression Coefficients from Minitab Output for FeCl3.
By considering the regression coefficients, it can determined that the results found that there when one applies Ferric Chloride (FeCl3) to an electrocoagulation experiment for the purpose of decolorizing Acid Yellow 11, it can be said that one can consider the
127 treatment time, current density, pH as major factors in the decolorization of the dye. In addition, Minitab predicted the results for each of the values that were collected during the experiment, comparing the values to what was collected from the experiment.
Finally, what is unique to Minitab is ability to is provide a visual contour plot that best represents the results that were developed from the experiment (Figure XXX). There are six different contour plots that would be prepared, based on the major interactions—
Current Density*Time, pH*Time, Dose*Time, pH*Current Density, Dose*Current
Density, and Dose*pH, where squared interactions (for example, time*time), contour plots would not be used for this experiment:
Contour Plots of Color Removal
-1 0 1 C urrent Density *Time pH*Time Dose*Time 1.0 % Color Removal 0.5 < 0.0 0.0 – 0.2 0.0 0.2 – 0.4 0.4 – 0.6 -0.5 0.6 – 0.8 > 0.8 -1.0 Hold Values pH*C urrent Density Dose*C urrent Density Dose*pH 1.0 Time 0 Current Density 0 0.5 pH 0 Dose 0 0.0
-0.5
-1.0 -1 0 1 -1 0 1
Figure XXX. Contour Plots of Color Removal for FeCl3.
128
Surface Plot of Color Removal vs Current Density, Time
Hold Values pH 0 Dose 0
100.00%
% Color Remova50l .00%
0.5 0.00% 0.0 -0.5 Current Density -1 0 -1.0 T ime 1
Figure XXXI. Surface Plot of Color Removal vs Current Density, Time for FeCl3.
129
Surface Plot of Color Removal vs pH, Time
Hold Values Current Density 0 Dose 0
75.00%
% Color Remova50l .00%
25.00% 1
0.00% 0 pH -1 0 -1 T ime 1
Figure XXXII. Surface Plot of Color Removal vs pH, Time for FeCl3.
130
Surface Plot of Color Removal vs Dose, Time
Hold Values Current Density 0 pH 0
75.00%
% Color Remova50l .00%
25.00% 1
0.00% 0 Dose -1 0 -1 T ime 1
Figure XXXIII. Surface Plot of Color Removal vs pH, Time for FeCl3.
131
Surface Plot of Color Removal vs pH, Current Density
Hold Values Time 0 Dose 0
55.00% % Color Removal 50.00%
45.00% 1
40.00% 0 pH -1.0 -0.5 0.0 -1 0.5 Current Density
Figure XXXIV. Surface Plot of Color Removal vs pH, Current Density for FeCl3.
132
Surface Plot of Color Removal vs Dose, Current Density
Hold Values Time 0 pH 0
60.00%
% Color Remova5l5.00%
50.00% 1
45.00% 0 Dose -1.0 -0.5 0.0 -1 0.5 Current Density
Figure XXXV. Surface Plot of Color Removal vs Dose, Current Density for FeCl3.
133
Surface Plot of Color Removal vs Dose, pH
Hold Values Time 0 Current Density 0
58.00%
56.00% % Color Removal
54.00% 1
52.00% 0 Dose -1 0 -1 pH 1
Figure XXXVI. Surface Plot of Color Removal vs Dose, pH for FeCl3.
From this Figure XXXI, one can see how that color removal Current Density*Time indicate the color removal (>80%) occurs as the treatment time increases, along with the current density. This also happens when treatment time increases regardless of the pH.
This also was found in the quadratic interaction between the dose and also the treatment time.
On the contrary, pH*Current Density indicates that color removal remains between 40 and 60% regardless of the pH and Current Density, along with the interactions between
Dose and pH. However, in the other contour plots, it is found that the quadratic interaction between Dose and Current Density, where the majority of the color removal efficiency is between 40 and 60% until approximately 30 A/m2, where it indicates the
134 coagulant dose of around 5 mg/L. This can also be seen by the surface plot prepared from the data below, which provides a three-dimensional image that can be found from the experiments.
5.4.2.4 SUMMARY
It can be seen that all three applications of coagulant are found statistically significant due to time and current density, where it is determined that a high current density and a long retention time produce the best results. In addition, it has been determined that pH has some significance with alum (p = 0.0557) and FeCl3 (p = 0.089). However, the interaction of Fe2(SO4)3 with pH was not statistically significant (p = 0.156). For quadratic interactions are the squared interactions of time are time2 for all three coagulants, while for Fe2(SO4)3 current density interactions. For the remaining quadratic interactions, it was determined that all three coagulants were Current Density*pH had statistical significance, while pH and dose (Fe2(SO4)3) and time*current density FeCl3.
However, as thought, the amount of coagulant dose appears to not have a major impact on the treatment efficiency.
Results from the experiment resonate with other results from other places in literature using the response surface methodology (RSM). Prasad et al. (373) and Prasad (376) treated distillery wastewater using copper anodes and found that high current density and long retention time.
5.4.3 BOX BEHNKEN EXPERIMENT
The following data will be used in preparation for publication (377).
135
5.4.3.1 DESIGN DETAILS
An additional experiment was conducted for the purpose of determining the treatment efficiency would be explained across three dye concentrations by weight (20 mg, 33 mg, and 45 mg), pH (4, 7, and 9), current densities (19.38, 25.83, and 31.15 A/m3) coagulant
(Alum, Fe2(SO4)3, and FeCl3), coagulant doses (5, 10, and 15 mg/L), and three types of dye (Acid Yellow 11, Acid Orange 7, and Naphthol Green B). Data was implemented into Minitab for the purpose of developing equations based on the information collected from the experiment.
5.4.3.2 DESIGN CHARACTERISTICS
Table XIII describes the coding for the experiment:
Coding -1 0 1 Dye Acid Yellow 11 Acid Orange 7 Naphthol Green B pH 4 7 9 Dye weight (mg) 20 33 45 Current Density 18.69 24.92 31.15 (A/m2) Coagulant Alum Ferric Sulfate Ferric Chloride Coagulant dose (mg/L) 5 10 15 Table XIII. Coding for Box Behnken experiments.
As indicated within the table, color removal was contingent on the type of dye, pH, weight of dye, current density, coagulant, and the coagulant dose.
5.4.3.3 RESULTS
136
First, it is important to mention that electrocoagulation treatment was capable of treating various dyes at different current densities, pH, and coagulant type and doses.
With a maximum treatment time of 30 minutes, it was found that color removal ranged from 61.75-99.42%. Korbahti et al. found decolorization of textile dyes using electrochemical treatment technologies between 70.6-99.47% for treating removing Acid
Blue 29, Reactive Blue 4, Acid Red 97, and Reactive Red 2 (378). Specifically for the dyes treated within this experiment, there are several papers on the decolorization of Acid
Yellow 11, Acid Orange 7, and Naphthol Green B using other treatment technologies.
Acid Orange 7 has been decolorized by means of the use of Fenton-biological treatment at an optimum pH of 7 (379), granulated activated carbon (GAC)-biofilm sequencing batch reactor (380), wet oxidation (381), membrane aerated biological reactor with Shewanella sp., and horseradish perxoidase (382). Within this RSM experiment,
Acid Orange 7 was decolorized between 78.37-98.82% in only 30 minutes retention time.
This is very comparable to what was seen in literature. In many cases Acid Orange 7 has been near or completely decolorized, but at the expense of long retention times— 6 hours
(342), 2 hours (344), and even 50 hours (380).
For Naphthol Green B, it was found that using antimony trisulfide as a semiconductor could reduce Naphthol Green B in 60 minutes by 96.5% (383). In addition, complete removal of Naphthol Green B was concluded by 10% Al zinc oxide
(ZnO) in sunlight exposure at a treatment time of 6 hours (384). Metal Hydroxides
Sludge (MHS) for adsorption has been used to treat Naphthol Green B, where it was determined that 52% was removed at a pH of 6 (385). By using electrocoagulation, one is capable of having high treatment efficiency with a shorter retention time.
137
Another important consideration made is that it has been determined that the experimental error is only (2.5%) when considering the center point replicates. This value is very comparative of what is in found in literature, where Aleboyeh et al. found the experiment error was (2.18%) by comparing the color removal of C.I. Acid Red 14 using electrocoagulation (173), Alinsafi et al. calculated a 2.8% experimental error when treating blue reactive dye using electrocoagulation (386), and Bhatti et al. determined a
3.02% experimental error. These values indicate that the results from the experiment are precise (186).
Following data entry into Minitab, the data was analyzed for the purpose of determining its ability to fit a linear regression by using Minitab’s analysis of Response
Surface Methodologies. From the residual plots, it that the model was not normal as determined by the Anderson-Darling test (p<0.005). Considering the normal probability plot, it was found that the points did not follow a linear model. In addition, the residuals versus fits graph indicated that the majority of the data points were scattered on the right hand side of the graph. While it was found that the residuals were not primarily positive or negatively correlated, the non-normality of the data indicated that the data required a transformation for the purpose of fitting a linear regression.
Therefore, it would require the data to be transformed. Minitab provides two methods of transformation—Box-Cox Transformation and Johnson Transformation. Data was first analyzed using both Box-Cox Transformation, using λ = 0 (ln(color removal)) and λ = 0.5 (square root of color removal). In addition, Minitab provides an option that the user can determine the optimum λ that best fits the graph (387). In this case λ = 9.
138
However, Box-Cox Transformation did not provide as strong of a linear fit as the
Johnson Transformation.
Using Johnson Transformation, the user is provided the probability of the original and transformed data showing the Anderson-Darling test. By transforming the data using the Johnson Transformation, the data successfully fit the conditions for the Anderson-
Darling test (p=0.303) (Figure XXXVII).
Johnson Transformation for % Color Removal
Probability Plot for Original Data Select a T ransformation 99 0.5
N 53 t s
AD 5.058 e
t 0.4
90 P-Value <0.005 D
A 0.3
t
r
n
o
e f
0.2
c 50
e
r
u
e l
P 0.1 a
V Ref P -
10 P 0.0 0.4 0.6 0.8 1.0 1.2 1 Z Value 60 80 100 120 (P-Value = 0.005 means <= 0.005)
Probability Plot for T ransformed Data 99 N 53 AD 0.357 P-V alue for Best F it: 0.443391 90 P-Value 0.443 Z for Best F it: 0.5
t Best Transformation Ty pe: SU n
e Transformation function equals
c 50 r
e 1.36301 + 0.656222 * A sinh( ( X - 98.4463 ) / 0.744691 ) P
10
1 -2 0 2
Figure XXXVII. Johnson Transformation for % Color Removal (Probability Plot for Original Data vs Probability Plot for Transformed Data). Anderson-Darling values and p-values (p-values <0.005 = data is not normal).
In addition to providing the probability plot, Minitab provides the transformation function
[46]:
xn = 1.36301 + 0.656222* Asinh((Xn-98.4463)/0.744691) [46] where 139
Xn = the original coefficient of the factor
xn = transformed coefficient
When analyzing the Johnson transformed data, the data fits the linear model as shown in the Regression Coefficients and the Analysis of Variance (ANOVA) tables (Table XIV).
Also, the residuals versus fits graph shows that the data provided a better scattering of data points as compared to the original presentation of the data.
Source DF Seq SS Adj SS Adj MS F P Regression 27 33.1003 33.1003 1.2259 3.19 0.002 Linear 6 23.7094 21.4978 3.583 9.34 0 Dye 1 10.7136 11.1966 11.1966 29.18 0 pH 1 0.3766 0.5229 0.5229 1.36 0.254 Dye weight 1 0.7676 0.7676 0.7676 2 0.17 Current Density 1 10.9501 10.3111 10.3111 26.87 0 Coagulant 1 0.0277 0.0277 0.0277 0.07 0.79 Coagulant Dose 1 0.8738 0.8738 0.8738 2.28 0.144 Square 6 2.102 2.0131 0.3355 0.87 0.528 Dye*Dye 1 0.7399 0.7373 0.7373 1.92 0.178 pH*pH 1 0.0959 0.3536 0.3536 0.92 0.346 Dye weight*Dye weight 1 0.1065 0.0026 0.0026 0.01 0.935 Current Density*Current Density 1 0.1746 0.2496 0.2496 0.65 0.428 Coagulant*Coagulant 1 0.08 0.0123 0.0123 0.03 0.859 Coagulant Dose*Coagulant Dose 1 0.905 0.9151 0.9151 2.38 0.135 Interaction 15 7.2889 7.2889 0.4859 1.27 0.292 Dye*pH 1 0.1752 0.1446 0.1446 0.38 0.545 Dye*Dye weight 1 0.0057 0.0057 0.0057 0.01 0.904 Dye*Current Density 1 1.4451 1.0998 1.0998 2.87 0.103 Dye*Coagulant 1 0.2816 0.2816 0.2816 0.73 0.4 Dye*Coagulant Dose 1 0.0159 0.0159 0.0159 0.04 0.841 pH*Dye weight 1 1.0842 1.0842 1.0842 2.83 0.105 pH*Current Density 1 0.6717 0.6717 0.6717 1.75 0.198 pH*Coagulant 1 0.1018 0.1018 0.1018 0.27 0.611 pH*Coagulant Dose 1 0.7584 0.7584 0.7584 1.98 0.172 Dye weight*Current Density 1 0.149 0.149 0.149 0.39 0.539
140
Dye weight*Coagulant 1 0.0276 0.0276 0.0276 0.07 0.791 Dye weight*Coagulant Dose 1 0.0029 0.0029 0.0029 0.01 0.932 Coagulant*Coagulant 1 1.0598 1.0598 1.0598 2.76 0.537 Residual Error 25 9.5931 9.5931 0.3837 0.071 Lack-of-Fit 20 8.2978 8.2978 0.4149 1.6 0.109 Pure Error 5 1.2953 1.2953 0.2591 Total 52 42.6934 0.317 R2 = 78.05%, R2 (adj) = 54.35% (p<0.1 = statistically significant)
Table XIV. Analysis of Variance (ANOVA) of the Response Surface Methodology.
It is important to consider the analysis of variance (ANOVA) table produced by
Minitab, specifically the Fisher’s test value (F-value) and the p-value. The F-value relates the data to the specific model analyzed and how it can explain response variability, while the p-value scales the F-value for statistical significance (388). Considering the ANOVA, it has been determined from the p-values that the regression (p =0.002) and linear (p =
0.000) models are both statistically significant (p= 0.000). The F-values were large enough to explain that the data has variability due to it being linear (F = 9.34) and a regression (F = 3.19) model. While the original data produced has similar p-values for both regression and linear, it did not pass the Anderson-Darling test for being normal.
There are also no squared interactions that are statistically significant. One thing also to point out is that the lack of fit is not statistically significant, as opposed to the original data which led to the need for a transformation (387). Finally, the F-test for dye type (F =
29.18), current density (F = 26.87) as parameters that have the most significance.
141
Term Coef SE Coef T P Constant 0.41292 0.2529 1.633 0.115 Dye 0.71481 0.1323 5.402 0 pH 0.15448 0.1323 1.167 0.254 Dye weight -0.17884 0.1264 -1.414 0.17 Current Density 0.68596 0.1323 5.184 0 Coagulant -0.03397 0.1264 -0.269 0.79 Coagulant Dose 0.19081 0.1264 1.509 0.144 Dye*Dye -0.27015 0.1949 -1.386 0.178 pH*pH -0.19565 0.2038 -0.96 0.346 Dye weight*Dye weight -0.01587 0.1936 -0.082 0.935 Current Density*Current Density -0.1572 0.1949 -0.807 0.428 Coagulant*Coagulant 0.03586 0.2 0.179 0.859 Coagulant Dose*Coagulant Dose -0.29895 0.1936 -1.544 0.135 Dye*pH 0.15245 0.2483 0.614 0.545 Dye*Dye weight -0.02674 0.219 -0.122 0.904 Dye*Current Density 0.28028 0.1656 1.693 0.103 Dye*Coagulant -0.18761 0.219 -0.857 0.4 Dye*Coagulant Dose -0.04451 0.219 -0.203 0.841 pH*Dye weight -0.36814 0.219 -1.681 0.105 pH*Current Density -0.32856 0.2483 -1.323 0.198 pH*Coagulant 0.07975 0.1549 0.515 0.611 pH*Coagulant Dose -0.30791 0.219 -1.406 0.172 Dye weight*Current Density 0.13647 0.219 0.623 0.539 Dye weight*Coagulant -0.05874 0.219 -0.268 0.791 Dye weight*Coagulant Dose 0.01344 0.1549 0.087 0.932 Current Density*Coagulant 0.13714 0.219 0.626 0.537 Current Density*Coagulant -0.41224 0.219 -1.882 0.071 142
Dose Coagulant*Coagulant Dose 0.36396 0.219 1.662 0.109 R2 = 77.53%, R2 (adj) = 53.26% (p<0.1 = statistically significant)
Table XV. Estimation of Regression Coefficients from Minitab Output.
From the regression output (Table XV), one can determine the significant interaction by considering the values where (p <0.1). It is understood that we can use
0.001, 0.05, 0.1 (389). From this experiment, it was determined that the regression coefficients that had the most statistical significance were dye (p = 0), current density (p
= 0), and current density (p = 0), and Current Density*Coagulant Dose (p = 0.071).
Gurses et al. concluded that the type of dye was one of the more important parameters that is statistically significant (363). From the regression coefficient table, one can develop two equations—first, an equation based on all coefficients [47], and second, based on coefficients that are statistically significant [48]. These equations are based on uncoded units (or original values):
y = 0.41292 + 0.71481x1 + 0.15448x2 – 0.17884x3 + 0.68596x4 – 0.03397x5 +
2 2 2 2 2 0.19081x6 – 0.27015x1 – 0.19565x2 + 0.01587x3 – 0.15720x4 + 0.03586x5 –
0.29895x6 – 0.15245x1x2 – 0.02674x1x3 + 0.28028x1x4 – 0.18761x1x5 – 0.04451x1x6 –
0.36814x2x3 – 0.32856x2x4 + 0.07975x2x5 – 0.30791x2x6 + 0.13647x3x4 – 0.05874x3x5 +
0.01344x3x6 +0.13714x4x5 – 0.41224x4x6 + 0.36396x6x6 [47]
where
x1 = dye
x2 = pH
x3 = dye weight (mg)
2 x4 = current density (A/m )
143
x5 = coagulant
x6 = coagulant dose (mg/L)
y = 0.71481x1 + 0.6856x4-0.41224x4x6 [48]
For this regression model, the r2 = 77.53%, states that 22.47% of the data is not explained by the model (363). The most probable reason for such a lower r2 could be attributed a missing variable (such as time).
Following ANOVA analysis and consideration of the regression equations, one can analyze the contour and surface plots that can be created by means of using Minitab software (Figures XXXVIII- LIV).
Contour Plot for Color Removal (Johnson Transformation)
pH*Dye Dye weight*Dye Current Density*Dye Coagulant*Dye 1 1 1 1 Color Removal 0 0 0 0 < -1.0 -1.0 – -0.5 -1 -1 -1 -1 -0.5 – 0.0 -1 0 1 -1 0 1 -1 0 1 -1 0 1 0.0 – 0.5 1 Coagulant Dose*Dye 1 Dye weight*pH 1 Current Density*pH 1 Coagulant*pH 0.5 – 1.0 1.0 – 1.5 0 0 0 0 > 1.5
-1 -1 -1 -1 Hold Values -1 0 1 -1 0 1 -1 0 1 -1 0 1 Dye 0 Coagulant Dose*pH Current Density*Dye weight Coagulant*Dye weight Coagulant Dose*Dye weight 1 1 1 1 pH 0 Dye weight 0 0 0 0 0 Current Density 0 Coagulant 0 -1 -1 -1 -1 Coagulant Dose 0 -1 0 1 -1 0 1 -1 0 1 -1 0 1
1 Coagulant*Current Density 1 Coagulant Dose*Current Density 1 Coagulant Dose*Coagulant
0 0 0
-1 -1 -1 -1 0 1 -1 0 1 -1 0 1
Figure XXXVIII. Contour Plots of Color Removal (Johnson Transformation).
144
Surface Plots for Color Removal (Johnson Transformation) vs pH, Dye
Hold Values Dye weight 0 Current Density 0 Coagulant 0 Coagulant Dose 0
1.0
0.5 Color Removal 0.0 1 -0.5
0 pH -1 0 -1 Dye 1
Figure XXXIX. Surface Plot of Color Removal (Johnson Transformation) vs pH, Dye.
145
Surface Plots for Color Removal (Johnson Transformation) vs Current Density, Dye
Hold Values pH 0 Dye weight 0 Coagulant 0 Coagulant Dose 0
2
1 Color Removal 0 1
-1 0 Current Density -1 0 -1 Dye 1
Figure XL. Surface Plot of Color Removal (Johnson Transformation) vs Current Density, Dye.
146
Surface Plots for Color Removal (Johnson Transformation) vs Coagulant, Dye
Hold Values pH 0 Dye weight 0 Current Density 0 Coagulant Dose 0
1.0
0.5 Color Removal
0.0 1 -0.5 0 Coagulant -1 0 -1 Dye 1
Figure XLI. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, Dye.
147
Surface Plots for Color Removal (Johnson Transformation) vs Coagulant Dose, Dye
Hold Values pH 0 Dye weight 0 Current Density 0 Coagulant 0
0.5
Color Removal 0.0
-0.5 1 -1.0 0 Coagulant Dose -1 0 -1 Dye 1
Figure XLII. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Dye.
148
Surface Plots for Color Removal (Johnson Transformation) vs Dye weight, pH
Hold Values Dye 0 Current Density 0 Coagulant 0 Coagulant Dose 0
1.0
Color Removal 0.5
1 0.0 0 Dye weight -1 0 -1 pH 1
Figure XLIII. Surface Plot of Color Removal (Johnson Transformation) vs Dye weight, pH.
149
Surface Plots for Color Removal (Johnson Transformation) vs Current Density, pH
Hold Values Dye 0 Dye weight 0 Coagulant 0 Coagulant Dose 0
1
Color Removal 0
1 -1 0 Current Density -1 0 -1 pH 1
Figure XLIV. Surface Plot of Color Removal (Johnson Transformation) vs Dye weight, pH.
150
Surface Plots for Color Removal (Johnson Transformation)
Hold Values Dye 0 Dye weight 0 Current Density 0 Coagulant Dose 0
0.45
Color Removal 0.30
0.15 1
0.00 0 Coagulant -1 0 -1 pH 1
Figure XLV. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, pH.
151
Surface Plots for Color Removal (Johnson Transformation)
Hold Values Dye 0 Dye weight 0 Current Density 0 Coagulant 0
0.5
Color Removal 0.0
1 -0.5
0 Coagulant Dose -1 0 -1 pH 1
Figure XLVI. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, pH.
152
Surface Plots for Color Removal (Johnson Transformation) vs Current Density, Dye weight
Hold Values Dye 0 pH 0 Coagulant 0 Coagulant Dose 0
1.0
0.5 Color Removal 0.0 1 -0.5 0 Current Density -1 0 -1 1 Dye weight
Figure XLVII. Surface Plot of Color Removal (Johnson Transformation) vs Current Density, Dye Weight.
153
Surface Plots for Color Removal (Johnson Transformation) vs Coagulant, Dye weight
Hold Values Dye 0 pH 0 Current Density 0 Coagulant Dose 0
0.6
Color Removal 0.4
1 0.2 0 Coagulant -1 0 -1 1 Dye weight
Figure XLVIII. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, Dye Weight.
154
Surface Plots for Color Removal (Johnson Transformation) vs Coagulant Dose, Coagulant
Hold Values Dye 0 pH 0 Dye weight 0 Current Density 0
0.5
Color Removal 0.0 1
-0.5 0 Coagulant Dose -1 0 -1 1 Coagulant
Figure XLIX. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Coagulant.
155
Surface Plots for Color Removal (Johnson Transformation) vs Coagulant Dose, Current Density
Hold Values Dye 0 pH 0 Dye weight 0 Coagulant 0
1
Color Removal 0
1 -1 0 Coagulant Dose -1 0 -1 1 Current Density
Figure L. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Current Density.
156
Surface Plots for Color Removal (Johnson Transformation) vs Coagulant, Current Density
Hold Values Dye 0 pH 0 Dye weight 0 Coagulant Dose 0
1.0
Color Removal 0.5
0.0 1
-0.5 0 Coagulant -1 0 -1 1 Current Density
Figure LI. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant, Current Density.
157
Surface Plots for Color Removal (Johnson Transformation) vs Coagulant Dose, Dye weight
Hold Values Dye 0 pH 0 Current Density 0 Coagulant 0
0.6
0.3 Color Removal
0.0 1
-0.3 0 Coagulant Dose -1 0 -1 1 Dye weight
Figure LII. Surface Plot of Color Removal (Johnson Transformation) vs Coagulant Dose, Dye Weight.
158
Surface Plots for Color Removal (Johnson Transformation) vs Dye weight, Dye
Hold Values pH 0 Current Density 0 Coagulant 0 Coagulant Dose 0
1.0
0.5 Color Removal 0.0 1 -0.5 0 Dye weight -1 0 -1 Dye 1
Figure LIII. Surface Plot of Color Removal (Johnson Transformation) vs Dye weight, Dye.
159
StdOrder RunOrder PtType Blocks Dye pH Dye weight Cur. Den Coagulant Coagulant Color Dose 10 1 2 1 0 1 -1 0 -1 0 96.38 14 2 2 1 0 1 -1 0 1 0 98.82 51 3 0 1 0 0 0 0 0 0 97.62 34 4 2 1 0 1 0 0 -1 -1 98.2 38 5 2 1 0 1 0 0 -1 1 95.46 26 6 2 1 1 0 0 -1 -1 0 97.35 48 7 2 1 1 0 1 0 0 1 97.36 50 8 0 1 0 0 0 0 0 0 96.81 2 9 2 1 1 -1 0 -1 0 0 64.87 54 10 0 1 0 0 0 0 0 0 96.9 43 11 2 1 -1 0 1 0 0 -1 92.71 42 12 2 1 1 0 -1 0 0 -1 97.52 16 13 2 1 0 1 1 0 1 0 92.88 23 14 2 1 0 0 -1 1 0 1 95.78 9 15 2 1 0 -1 -1 0 -1 0 97.82 47 16 2 1 -1 0 1 0 0 1 88.32 19 17 2 1 0 0 -1 1 0 -1 97.76 40 18 2 1 0 1 0 0 1 1 97.42 4 19 2 1 1 1 0 -1 0 0 96.67 46 20 2 1 1 0 -1 0 0 1 95.43 31 21 2 1 -1 0 0 1 1 0 93.65 44 22 2 1 1 0 1 0 0 -1 96.31 24 23 2 1 0 0 1 1 0 1 97.14 8 24 2 1 1 1 0 1 0 0 *, N/A 11 24 2 1 0 -1 1 0 -1 0 97.1 15 25 2 1 0 -1 1 0 1 0 95.74 1 26 2 1 -1 -1 0 -1 0 0 68.24 18 27 2 1 0 0 1 -1 0 -1 78.37 22 28 2 1 0 0 1 -1 0 1 92.93 13 29 2 1 0 -1 -1 0 1 0 95.91 52 30 0 1 0 0 0 0 0 0 91.59 36 31 2 1 0 1 0 0 1 -1 88.54 32 32 2 1 1 0 0 1 1 0 99.31 3 33 2 1 -1 1 0 -1 0 0 87.05 25 34 2 1 -1 0 0 -1 -1 0 61.75 33 35 2 1 0 -1 0 0 -1 -1 82.3 28 36 2 1 1 0 0 1 -1 0 99.42 49 37 0 1 0 0 0 0 0 0 97.76
160
6 38 2 1 1 -1 0 1 0 0 98.62 21 39 2 1 0 0 -1 -1 0 1 97.27 17 40 2 1 0 0 -1 -1 0 -1 80.46 41 41 2 1 -1 0 -1 0 0 -1 84.68 5 42 2 1 -1 -1 0 1 0 0 92.4 37 43 2 1 0 -1 0 0 -1 1 95.87 45 44 2 1 -1 0 -1 0 0 1 91.27 7 45 2 1 -1 1 0 1 0 0 92.19 39 46 2 1 0 -1 0 0 1 1 97.39 29 47 2 1 -1 0 0 -1 1 0 68.84 53 48 0 1 0 0 0 0 0 0 97.59 30 49 2 1 1 0 0 -1 1 0 90.16 12 50 2 1 0 1 1 0 -1 0 83.5 35 51 2 1 0 -1 0 0 1 -1 86.47 27 52 2 1 -1 0 0 1 -1 0 93.38 20 53 2 1 0 0 1 1 0 -1 97.04
*Incorrect Current Density Recorded.
Table XVI. Summary results of the experiments.
Considering Naphthol Green B, the dye that was found to report the highest treatment removal, it appears that the best conditions based on the contour plots would be having a low dye weight of 20 mg (dye weight vs dye), a current density of 31.15 A/m2
(current density vs dye), Alum (coagulant vs dye). When it comes to determining the optimum coagulant, Ferric Chloride had the highest removal also at the highest current density (31.15 A/m2), along with having the highest dose (15 mg/L). From the Box
Behnken summary table (Table VII), the coagulant dose is 5 mg/L and pH = 9 (FeCl3 = 5 mg/L, pH = 9, Color removal = 98.2%). Beyond contour and surface plots, one can consider main effects plots (Figure LIV) as well as another to consider the effects of the various parameters on the treatment efficiency of the various dyes.
161
Main Effects Plot for Color Removal Data Means
Dye pH Dye weight 0.5
0.0
-0.5
n -1.0 a
e -1 0 1 -1 0 1 -1 0 1
M Current Density Coagulant Coagulant Dose 0.5
0.0
-0.5
-1.0 -1 0 1 -1 0 1 -1 0 1
Figure LIV. Main Effect Plot for Color Removal.
Overall, it appears that dye type, current density, and coagulant dose appear to indicate an increase in treatment efficiency, where it is further confirmed that Naphthol
Green B had the highest removal of all of the three dyes, along with Acid Yellow 11 being the lowest. The plot shows that color removal increases as the current density shifts from 18.69 A/m2 to 31.15 A/m2. Prasad et al. found that 31 A/m2 was the optimum current density for current removal (373), coinciding with the results from this experiment. Added coagulant concentration moves from 10 mg/L to 15 mg/L has a slight change in terms of its ability to decolorize the wastewater, while increasing the weight of dye certainly decreases the potential of removing color. Two factors that display no particular pattern are coagulant type and pH.
162
Many in literature have used response surface methodology to analyze their data.
Our results found a conclusion similar to Bhatti et al. where the amount of electricity
(voltage was measured by these authors) and time as the most significant parameters from response surface methodology (387). While treatment time was not a parameter within this experiment, it can be stated that the operation time would be significant, simply because of the fact that as treatment time increases, the color removal increases.
Zaroual et al. found pH, potential time, and temperature as important factors (365), and the initial pH has been found for the removal of Acid Black 172 (367). Unlike the previous authors, the initial pH was not shown as a statistical significant value, along with the treatment time. However, the pH was suitable for high color removal and agreed with the optimum pH range (4-9) for electrocoagulation using aluminum electrodes
(386). Nevertheless, current density has been the most prevalent factor within electrocoagulation treatment (364, 386). This is because the current density controls not only the amount of coagulant produced, but also the amount of hydrogen gas bubbles that are formed to allow for the wastewater to aggregate at the top and separate (364).
5.4.3.4 SUMMARY
The results from this experiment considered the use of varying weights of three different dyes, pH, and adding additional coagulant at three different doses. It was found that the most statistically significant values from the experiment included current density and dye as having the most impact on the treatment performance, while the interaction between pH and current density making the most impact. Literature has supported this viewpoint, as many authors have found current density as a major influence on the treatment
163 performance, along with the type of dye when comparing different types of dyes. Finally, the moderate size r2 value (72.88%) can be attributed to a possible variable that has not been considered, such as treatment time, to further explain the model.
164
CHAPTER 6
ELECTROCOAGULATION RESULTS
6.1 RESULTS
In Chapters 5 and 7, the results for Response Surface Methodology (RSM) and photocatalysis coincided with these chapters. In this section, results from the electrocoagulation experiment for treatment of Acid Yellow 11 only will be discussed.
Two results will be considered—first the results comparing the effects of treatment performance as it is related to current density, pH, and coagulant type and dose, and second the kinetic results.
6.1.1 REPEATABILITY EXPERIMENTS
However, it is important to consider the effects of the reactor on the repeatability of the experiments. It can be confirmed from our response surface methodology that our results have very little experimental error. Experimental error was calculated based on results determined by (390). Prior to the RSM, an experiment was conducted to determine 165 whether or not electrodes would have an effect on the treatment performance of the reactor. The experiments compared not only the use of new and spent electrodes, but also the effects of additional coagulant and a major change in current. The experiment considered two types of electrodes—virgin and spent electrodes that had been recently used at a current density of 31.19 A/m2 . Two treatments were applied—one without coagulant and one with coagulant (Fe2(SO4)3). Figure LVI concluded that the experimental error between the final and initial color removal was 10.5% for those without coagulant, and 0.82% for the ferric sulfate addition. In regards to concentration, experimental error was 16.44% for no coagulant and 5.38% for ferric sulfate. Averaging the experimental error for both parameters, the experimental error was 5.66% for color removal and 10.91% for concentration. These values indicate our experimental error is ok and that are results are repeatable.
For photo-oxidation, the two reactors were compared to consider their reaction efficiency. Using 0.25 g/L of Co-TiO2 and a dye wastewater concentration of 30 mg/L of
Acid Orange 7, it was found that the treatment efficiency for the reactors had an experimental error of 3.59% when comparing the two concentrations and 0.033% when comparing percent removal of wastewater. Despite the different dimensions, it appears that the reactors can be used for both experiments and generate the same results.
166
30
25 Control (spent)
20 Ferric Sulfate, spent 15 Control (virgin) 10
Concentration (mg/L) Concentration Ferric Sulfate 5 (virgin)
0 0 5 10 15 20 30 Time (mins)
Figure LV. Repeated experiments comparing spent and virgin electrodes (Control
(spent) = 3 A after 5 A; Control (virgin); Coagulant (Fe2(SO4)3 (spent) = 3 A after 5
A; Coagulant (Fe2(SO4)3 (virgin)).
1.2
1
0.8
0.6
C/Co Co-doped (Reactor 2) 0.4 Co-doped (Reactor 1)
0.2
0 0 30 60 119 180 Time (mins)
Figure LVI. Repeated experiments comparing Reactors 1 and 2 for photo-oxidation experiments.
167
6.1.2 ELECTROCOAGULATION RESULTS
6.1.2.1 Affect of current density and coagulant
6.1.2.1.1 Affects of Alum Addition
Figures LVII- LXXXIX summarized all of the data from the runs, with Figure LXXXIX pertaining to comparing results across different added coagulants. Specific figures produced to determine the effects of the final concentration (mg/L) and the current density based on each coagulant and corresponding doses. This comparison was done for both pH of 4 and 7, along with considering each coagulant and coagulant dose. For Alum
(Figures LVII-LXVII), it was found that at a pH of 4, final concentration did not indicate a decreasing pattern with increasing current density, as it was shown that at an Alum concentration of 10 mg/L, the final concentration was found at 24.92 A/m2. This pattern was shown at an Alum concentration of 5 mg/L. However, at the Alum concentration of
15 mg/L, the lowest final concentration was at a current density of 31.15 A/m2.
Nevertheless, the lowest additive concentration of Alum (5 mg/L) produced the best treatment efficiency at the pH of 4. This is certainly due to the effects of pH on the performance of electrocoagulation. It can be argued, although microscopy analysis could better confirm this argument, that the addition of coagulant improve the treatment performance of Acid Yellow 11. While at a pH of 7, there was more of a consistent pattern with increasing current density, as the higher current density produced better treatment efficiency, the treatment performance was better than at a pH of 7.
The pH appears to have an important effect on the treatment performance of additional alum. At the pH of 7, it appears that the higher amount of alum has better treatment performance than at lower concentrations when increasing the current density from 18 to
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31 A/m2. This may have been due to the increase hydrogen bubble production due to increase of hydroxide (OH-) at the higher current density. When this occurs, the additional alum provides available aluminum for aluminum hydroxide complexes.
Nevertheless, while the pH of 7 has a consistent pattern across the concentration of coagulant dose and increasing current densities, a pH of 4 had more optimum treatment performance.
6.1.2.1.2 Affects of Fe2(SO4)3 Addition
Figures LXVIII-LXXIX provide details on the affects of Fe2(SO4)3 as compared with current densities. At a pH of 4, Ferric Sulfate (Fe2(SO4)3) created a pseudo-bell shaped curve, where the tail ends of the current densities (18.69 A/m2 and 31.15 A/m2) had the best treatment performance, where 24.92 A/m2 had the worse treatment performance. Considering 24.92 A/m2, it was shown that as the Ferric Sulfate increased in concentration from 5 mg/L to 15 mg/L, the treatment performance worsened. This can be possibly explained by the fact that at a pH of 4, Ferric Sulfate reduced the pH and also created a potential competition for (OH-) complexes that were most likely reduced in availability for the oxidized aluminum. In addition, sulfate presence may have caused more of an issue of inhibition of the chemical reaction between the dye and the coagulant complex formation. Nevertheless, as stated earlier the treatment performance was shown the best at 31.15 A/m2.
At a pH of 7, increasing the current density certainly maintains or increases the treatment performance of Acid Yellow 11. It is shown that going from 18.69 A/m2 to
24.92 A/m2 does have a major impact on the treatment performance. However, at a current density of 31.15 A/m2, there is a considerable distance from the previous current
169 densities. This appears to imply that the addition of Ferric Sulfate is only helpful to removing Acid Yellow 11 when there is an increase in current density. The high current density appears to maintain this pattern. However, when one looks at Ferric Sulfate addition, the best treatment efficiency occurs at a pH of 4 and a dose of 5 mg/L. The increase of pH does not appear to improve the treatment performance, but rather better shows a better performance of treatment efficiency when increasing the current density.
However, a current density of 31.15 A/m2 still can be highlighted as having the best treatment performance for Fe2(SO4)3.
6.1.2.1.2 Affects of FeCl3 Addition
Results from Acid Yellow 11 with the addition of FeCl3 have been graphically represented in Figures LXXX-LXXXVIII . In chemical coagulation, ferric chloride is oxidized in the following method, where it can act as an acid, lowering the pH due its production of hydronium ions (391):
+3 - FeCl3 → Fe + 3Cl [49] (391)
However, despite this fact, it appears that regardless of the pH the treatment performance
2 of adding FeCl3 was shown at a current density of 31.15 A/m . At a pH of 4 and a current
2 density of 18.69 A/m , FeCl3 indicated a better treatment performance with increasing
FeCl3 addition. This can attributed to the increase of FeCl3 provided an additional available coagulant without the suffering from an anion production such as seen with the
2 sulfate from Fe2(SO4)3. At a current density of 24.92 A/m , with the exception of 5 mg/L, treatment efficiency was higher at both 18.69 A/m2 and 31.15 A/m2.
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At a pH of 7, as the current density increases, the treatment performance decreases for all doses of FeCl3. It appears that FeCl3 follows a similar pattern as Alum, where the highest removal efficiency occurred at a pH of 4. It has been showed that Alum as a coagulant in textile wastewater treatment performed very similarly to FeCl3 in coagulation and it appears that it follows a similar pattern when added to an electrocoagulator (392-394).
In general, Ferric Chloride can be considered as being very advantageous to the treatment efficiency of the wastewater, as the iron is readily available within the wastewater due to the dissolution into the wastewater during the stirring process. It has been shown in coagulation that FeCl3 is a viable coagulant performance as compared to Fe2(SO4)3 and
- Alum because FeCl3 is able provide additional Cl anions to the system for the treatment process along with reduce pollutant loading (395). However, from the results it does seem at pH of 7, the high concentration of FeCl3 maybe too high, leading to a phenomenon known as particle bridging (391).
Overall, Alum and FeCl3 had the best treatment performance of the wastewater as compared with Fe2SO4. As seen through literature, it has been found that FeCl3 and Alum have similar treatment performances when applied to chemical coagulation, while the performance of Fe2(SO4)3 is not as efficient as Alum (396). This was also confirmed in the response surface methodology equations, as the regression coefficients of great significance were time, current density, and time2. Also, pH of 4 has been the optimum treatment for both Alum and FeCl3, as seen within literature for chemical coagulation
(397), which has surfaced within treatment using adding chemical coagulant to the electrocoagulation process.
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6.1.3 KINETIC RESULTS
Kinetics rate constants were developed for each of the experiments based on the type of coagulant, coagulant dose (mg/L), pH, and current density (Figures XC-CXLIII). Kinetic analysis was completed based on zero, first, and second order for the purpose of comparison. Data plots were created for each series of coagulant doses at a particular pH and current density. The data was plotted in respect to treatment time. For zero order reactions, the data was the normalized concentration vs time; for first order reactions, -
nd 2 ln(C/Co), and 1/C for 2 order data. Analysis was considered the highest r value would be regarded as the order that most likely best represented the data collected. Table XVII summarizes all of the values for each coagulant, dose, and coagulant dose with its associate k and r2 values.
6.1.3.1 RESULTS FOR ALUM
The results from Alum were summarized in Figures XC-CVII In regards to alum, it was determined that the results from the experiment primarily reflected the zero-order rate, or that the reduction of concentration was linearly related to time. This occurred particularly when the current density was 18.69 A/m2 and a pH of 4 (Figures XC-XCII) (Alum dose =
0, k = -0.683 min-1, r2 = 0.939; Alum dose = 5 mg/L, k = -0.8553 min-1, r2 = 0.9642;
Alum dose = 10 mg/L, k = -0.7137, r2 = 0.9669; Alum dose = 15 mg/L, k = -0.8137 min-
1, r2 = 0.9535). For the remaining current densities and pH, this does not appear to have been the particular case. Out of the twenty remaining points of reference, ten best fit the zero-order reaction (for example, Alum dose = 10 mg/L, pH = 7, Current Density, 18.69
A/m2; Alum dose = 15 mg/L, pH = 7, Current Density, 18.69 A/m2). When coagulant is 172 added to the Acid Yellow 11 solution, it has been observed that as the dose increases, the number experiments that best reflected the zero order increases, where at a coagulant dose of 5 mg/L, only one out of six reflected a zero order reaction, while four out of the six were first order. However, once the coagulant dose increased to 15 mg/L, five out of six were zero order, and only was first order.
In regards to the effect of pH on the kinetic rate order, it appears that there is no major effect. Eight out of the twelve kinetic values at a pH of 4 were reported as best reflecting zero order, while only six out of the twelve kinetic values at pH of 7. Of the remaining values, it appears that the data best reflects first order, where only one out of the twenty four data points best reflects the second order. However, the data does indicate an effect occurs when it relates to current density. Current density showcases a decrease of zero order reaction rates with increasing current density measurements. At 18.69 A/m2, six out of the eight data points were zero order compared to only two for first order. However, looking at a current density of 31.15 A/m2, only three data points were zero order, while five were first order.
In summary, data collected from experiments using alum as a coagulant primarily reflected the zero order kinetic rate where increasing current density and coagulant dose had the most effect on the number of experiments that reflected the order. In addition to the zero order, first order was another primary rate reaction that best reflected the data in many cases. Only once throughout all twenty four possible conditions did the reaction rate reflect the second rate.
6.1.3.2 RESULTS FOR FERRIC SULFATE
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Ferric Sulfate experiments were summarized in Figures CVIII-CXXV. Similar to alum, the zero order rate best represents experiments using ferric sulfate as the coagulant, particularly twelve out of the twenty-four experiments, while eight were first order, and four were second order. The number of second order kinetic rates increased four times from alum. With the exception of no coagulant dose (ferric sulfate = 0 mg/L), the number of zero order kinetic rates is similar across the amount of ferrous sulfate, as two out of six were for coagulant doses of 5 mg/L and 10 mg/L and three out of six for a coagulant dose of 15 mg/L. This states that coagulant dose of ferrous sulfate does not predict whether or not a reaction will be zero, first, or second order.
In regards to initial pH, ferrous sulfate had a higher amount of zero order experiments
(eight out of twelve) when the pH was at 4, as compared with the pH of 7, where an equal number of experiments best fit zero, first, and second order rates (four). When one considered the initial pH, it appears that at lower pH when adding ferrous sulfate dose best fits a zero order reaction, while at a higher initial pH (7), could be either zero, first, or second order. For current density, the zero order rate is best represented five out of the eight experiments when the current density is 24.92 A/m2. This followed by four out of eight (current density = 18.69 A/m2) and 3 out of eight (current density = 31.15 A/m2).
For first order reactions, the most prevalent was at a current density of 31.15 A/m2 (three) and at 18.69 A/m2 (three). 31.15 A/m2 was also the most prevalent for second order rate reactions (two).
To summarize, ferrous sulfate was best represented by zero order reaction rates. It was determined that one could see pH does effect whether or not the kinetic rate is zero, first,
174 or second. This coagulant had the more second order rate reaction rates as compared with alum.
6.1.3.3 RESULTS FOR FERRIC CHLORIDE
Ferric Chloride (Figures CXXVI-CXLIII ) differs greatly from both Alum and Ferrous
Sulfate in that it consists of more variable kinetic orders as compared with the other two coagulants. It was found that twelve out of twenty-four experiments follow first order
2 2 kinetics (for example, pH = 4, FeCl3 = 10 mg/L, Current Density = 18.69 A/m , r =
2 2 0.9289; pH = 4, FeCl3 = 15 mg/L, r = 0.9807, Current Density = 18.69 A/m ; pH = 7,
2 2 FeCl3 = 10 mg/L, Current Density = 24.92 A/m , r = .9875). When considering a pH of
4, it was found that seven out of twelve experiments best fit first-order kinetics, while the remaining five experiments followed zero and second-order kinetics. Using the other coagulants at a pH of 4, eight out of twelve experiments were at zero order for both Alum and Ferrous Sulfate. At a pH of 7, five out of the twelve experiments were observed at both zero and first order kinetic rates (zero order: FeCl3 = 0 mg/L, Current Density =
2 2 2 2 31.15 A/m , r = 0.9708; FeCl3 = 5 mg/L, Current Density = 31.15 A/m , r = 0.9782;
2 2 first order: FeCl3 = 5 mg/L, Current Density = 24.92 A/m , r = 0.9936; FeCl3 = 10 mg/L,
Current Density = 24.92 A/m2, r2 = 0.9875). This is compared with Ferrous Sulfate which recorded four experiments each at zero, first, and second order, and zero order for Alum at six out of twelve experiments.
In regards to the dose of Ferric Chloride, when the coagulant dose was at 5 mg/L, only two out of six experiments best fit a zero-order kinetic rate. The remaining four were first order (three) and second order (one). Compared with Alum and Ferrous Sulfate, this
175 amount was very consistent with the use of these coagulants (Alum had one out of six zero-order and Ferrous Sulfate had two out of six). At a coagulant dose of 10 mg/L, four out of six and one out of six followed zero and first-order kinetics respectively. This value is very similar to Alum with four out of six and two out of six in Ferrous Sulfate were zero-order kinetic rates. Finally, the coagulant dose of 15 mg/L modeled three out of six at the first order. Alum and Ferrous Sulfate were both primary zero-order kinetics
(five out of six for Alum and four out of six for Ferrous Sulfate). It has been determined that Ferric Chloride at a dose of both 5 mg/L and 15 mg/L best fit first-order kinetics and zero-order for a dose of 10 mg/L.
Finally, the Current Density does not follow a specific pattern when the Current Density increases from 18.69 A/m2 to 31.15 A/m2. In fact, it was determined that first-order kinetics were almost even at each category of Current Density, where the remaining experiments were found to be at zero-order. It appears that the results from this experiment found variable order kinetics throughout the course of the experiments.
Overall, Ferric Chloride was primarily reflected as a first-order kinetic rate. This is slightly different than what was found from Ferric Sulfate and Alum which reported only zero-order kinetics. Across the board, nine out of seventy-two (1/9th of the experiments) were second-order, where both Ferric-based coagulants (Ferrous Sulfate and Ferric
Chloride) had four experiments were second-order and only one for Alum.
6.1.4 KINETIC RESULTS WITHIN LITERATURE
If one were to compile the results of published data concerning electrocoagulation and kinetics, it would be observed that there are two major factions that would occur—first,
176 the analysis of the overall reaction kinetics using zero, first, and second order kinetic rates, and second, the use of adsorption models such as Lagregren for the purpose of analyzing adsorption kinetics. Wang and Chou found that the removal of chemical oxygen demand (COD) best followed the first-order model (398) comparing first order and second order kinetic rate. Also, Olad et al. found pseudo-first order kinetics was heavily favored as compared to pseudo-second order for the purpose of treating
Alphazurine FG Dye (399). Ahmadian et al. compared zero, first, and second kinetic order for five-day biochemical oxygen demand (BOD5), chemical oxygen demand
(COD), total suspended solids (TSS), and total nitrogen (TN) across five different current densities (5 to 25 A/m2) for the purpose of treating slaughterhouse wastewater. It was found that the parameters primarily following first-order kinetics regardless of the current density or parameter (354). First-order kinetics also is found for the removal of fluoride
(400) and Reactive Red 88 (401).
As previously stated, dye is adsorbed to the hydroxide complexes for the purpose of being removed. In order to describe this phenomenon, many authors have used a series of kinetic models that consider pseudo-first and pseudo-second order kinetic rates. These models are known as Lagergren models. Pseudo-first order Lagergren models form kinetic rate constants by comparing the difference between the adsorption of constituent at a given time and the adsorption at the initial time.
The pseudo-first order model begins with Equation 50:
dqt/dt = k1(qe-qt) [50] (402, 403)
177 where qe = amount of constituent adsorbed onto hydroxide complex at time 0 (mg/g) qt = amount of constituent adsorbed onto hydroxide complex at time t
Integration beginning with t=0 and q=0 to any given t and q results in the following: log(qe - qt) = log(qe) - k1t/(2.303) [51] (402, 403)
To determine the effectiveness, authors will complete a similar procedure as with conventional kinetics, graphing log(qe-qt) vs time and determine the r2 value as an indicator of best fit for that particular experiment. To calculate the pseudo-first order, one will first need to calculate the adsorption by the particular entity. It can be estimated that one can calculate from the following:
q = V(Co -C)/w [52] (404, 405) where q = amount of constituent adsorbed onto hydroxide complexes (mg/g) V = volume of solution (L) Co = initial concentration (mg/L) C = concentration at time t (mg/L) S = mass of constituent (g)
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When calculating S, one is capable of calculating this value by using Faraday's Law, for the purpose of determining the production of flocs by using stochiometry (404, 405).
Equation 53 expresses Faraday's law:
w = iAtM/zF [53] (405) where w = amount of coagulant dissolved in (g) i = current density (A/m2) A = area of anode (m2) t = electrocoagulation time in seconds (total treatment time) M = molecular weight of the electode z = valence number of the electode F = Faraday's Constant (96,500 C/mol)
One is capable of estimating the total amount of coagulant that is dissolved into the electrocoagulator. By solving for w, one can replace into the equation for the amount of constituent adsorbed onto the hydroxide complex to determine the various q's. Qe, or the equilibrium adsorbent, is determined by using the equation, replacing C by the final concentration (405).
On the other hand, the pseudo-second order kinetic equation [54] can be described in the following manner:
2 dqt/dt = k2(qe-qt) [54] (402-403)
Through integration, one is capable of developing a linear equation [55]:
2 t/q = 1/k2qe + t/qe [55] (402-403)
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Within this case, one will graph t/q vs t to determine the slope of the line (402-403, 405).
By knowing the slope, one can calculate qe. For the purpose of calculating k2, one must use the y-intercept and the recently calculated qe.
For the purpose of this experiment, Lagergren models were created and compared for the best % removals by all three coagulants at three separate current densities. In order to calculate the amount of coagulant dissolved, Faraday’s law was calculated for aluminum at each current density. Since chemical coagulant was added to the treatment process, it was decided to calculate the amount of Fe+3 and Al+3 from the chemical coagulant. This was added by taking the weight of the coagulant used and using molecular weights and molar ratio, determine the amount of Fe+3 found in Ferric Chloride and Ferric Sulfate, and Al+3 in Alum. Once these values were calculated, they were added to the Al+3 formed from electrocoagulation. Overall, an estimated value was determined for the total amount of coagulant that was dissolved into the electocoagulation through the 30 minutes of treatment.
Using the methods as described by literature, pseudo-first and pseudo-second order kinetic constants were calculated along with the q corresponding to the process for each order. Figures CXLIV-CXLIX visually summarize pseudo-first order and pseudo-second order Lagregren models for each coagulation addition. According to the results, it was determined that the pseudo-first order was more likely to describe the adsorption kinetics being displayed within this experiment for all coagulant additions, with the exception of
2 -1 2 FeCl3 and a current density of 31.15 A/m (qe = -28.99 mg/g, k2 = 0.000839 min , r =
2 0.9149). Table XVIII provides the individual k, qe, and r values for the rest of the experiments based on the coagulant addition and current density. According to literature
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(402-403, 405), conventional electrocoagulation (no coagulant added) followed pseudo- second order kinetics when using the Lagregren method. However, in this experiment, it was shown to be in the contrary. One of the possible explanations that can attribute to following the pseudo-second order kinetics is the fact that the authors used iron and galvanized steel as their anode and cathode respectively, while this experiment used aluminum. Therefore, the potential differences in adsorption characteristics may have been due to the use of aluminum as compared to iron or galvanized steel.
6.2 DISCUSSIONS
6.2.1 Electrocoagulation process
The anode oxidizes (aluminum = Al+3, iron = Fe+2), while on the cathode hydroxide forms from the disassociation of water, increasing the pH. The pH determines what type of complex is formed. For the iron electrodes, when pH is between 5.5 and 8.5, ferrous hydroxide complex is formed. As pH increases past 8.5, ferric hydroxide (Fe (OH) 3) precipitates out. If one were to use aluminum, when the pH is less than 5, monmeric
4- +4 complexes form (Al(OH)3, Al(OH) ), while between 5-6 polymeric (Al2(OH)2 ,
+3 Al6(OH)15 ), and greater than 6, Al(OH)3 precipitates. With the presence of ferric and aluminum hydroxide complexes, a process known as surface complexion or electrostatic attraction happens (406). What this means is two things:
A. The dye acts as a lignin to these complexes and bind to the central ion, pushing the dye away from the wastewater. At high current, the complexes float to the top and are needed to be skimmed by electroflotation (406).
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B. The dye is opposite in charge to the complexes which causes the attraction. Following this attraction is the coagulation process. Coagulation happens after attraction from the formation of hydroxide complexes (406).
In addition, there are two primary mechanisms that occur within an electrocoagulator. At a pH of less than 6, flocculation occurs by means of precipitation. Monomeric complexes form at a pH between 4 and 5 and then precipitate a solid dye monomeric complex. At a pH of 5 to 6, solid dye polymeric complexes form. On the other hand, when the pH increases to a value of 6.5, the primary mechanism at work is adsorption. Adsorption forms particles that combined with aluminum sulfate monomeric and polymeric complexes (363).
Therefore when measuring the pH during the experiment, it is important to consider the various components that occur during the electrocoagulation reaction. For example, it has been determined that the pH is usually higher at the cathode as compared to the anode.
This is due to the fact that at the anode there is a higher formation of hydronium ions from the disassociation of the hydroxide ions during the electocoagulation process. Also, it is important to consider that chloride ions have a major impact on the corrosion of electrodes. Sodium chloride (NaCl) as an electrolyte “attacks” the OH groups at the surface of the electrode (407). The corrosion of electrodes due to chloride can greatly affect how proficient using the sacrificial electrodes within experiments. This was one of the advantages of completing the Box Behnken as the center points experiment was completed several times. By completing the same experiment frequently, one can
182 compare the results from the experiment with those that had already been previously done.
The current density (based only on the anode area and not both), is the control mechanism for the coagulant dosage (163, 364). When we use aluminum as a cathode, it can sometimes produce Al (OH)4- by being "attacked" by hydroxide ions increasing the amount of coagulant present. Iron does not do this (406). Therefore, it can be said at low concentration of dye wastewater, high added coagulant dosage is in excess, which is why color is not really removed, as compared with low added coagulant doses. It is almost like it "steals" dyestuff from the in-situ coagulant. When high dosage of dye is present, higher dosages of coagulant can be added to improve treatment.
What I found is consistent to my results from my experiments. During preliminary experiments, when we had high concentration of waste (150 mg/L), it was observed that about 40% absorbance removal after ten minutes at 100 mg/L, while using no added coagulant, about 18% absorbance removal. Because of dye concentration is so low and too much coagulant causes problems, I asked “How much coagulant should I apply?” The coagulant doses of 5, 10, 15 mg/L of coagulant were fair for comparison.
Anything over 20 mg/L probably would cause excess aluminum hydroxide complexes causing absorbance to increase as time increases, where the aluminum cathode was being
"attacked" by the hydroxide. This can be confirmed by a higher pH from time zero to 5 minutes. In fact, it has been reported that excess alum reduces the neutralization of dye
183 constituent reversing the charge. When this occurs, the treatment process has been slowed significantly (408).
6.2.2. Electrocoagulation as Full-Scale Treatment
Electrocoagulation has been used in various avenues for the purpose of wastewater treatment. There have been many attempts in developing equations for electrocoagulation based on first order principles in order to apply this technology on a full-scale level. Currently, electrocoagulation treatment technology has not found the methodology to develop design equations. In fact, there have been various difficulties in regards to developing consistent equations that would garnish design equations that could be applicable in any given circumstance. Possibly reasoning is outlined in the following
(409):
1. Inconsistency in design—One of the proponents of electrocoagulation design is that each design has been created individually by the researcher void of any regards towards other research projects completed. The major constituent that can be highlighted within design inconsistency is the geometry of the reactor (409). Properly sizing the reactor can have profound effects on the treatment, specifically in the realm of bubble pathways, flocs, and flotation (410).
2. Effect of treatment—Some of the treatment effects that need to be considered include the type of electrode being used (aluminum, steel, iron, and/or the combination thereof), type of wastewater being treated, and the process of restoring the electrodes or passivation. These three can have a major impact on developing design equations, simply
184 because they would involve factors that would have to be implemented in order to correctly design an equation that would be useful in any given circumstance (409).
3. pH effect—It can be said that a sound range of treatment would be between 6.5 and 7.5. However, depending on the wastewater characteristics and other factors, it may not be possible to optimize the pH within such a small realm (409).
Nevertheless, various groups have created pilot scale treatment units that are capable of treating real wastewater. Burrangong Meat Processes (BMP) uses a 29 electrode electrocoagulator for the purpose of treating sheep, pig, and beef wastewater.
Stick or low temperature wastewater and abbatoir or blood wastewater has been fed through a series of treatment units consisting of a feeding tank, reaction chamber, developmental tank, and sludge separation tank. Within the electrocoagulation reactor includes parameters such as a flow rate between 0 and 10 kL/hr, power supply (0-750 A;
0-75 V), temperature of 30 degrees Celsius. Highlighting some of the results from the experiment it was found that when treating 50% stick and abbatoir at a flow rate of 4.5 kJ/hr, 200 and 300 A, includes 99% total suspended solid (TSS) removal, 74% total
Kjeldahl nitrogen (TKN), 92% total phosphorus (TP), and 93% chemical oxygen demand
(COD).
A pilot-scale electrocoagulation treatment process used for the purpose of treating grey wastewater operating at a total of 30 days. The process design for the treatment of wastewater begins where wastewater enters a sinkhole combining sodium chloride
(NaCl) and sulfuric acid (H2SO4) is then transferred into an ECF system using aluminum electrodes. Having been treated by ECF, the treated wastewater moves to a flotation
185 device where floatables would be collected in the sludge. Finally, water enters a storage tank, combining sodium hypochlorite (NaClO), pumped, and then distributed. The results considered the chemical oxygen demand (COD), suspended solids (SS), and turbidity versus operating time. The optimum removal occurred between 5 and 10 days of operating time (411).
6.2.3. Electrocoagulation Combined with Photocatalysis for Two-Staged Treatment
To further expand on current treatment, it was proposed that electrocoagulation could be combined with photocatalysis for the purpose of creating a two-staged treatment experiment. Preliminary tests were conducted (results not presented) with approximately
100 mg/L of Acid Orange 7. The treatment consisted of 30 minutes treatment time of electrocoagulation and then 3 hours of irradiation, where 1 L of Acid Orange 7 underwent electrocoagulation, while 200 mL of the electrocoagulation effluent received photocatalyst treatment by Fe-doped and Co-doped Titanium Dioxide. The results indicated that while electrocoagulation was capable of significantly reducing the concentration of Acid Orange 7, it was found that once reaching the photocatalyst stage, the treatment was lowered significantly.
It was concluded that the possible reason for poor treatment at the photocatalyst stage was due to the presence of sodium chloride used as the electrolyte for electrocoagulation. This problem is not a unique circumstance as it has been studied carefully in literature. Olabarrieta et al. found that NaCl has a potential effect on photocatalyst coatings, where the severity is contingent on the composition of the TiO2 nanoparticles. In addition, NaCl can potentially change the dynamics of the TiO2-TiO2 interaction, specifically noting the formation of TiO2 agglomerates that were detached
186 from the photocatalyst coatings by means of water flow (412). Other authors referenced previous literature as well. For example, several authors stated that the Cl- anions effectively “scavenged” hydroxyl radicals from oxidized agents to form chlorine radicals
(413-415). Equation 55 summarizes the effects of this effect:
*OH + Cl- - Cl* + OH- [55] (413, 415)
By interrupting the hydroxyl radical process, newly created chlorine radicals effectively slow down the degradation rate of the wastewater compound, along with blocking the activate binding sites on TiO2 by essentially by adsorption (415-417), and inhibit photocatalyst activity (413, 415-417). Also, Cl- anions engage in competition with oxygen for electrons (417), and trap valence band holes and hydroxyl radicals at the surface (414, 416). It has been also stated that Na+ ions have the potential of preventing catalyst electrostatic attractions (413). A potential solution that could reduce the effects of Cl- anions would be to change the pH, to particularly neutral (pH~7) and alkaline
(pH~8-9) (417). Future research involving comparing the concentrations of NaCl added for electrocoagulation is necessary to determine the optimum initial concentration, current density, and also pH. Once finding significant parameters, additional studies need to be considered if precipitated flocculants and adsorbed particles of the resulting treated dye from electrocoagulation may have a dual effect on the photocatalytic activity.
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CHAPTER 7
TOXICITY
7.1 DEFINITION OF TOXICITY There is great importance to consider the use of toxicity. The reason for toxicity is such that one will be able to determine when applied the significant effects of a given dose that would usually result in the measurement of mortality of the given species that is being tested. The majority of the time, experiments conducted will apply concentrated doses in short time intervals for the purpose of making assessments on health implications. When one thinks of measuring values of toxicity, there are two major tests that are conducted--median lethal dose (LD50) and median lethal concentration (LC50).
One can differentiate the difference between median lethal dose and concentration, as being on a macro-scale, considering the species in its entirety, while lethal concentration pertains to the group that is being exposed during the given experiment (418).
One can designate that there are various responses that one can measure when completing toxicity testing. These include visible structure changes of a given organism at the molecular, cellular or tissue level, the entire organism itself, a subset group of a given species, or the consideration of a species on a larger scale (419).
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7.2 TYPES OF TOXICITY TESTING IN WASTEWATER TREATMENT The Environmental Protection Agency (EPA) has outlined a series of test methods that are to be implemented to ensure proper discharge levels into surface waters from sources as outlined by the National Pollution Elimination Discharge Systems (NPDES).
The analyses of the waters have been conducted by the use of a series of tests known as
Whole Effluent Toxicity (WET) test methods. Each test method has been segmented into four major categories—freshwater acute toxicity, freshwater chronic toxicity, saltwater acute toxicity, and saltwater chronic toxicity. Each of these tests is based on procedures as outlined by the WET (420, 421). When considering toxicity testing in general, one can divide tests into categories based on the organism used for testing. Farré and Barceló outlined five different divisions—fish, invertebrate, plant and algae, bacteria, and biosensors (422).
Fish bioassays consider the mortality of organism presence during a given time of exposure. Usually, rainbow trout and fat head minnow are considered, measuring constituents such as growth-rate, larval survival, and larval growth. Invertebrate toxicity testing uses organisms such as Ceriodaphnia and the well-known Daphnia magna. Tests have been conducted on both acute and chronic levels, where the most common test using
Daphnia for a time frame of 24-48 hours. Usually, invertebrate toxicity considers death of an organism or ability to reproduce under the given circumstances (422).
Plant and algae tests provide strong toxicity information, considering biomass weight, and enzyme activity. A common example of plants would include Chinese cabbage has been more commonly used for plant toxicity tests, while algae such as
Selenastrum capricornutum have been implored for the purpose of determining algal
189 growth. Luminous bacteria such as Vibrio fischeri have been used developing toxicity test procedures such as Micotox, where light emission is used for the determination of the organism’s activity. Biosensors, “analytical devices that combine a biological sensing element (such as an enzyme, DNA, or a microorganism) with a transducer, which converts a biological signal into a measurable signal” have become more recently common methods for toxicity testing. An example of a biosensor test that has been used includes UV absorption by bacteria (422).
ToxTrak Rapid Toxicity is a toxicity system that has been developed by Hach
Company which is to be used for the purpose of determining toxicity by considering bacterial respiration. Analysis made by considering the amount of reazurin reduced within a given sample having been measured in a spectrophotometer or colorimeter at a range between 600 and 610 nm (423).
7.3 APPLICATION OF BIOASSAYS DURING ELECTROCOAGULATION
There have been very few bioassays techniques that have been used for the purpose of analyzing the efficiency of electrocoagulation. Palacio et al. considered using lettuce seeds (Lactuca sativa) and brine shrimp (Artemia salina), where it was determined that toxicity was within 5 minutes of electrolysis time was the lowest toxicity level (424). This was completed by having textile dyeing wastewater using iron electrodes. Yetilmezsoy et al. analyzed the toxicity of pretreated wastewater by the means of an up-flow anaerobic sludge blanket (UASB) reactor and using electrocoagulation. The toxicity test was conducted using guppy fish (Lebistes reticulatus) (425). In addition, Chang et al. concluded that the mean concentration
190
(EC50) were reduced from their initial toxic values of 6.6 to 25% based on the electrocoagulation treatment of acid orange 6 by the means of using iron electrodes (426).
7.4 APPLICATION OF BIOASSAYS TREATING DYE WASTEWATER
With respect to dyes, due to their constituents, it has been determined that dyes consist of carcinogenic, tetraogenic, and mutanogenic concerns (427), where azo dyes exhibited the highest mutanogenic potential (428). Zhang et. al reported several human health consequences of dye wastewater release, including kidney, liver, and central nervous system damage (429). Experiments conducted on acute toxicity found that bleaching exhibited the highest acute toxicity. In addition, literature have also used different toxicity testing methods for other treatment of dye wastewater such as nematodes Caenorhabditis elegans for the purpose of determining three azo dye wastewaters (430) and Daphnia magna for toxicity testing of 48-h EC50 of azo dyes
Remazol Parrot Green and Remazon Golden Yellow (431). Also, authors Abadulla et al. were able to use the organism Trametes hirsute and laccase for the purpose of being able to decolorize and detoxify various dyes consisting of components such as triarylmethane, indigoid, azo, and anthraquinonic constituents (432), while decolorization of textile dyes was accomplished for Remazol Turquoise Blue G and Lanaset Blue 2R by means of horseradish peroxidase (HRP) within both the dyes and the effluents themselves. After using treatment by means of HRP, the study concluded by considering the toxicity using
Daphnia magna for the purpose of analysis (433). Following hydrolysis and decolorization of Reactive Black 5 and three Procion dyes, authors Gottlieb et al. considered toxicity by means of using the bacteria Vibrio fischeri (434).
191
Toxicity texting was completed by using a diazotrophic cyanobacterium Nostoc muscorum ISU for the dye Omega Chrome Red ME (435), while textile dyes and
Eithidium bromide dye were tested by means of using both Salmonella typhimurium strains (Ames test) and also the use of seed germination of four plants (seed germination test) (436). Birhani and Ozmen considered the toxicity of frog embryo textratogenesis assays-Xenopus for astrazon red FBL, astrazon blue FGRL, remazol red RR, remazol turquoise blue G-A, cibacron red FN-3G, and cibacron blue FN-R based on the exposure limit to determine enitites such as malformation (EC50) and median lethal concentration
(LC50) (437).
Finally, Meric et al. determined the effectiveness of Fenton’s oxidation and ozone with coagulation-floculation process for the purpose of not only considering the removal of color but also toxicity by means of this treatment method (438). Evaluation of toxicity was conducted by means of using Daphnia magna, Dojcinovic et al. considered the toxicity having completed decolorization of Reactive Black 5, Reactive Blue 52, Reactive
Yellow 125, and Reactive Green 15 by means of advanced oxidation processes (439), while evaluating the brine shrimp Artemia sauna, Daphna magna acute toxicity test analyzed the degradation of Glycoconujugate Azo Dye (GAD) by means of using
Fusarium oxysporuym (440).
The Lemna minor toxicity test reduced toxicity to evaluate the use of twenty-five species of fungi for the purpose of the degradation of 9 commercial industrial dyes (441).
The Microtox assay toxicity testing was used to evaluate the decolorization made by the use of fungal laccase to treat Reactrive Blue 19, Dispersed Blue 3, Acid Blue 74, Acid
Red 27, and Reactive Black 5 (442). Bergsten-Torralba et al. evaluated the effectiveness
192 of toxicity reduction after the use of fungal treatment for the purpose of decolorizing textile dyes Reactive Red 198, Reactive Blue 214, and Reactive Blue 21 by means of
Penicillium simplicissimum INCQS 40211 (443). The toxicity test was completed by looking at the microcrustacean Daphnia pulex, and microbial toxicity was evaluated on the treatment process of C.I. Reactive Green 19A by Micrococcus glutamicus NCIM-
2168 for completed decolorization and degradation (444).
7.5 TOXTRAK
Hach’s Toxicity testing was birthed out of paper that was written by Liu which described the use of reazurin as a method of analyzing toxicity that was contrary to conventional methods (390), referenced by Anglin, an inventor of a patented colormetric test (445).
What was done differently by Liu as traditional methods ascribed within the aforementioned pages was the analysis of dehydrogenase considering the inhibition of 50% of the population (390). Liu developed Equation 56:
I = [(A-B) – (A-C)]/(A-B)*100 [56] (390) where A = final O.D. of reagent control
B = final O.D. of cell control
C = final O.D. of reaction mixture
In addition, the analysis of toxicity was only 2 hours, as compared with 24 hours for most conventional methods.
Chapter Summary
Toxicity is a very important parameter that has not been explored as highly as it could be in environmental engineering. Future studies conducting toxicity tests would help in
193 shedding light on the potential toxic effects of discharged wastewater from electrocoagulation.
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CHAPTER 8
PHOTO-OXIDATION
This chapter has been taken in its entirety from a research paper submitted for publication in the journal Environmental Progress and Sustainable Energy by Dr. Yung-Tse Hung,
Dr. Yen-Pei Fu, Dr. Mohammad Suleiman Al Ahmad, Dr. Ching-Cheng Chen, and myself titled: Photo-oxidation treatment of Acid Orange 7 Synthetic Wastewater by
Nanopowder TiO2 catalyst using an ultraviolet, visible, and infrared light source (446).
8.1 INTRODUCTION
Dye wastewater treatment has evolved branching from specializations outside the parameters of conventional sanitary engineering. Dye wastewater has been characterized as having many pollutants of concern such as high biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solids (TSS) (10), along with toxicity and color (6). These pollutants are produced during the various dyeing processes,
195 either through the application onto the fabrics themselves, or the by-products which are formed following the process (10), (13).
Photocatalytic degradation is one of the new treatment methods being evaluated for dye wastewater. An advanced oxidation process (AOP), photocatalytic treatment involves the use of nanocatalysts. Nanocatalysts impact dye wastewater by using adsorption and desorption techniques, where the dye itself transitions in a circular liquid-solid phase change, from its initial attachment onto the catalyst surface, through its destruction in the solution itself. The conventional process begins with ultraviolet light, where one is capable of achieving the formation of hydroxyl radicals from water and super radical anions from oxygen, as electrons interact with the catalyst surface. From there, electrons move from the valence band to the conduction band causing these transitioned electrons to combine with those species surrounding the catalyst surface. Having formed hydroxyl and super anions, these entities combine with the dye present in the wastewater and decolorize it (447). In addition, photocatalysts form electron/hole pairs. These pairs conduct reduction-oxidation reactions, where the holes will oxidize the constituent on the surface of the catalyst by means of oxygen. This reaction occurs at the surface of the semiconductor (448). The reaction mineralizes constituents into products such as water and carbon dioxide (449). Equations 58-60 summarize photocatalysis equations with titanium dioxide (TiO2) as the photocatalyst:
+ - TiO2 + hν → h + e (58) [342, 447, 450-451]
+ ∙ + h + H2O → OH + H (59) [342, 447, 450-451]
- -. e + O2 → O2 (60) [342, 447, 450-451]
196
where h+ = valence band electrons; e- = conduction band electrons;
Photocatalysis has many advantages for the treatment of wastewater as compared with other treatment process in that one is capable of designing an experiment that would develop a very short retention time. While decolorizing dye by means of anaerobic and aerobic processes achieves favorable treatment efficiencies, aerobic and anaerobic treatment require long retention times (452-454). However, in some cases it has been noted using some microorganisms during aerobic treatment are incapable of biodegrading because of the xenobiotic constituents present within the wastewater (455-456). Other advanced oxidation processes (AOP) such as ozone, run the risk of having high energy costs that is associated with the equipment and formation of ozone (38). Finally, electrochemical methods such as electrocoagulation produce waste from spent electrodes, which will require regeneration or sold for scrap metal. This can become very cumbersome if one has a large amount of spent electrodes that may or may not be recoverable based on their condition. Photocatalysis not only is capable of treating wastewater, but because of its regeneration potential, it is capable of being a sustainable treatment technology for dye wastewater. In addition, photocatalysis has been reported as being successful in treating dye wastewaters (449, 457).
Within photocatalysis, there are a plethora of benefits for using TiO2—it is non-toxic, there are many methods of synthesization available to the researcher, and it is versatile in photocatalysis application (458, 459). Also, TiO2 has been expressed as being the most useful in environmental applications (460). However, throughout literature it has been discovered that one of the biggest issues with using titanium dioxide (TiO2) is the band gap energy, where it is estimated at 3.2 eV (458, 461-464). This measured band gap
197 energy is insufficient in being able to be used in visible light due to absorbing wavelengths less than 385 nm (451,458,465), which constitutes a limiting amount of solar energy (458,466). Therefore, research has been conducted by combining TiO2 with a transition metal in a process known as doping.
There are many advantages of using transition metals. First, transition metals increase a photoreaction due to its higher surface area. In addition, doped TiO2 traps electrons within band gaps forming anions. This anion formation increases dye decolorization
(456-457,467). Third, the augmentation of TiO2 with transition metals develops a sustainable treatment method, making this a more cost effective treatment process.
Instead of the high costs and safety hazards associated with using UV lamps, photo- oxidation can be performed under various light sources, such as simulated or actual solar light (468-470). Finally, doped-TiO2 has the potential of being reused following purification (471-472).
Acid Orange 7 (AO7), C16H11N2NaO4S (449), an azo dye, has been treated by methods such as photocatalysis (342,473-479), electrochemical techniques (480-481), adsorption
(482-489), biological methods (343-344, 382, 490-491), hydrogen peroxide (345, 492-
495), and other new techniques (479, 496-497). However, many photocatalyst experiments use light sources that can be very expensive. It is important to consider a treatment method that can be efficient in reducing the overall cost of setup and operation.The purpose of this paper is to determine the effect of doping on the treatment performance of nanocatalysts on Acid Orange 7 dye wastewater using an inexpensive light source.
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8.2 EXPERIMENTAL SECTION
8.2.1 DOPED TIO2 SAMPLES PREPARATION
Iron-doped titanium dioxide (Fe-doped TiO2) and Cobalt-doped titanium dioxide (Co- doped TiO2) photocatalysts were synthesized by a conventional solid-state reaction method with high purity anatase TiO2, Fe2O3, and CoO powders (>99 %) as starting materials. The synthesization process used core-shell techniques for the purpose of creating magnetic materials. These powders were mixed under ethanol and milled for 12 hours using zirconia balls. The ball-milled mixture was dried and ground into a powder with mortar and pestle and then calcined in air at 600℃ for 4 hours. In this study, the mole ratios of TiO2:Fe and TiO2:Co were set to be 100:2. The size of the nanoparticles is in the range of 0.2 to 2 micrometers. Photocatalysts were prepared in the laboratory at the
Department of Materials Science and Engineering at the National Dong Hwa University,
Taiwan. However, the photocatalysis experiments were conducted in the laboratories at the Department of Civil and Environmental Engineering at Cleveland State University,
Cleveland, Ohio, United States.
8.2.2 REACTOR SETUP
The photocatalyst experiment was conducted in mind for the purpose of being cost- effective. Two reactors were setup side-by-side in a dark room with dimensions of 21 ½” x 19 ¼” x 15 ¼” and 25” x 18 ½” x 13 ¼”. Despite the dimension differences, a preliminary test of identical conditions was conducted within each reactor. The results concluded an error for the final % absorbance between each reactor was about 1.4%. In
199 addition, the distance of the lamp to the beaker was measured carefully to retain consistency (distance = 3.12 inches). The apparatus consisted of a stirrer, lamp, and lighting fixture. The light source consisted of a UV-B lamp (150 W, Powersun UVB bulb, Zoo Med, San Luis Obisco, CA) and a fixture (Deep Dome Fixture, Zoo Med, San
Luis Obisco, CA). The UV-B lamp consists of an incandescent filment, along with a mercury vapor arc tube (498, 499). This light source simulates solar light, emitting ultraviolet, visible, and infrared light, which according to the manufacturer, has a spectrum between 200 and 800 nm. It is a very inexpensive light source that requires the use of a ceramic fixture. Spectroadiometic measurments had been previously conducted by the manufacturer for the purpose of understanding the output of the light source. At a distance of 12 inches and a voltage of 120, it was determined that the irradiance was 1.72 x 10-3 W/cm2 with a range of 200 and 750 nm. It was also determined that 81.6% of the light was visible, while 18.7% was UV (15.0% UVA, 3.7% UVB, and 0.001% UVC). In addition, the correlated color temperature was measured at 4014 K (500). An estimated cost of the lamp, fixture, and the energy (based on 0.08 US$/kWh) for treating 100 mL of
3 waste for three hours was a total of $1.19/m of wastewater treated.
8.2.3 SAMPLE PREPARATION
Acid Orange 7 sodium salt (>99% purity) (501) was purchased from Santa Cruz
Biotechnology (Santa Cruz, CA) and was prepared as a stock solution. It was chosen as the treated wastewater due to the success of removal by nanocatalyst, along being one that is found within the food and textile industries (502). A calibration curve based on the stock solution was created. Each sample consisted of a total volume of 100 mL which was added to a 250 mL beaker from the stock solution. 200
Following sample preparations, synthesized anatase doped titanium dioxide was measured and weighed on a scale. The doped titanium dioxide was then transferred to its respective 250 mL beaker; the beaker was covered with plastic wrap to reduce evaporation of the sample during irradiation.
Samples were then placed on a magnetic stirrer at an approximate distance of 7.94 cm from the light source and stirred in the dark for a total of 5 minutes. After the conclusion of 5 minutes, the UV-B bulb was then turned on and samples would receive light from the bulb to begin the reaction.
8.2.4 SAMPLE COLLECTION AND ANALYSIS
Sample collection began prior to placing the doped titanium dioxide into the beaker, as a small sample of Acid Orange 7 dye was collected using a syringe. Subsequent samples were taken from the beaker following reaction times of 30, 60, 119, and 180 minutes.
After each sample period, there was a 5-10 minute period prior to the continuation of the reaction. This is due to the fact that the bulb became warm and required cooling prior to continuing the experiment. Following collection, samples pass through PVDF syringe filters (Tisch Scientific, North Bend, Ohio) into a test tube (503-505). Testing of the syringe filters before and after the experiment discovered that filtration had little effect on results comparing wastewater with and without photocatalysis. Following filtration of the samples, the test tubes were then placed into a Spectrophotometer (Spectronic
Genesys 20, USA) for measurement of absorbance. Recorded values for samples were completed at a wavelength of 484 nm, a value that is an appropriate range for the
201 maximum absorbant wavelength of Acid Orange 7 dye (480-486 nm) (342-348). Samples were analyzed for the effectiveness of the nanocatalysts to decolorize Acid Orange 7 dye.
The calculation of Acid Orange 7 efficiency of decolorization was determined by using
Equation 61:
Decolorization (%) =[(AO7)i – (AO7)f)/(AO7)i] x 100 (61)
Where (AO7)i is the initial Acid Orange 7 concentration (mg/L); (AO7)f is the final
Acid Orange 7 concentration (mg/L).
Prior to analysis of results, all data points were normalized to ensure that all samples reflected the perspective initial concentrations of 24 mg/L, 34 mg/L, and 44 mg/L. Each of these values were transformed into –ln(C) for the purpose of graphing –ln(C/Co) vs time (mins) was completed as a prior step to determine Langumir-Hinshelwood coefficients. The values were compared to a control (without doped-TiO2).
8.3 RESULTS AND DISCUSSION
8.3.1 ANALYSIS OF CO-DOPED TIO2 AND FE-DOPED TIO2
Figure CL, prepared by a UV/Vis Spectrophotometer at the Department of Materials
Science and Engineering at the National Dong Hwa University, Taiwan, indicated that metal doped-TiO2 effectively reduced the band gap energy from 3.0 eV for undoped TiO2 to 2.61 eV for Fe-doped TiO2 to 2.60 eV for Co-doped TiO2. The significance of these items is that the application of a transition metal allows for the use of visible light for photo-oxidation experiments.
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8.3.2 TREATMENT EFFICIENCY BASED ON NANOCATALYST TYPE
8.3.2.1 CO-DOPED TIO2
The summary of the results have been presented in Figures CLI-CLVI. It was discovered that treatment efficiency was contingent on two major factors-- the initial concentration of the wastewater dye and nanocatalyst dose. For Co-doped TiO2, the highest treatment efficiency occurred when the dose of Co-doped TiO2 was 0.25 or 0.5 g/L. When 1 g/L Co-doped TiO2 was applied, the treatment efficiency was the lowest. At an initial dye wastewater concentration of 24 mg/L, treatment efficiency decreased as the dosage of Co- doped TiO2 increased. When 0.25 g/L of Co-doped TiO2 was applied to the wastewater, 24 mg/L of Acid Orange 7 was reduced to 5.75 mg/L after 180 minutes
(76.43%). When one applied 1 g/L, 24 mg/L of Acid Orange 7 reduced to 8.93 mg/L
(62.76%). At 34 mg/L, treatment efficiency was highest when the dose of Co-doped
TiO2 was 0.5 g/L (60.90%), as compared with 1 g/L (39.90%). Finally, at 44 mg/L, the treatment efficiency followed a similar pattern as 24 mg/L, where treatment efficiency was inversely related to the catalyst dose. In this treatment application, the results indicated that the dose of 0.25 g/L and 0.5 g/L were very similar at this concentration
(40.27% and 40.07% respectively), as compared with the dose of 1 g/L (35.26%). When one observes the treatment efficiency across the Co-doped TiO2 dose and the initial dye wastewater, treatment efficiency is inversely related to concentration. Overall, Co-doped
TiO2 had the highest treatment efficiency across the initial concentrations when the dose was 0.25 g/L—76.43% at 24 mg/L, 47.26% at 34 mg/L, and 40.28% at 44 mg/L. This can be compared with 1 g/L which has the lowest treatment—62.77% at 24 mg/L, 39.90% at
203
34 mg/L, and 35.26% at 44 mg/L. Overall, Co-doped TiO2 had a treatment range between
39.90% and 76.73%. This treatment efficiency fits within the range for literature, which recorded values between 5% (467) and 92.57% (467, 506). Poor treatment by Co-doped
TiO2 at 1 g/L of nanocatalyst can be attributed to its clumping and settling at the bottom of the beaker, reducing the surface area. This phenomenon resulted in poor treatment results at this dose (467, 507).
Irradiation time is very significant in the treatment efficiency of wastewater. All runs within this experiment were conducted with a maximum irradiation time of 180 minutes per run. The results under the experimental irradiation time have greatly improved on the performance of Co-doped TiO2 in visible light. Bouras et al. compared various mole% of
Co-doped TiO2 for an irradiation time of 90 minutes. It was determined that at 0.07 mole% of Co-doped TiO2, the removal efficiency was less than 5% (467,508).
Hamadanian et al. achieved complete removal of methyl orange (MO) using Co-doped
TiO2 under a treatment time ranging between 90 and 420 minutes, depending on the mole% of Co-doped TiO2 (467,507). Amadelli et al. used 0.5 mole% Co-doped TiO2 with visible light exposure for the purpose of reducing 0.8 mM Bisphenol A to approximately 78% within 8 hours, where after 3 hours of photodegradation time, treatment was about 58%, with only 6% removal after 8 hours of mM 4-chlorophenol
(467,509).
8.3.2.2 FE-DOPED TIO2
Fe-doped TiO2 was effectively capable of removing a higher percentage of AO7, as compared with Co-doped TiO2. Similar to the effects of Fe-doped TiO2, treatment
204 efficiency is related to the initial concentration of dye, as the efficiency is inversely related to the initial concentration. When the initial concentration of Fe-doped TiO2 was
24 mg/L, the treatment efficiency is linearly related to the dose of Fe-doped TiO2.
Treatment efficiency was the lowest when the dose was 0.25 g/L (88.49%) as compared with 1 g/L (98.52%). This trend is slightly different in Co-doped TiO2, where the treatment efficiency decreased with an increasing dose of catalyst.
At an initial dye concentration of 34 mg/L, a similar patterned occurred as with the initial dye concentration of 24 mg/L, where the highest removal efficiency was (88.67%) at 1 g/L dose applied, and lowest at 0.25 g/L applied (71.39%). Finally, with the initial concentration 44 mg/L, the pattern of the previous initial dye concentration does not hold for this concentration, as it was found that the highest treatment efficiency occurred at 1 g/L (73.85%), as compared with the lowest (68.90%) which was found at 0.5 g/L.
Nevertheless, when one compares the treatment efficiency for an individual Fe-doped
TiO2 dose across the three initial concentrations, it was found that the initial concentration was inversely related to the treatment efficiency. This is very similar to what was found with Co-doped TiO2. This mole% used within this study has been successful in treating waste efficiency. Wang et al. discovered that 2 mole% Fe-doped
TiO2 had the best success in treating 20 uM (6.54 mg/L) methyl orange (510).
Possible explanations for the maximum removal may be attributed to the characteristics of Fe-doped TiO2. When authors used diffuse reflectance spectra (DRS) to analyze Fe- doped TiO2, it has been determined that the absorbance ranges from 400 to 600 nm, where Ambrus et al. reported a wavelength of 600 nm (511), Vijayan et al. between 410-
445 nm (512), and Sojic et al. recorded a range between 400 and 590 nm (513). A high
205 absorbance range can be attributed to the red light shift confirming that it is activated within the visible region (512, 514). The light source range of 200 and 800 nm is suitable for the absorbance range reported for Fe-doped TiO2, thereby allowing for activation by the light source used within this experiment.
However, there has not been an ideal mole% for all given circumstances. While 2 mole% has been the most effective in some cases, other authors have found ideal conditions at other molar percentages. Several authors such as Cong et al. treating
Rhoadmine B (514), Vijayan et al. treating 2,4,6-trichlorophenol (467, 512) and Wang et al. treating methanol experienced the highest efficiency at 0.5 mole% (467, 515). While this study does not compare other mole%, it does consider the effects of varying the nanocatalyst dose.
8.3.2.3 ANALYSIS OF CO-DOPED AND FE-DOPED TIO2 USING KINETICS
Following treatment efficiency, it was necessary to determine the kinetic values for Co- doped TiO2 and Fe-doped TiO2. Gracien et al. and others (516-519) described the use of photocatalytic degradation by means of the Langmuir-Hinshelwood model for the purpose of developing a kinetic constant. The purpose was to develop a theoretical rate constant (k) and the Langmuir-Hinshelwood constant (Kads). The methodology to calculate rate constants, kinetic constants, both experimental and theoretical rate constant for each dose, and the Langmuir-Hinshelwood constant were extracted from (516). One will begin with considering the principles of first order kinetics:
r = - dC/dt = kexpC [62]
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Through integration, the previous equation [62] can also be rearranged and linearized:
1/kexp = 1/kKads + C/k [63]
The rearrangement of [63] is now in y-intercept form. By being in y-intercept form, equation 6 was used to develop both k and Kads, provided that one knows kexp and the C.
One will employ the following techniques to be able to develop the unknowns. First, one will calculate –ln (C/Co) for every data point within each set of experiments. Having calculated –ln (C/Co) for each sample point during the experiment, the calculated points were then plotted as being –ln(C/Co) versus treatment time. Following this execution, an equation was developed from the plot.
After calculating the experimental rate constant (kexp), one must then calculate 1/kexp, as seen within [63]. After calculating 1/kexp, 1/kexp versus initial dye concentrations was plotted for each dose of nanocatalyst. These plots are shown in Figures CLV and CLVI and are separated by nanocatalyst. From these plots, one will generate an equation of the line. Out of the equation, one can generate the experimental kinetic rate constant from the experiment (kexp) by considering the slope of the line (1/k). Having discovered the slope, one can now calculate the Langmuir-Hinshelwood constant for each dose of each nanocatalyst. By knowing the y-intercept of the line (1/kKads) and the slope of the line, one can calculate Kads, by taking the quotient of the slope of the line and the y-intercept.
Tables XIX and XX summarize the k, Kads, kexp constants for each dose of Co-doped
TiO2 and also Fe-doped TiO2. Following the development of the kinetic constant, one can then develop a decolorization rate constant for the entire system. This is done by multiplying the kinetic constant by the initial concentration of the dye. Having completed 207 this, one can conclude that for Co-doped TiO2, the range of rate constants (r) (ppm/min) range from 0.01232 to 0.0192 for 0.25 g/L, 0.01276 to 0.0178 for 0.5 g/L, and with a k
-1 2 constant of 0.0041 (min ), while 0.5 g/L is at 0.1901 (r = .941) and a k-constant = 0.006.
With the exception of 0.5 mg/L dose, Co-doped TiO2 has the highest rates of degradation at 24 mg/L/ On the other hand, Fe-doped TiO2 has a range of rate constants between
0.02482 and 0.3036 for 0.25 g/L, 0.03036 to 0.03528 for 0.5 g/L, and 0.033 to 0.05712, with the exception of 0.25 mg/L dose, Fe-doped TiO2 has the highest rates of degradation at 24. This has also been seen evident when one considers the Langmuir-Hinshelwood constants.
The application of the Langmuir-Hinshelwood coefficients show a high correlation (r2
2 > 0.90) for all doses with the exception of 0.50 g/L Co-doped TiO2 (r = 0.8239) and
2 0.25 g/L Fe-doped TiO2 (r = 0.8432). One can see the results within Figures CLV and
CLVI for Fe-doped and Co-doped TiO2 respectively, along with Table XX.
Finally, if one completes an analysis between the Fe-doped TiO2 and Co-doped TiO2, it has been determined that the kinetic rate constants were higher when a wastewater received treatment by Fe-doped TiO2 as compared with Co-dopedTiO2. The highest variations were when at an initial dye concentration of 24 mg/L and a Fe-doped TiO2 dose of 0.5, the experimental kinetic coefficient is over two and a half times higher with
-3 -3 -1 Fe-doped TiO2 as compared with Co-doped TiO2 (14.70 x 10 and 5.70 x10 min respectively). However, great disparities do not appear until one approaches the 1 g/L dose. At 1 g/L dose, and an initial concentration of 24 mg/L, the difference in kinetic
-3 -1 constants was almost five times (Fe-doped TiO2 23.80 x 10 min , Co-doped TiO2 5.10
-3 -1 x 10 min ), while at 34 mg/L, the difference was over four times (Fe-doped TiO2: 12.30
208
-3 -1 -3 -1 x 10 min , Co-doped TiO2: 3.00 x 10 min ). This trend is also found among the degradation rate constants. On the contrary, the smaller variations between Fe-doped
TiO2 and Co-doped TiO2 treatment efficiency was found when the dose was 0.25 g/L, where the lowest variation between kinetic rate constants for the catalyst was at an initial
-3 -1 concentration of 24 mg/L, about 1.5 times (Co-doped TiO2: 8.00 x 10 min , Fe-doped
-3 -1 TiO2: 11.70 x 10 min ). Overall, at 2 mole% treating Acid Orange 7 dye wastewater,
Fe-doped TiO2 had better treatment efficiency as compared with Co-doped TiO2.
8.4 CONCLUSIONS
Overall, it was found that the ultraviolet, visible, and infrared light source was sufficient in activating the nanocatalyst to treat Acid Orange 7 wastewater with an inexpensive reactor setup. Specifically, 2 mole% Fe-doped TiO2 was the most effective in removing Acid Orange from synthetic dye wastewater at various nanocatalyst doses and various initial wastewater concentrations. Removal efficiency was related to the initial concentration, as both catalyst recorded its highest treatment efficiency at 24 mg/L and lowest at 44 mg/L. For Fe-doped TiO2, treatment efficiency was best when the dose was
1 g/L, as compared to Co-doped TiO2, when the treatment efficiency was better at 0.25 and 0.5 g/L. Having a higher dose of Co-doped TiO2 proved to be ineffective as treatment efficiency was the lowest for all concentrations of dye wastewater. Additional studies can be conducted to determine whether or not photocatalysis can be completed using other light sources with only incandescent filaments, along with other nanocatalysts.
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CHAPTER 9
CONCLUSIONS
9.1 CONCLUSION
Based on the research, the following conclusions have been made:
9.1.1 ELECTROCOAGULATION
When treating Acid Yellow 11, electrocoagulation can be best expressed by using
Response Surface Methodology, where it was indicated that Alum and Ferric Chloride have the same statistically significant variables (time, current density, and time2). In previous chemical coagulation experiments in literature, alum and ferric chloride had similar treatment proficiencies. In addition, it was determined that the highest treatment removal occurred at a pH of 4 for both Alum and Ferric Chloride. Statistically significant variables were current density and pH*dose. In regards to kinetics, it was discovered that both Alum and Ferric Sulfate additions were best described by zero-order kinetics, while
Ferric Sulfate is more variable across zero, first, and second order kinetics. Finally,
Lagergren models are sufficient in describing the adsorption (r2>.9), where the majority of the top removal efficiency is described by the pseudo-first order kinetic rate. 210
When comparing all three dyes, a transformation was used for the purpose of ensuring the data does not fail the Anderson-Darling test for normality, along with avoiding the statistically significance of the lack-of-fit according the ANOVA table. Using a Johnson transformation, it was found that dye type, current density, and the interaction between initial pH and current density were very statistically significant. The statistical significance for dye type and current density were found being consistent with literature.
Also, the replicates of the center points provided a very low experimental error, indicating that while using different sized electrodes (although the experimental error was less than 10% for the average) and two different reactors. This means that the results of the experiment are very reliable.
9.1.2 PHOTO-OXIDATION
From photo-oxidation experiments, it was found that iron-doped TiO2 has better color removal for dye wastewater than Co-doped TiO2. In addition, as Iron-doped TiO2 dose increases, color removal for dye wastewater increases. At low concentrations of dye
wastewater, Co-doped TiO2 has better color removal than high concentrations of dye wastewater. Langmuir-Hinshelwood coefficients were calculated for all dye wastewater experiments, where the r2 values were greater than 0.80. The experiment was capable of achieving high treatment removal efficiencies as lower irradiation times for Co-doped and Fe-doped titanium dioxide were required for decolorizing dye wastewater. With a longer irradiation time, it could be determined that complete decolorization could occur for all Fe-doped and Co-doped TiO2.
211
CHAPTER 9.2: RECOMMEDNATIONS
In regards to electrocoagulation, toxicity experiments should be completed to determine how efficient electrocoagulation is removing toxic components. In addition, it was shown that electrocoagulation best bits the pseudo-second order Lagergren model when using iron-based electrodes. Therefore, research could be completed to confirm the trends using the same conditions in the experiment, with the exception of using iron-based electrodes instead of aluminum.
In regards to photo-oxidation, it would be viable to consider the effects of various electrode types beyond aluminum, other light sources for photo-oxidation, and most- importantly, find a method of reducing the effects of salt and other potential ions on the treatment efficiency by nanocatalyst when using two-stage treatment.
212
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of manganese, chromium and cobalt-doped anatase titanium dioxide nanoporous
electrodes produced by re-anodization method. Thin Solid Films. 2007, 515, 5287-5297.
517. Adan, C.; Bahamonde, A.; Fernandez-Garcia, M.; Martinez-Arias, A. Structure and
activity of nanosized iron-doped anatase TiO2 catalysts for phenol photocatalytic
degradation. Appl. Catal. B. 2009, 72, 11-17.
518. Wang, C.; Li, Q.; Wang, R. D.Synthesis and characterization of mesoporous iron-doped
TiO2. J. Mater. Sci. 2004, 39, 1899-1901.
519. Cunningham, J.; Alsayyed, G.; Srijaranai, S. In Adsorption of Model Pollutants Onto
TiO2 Particles in Relation to Photoremediation of Contaminated Water, Symposium on
Aquatic and Surface Photochemistry, at the 203rd National Meeting of the American-
Chemical Society, San Francisco (California), April, 5-10 1992; Helz, GR; Zepp, RG;
Crosby, DG., Eds.; American Chemical Society, 1994.
283
APPENDICES
284
APPENDIX A EXPERIMENTAL EQUIPMENT SET-UP
285
Figure I. Electrocoagulation Reactor Setup.
286
Figure II. Inside Electrocoagulant Reactor with electrodes.
287
Figure III. New sacrificial Aluminum electrodes (left: anode; right: cathode).
288
Figure IV. Plexiglass panel.
289
Figure V. YiHua DC Power Supply.
290
Figure VI. Spent electrodes after treatment (left: cathode; right: anode). Notice that cathode has been corroded due to hydrogen gas, while the anode has been oxidized. Electodes have been used a maximum of 60 minutes treatment time.
291
Figure VII. Photo-oxidation Reactor 1 beaker and UVB lamp.
292
Figure VIII. Photo-oxidation Reactor 1 beaker and stirrer.
293
Figure IX. Photo-oxidation Reactor 1 lamp ontop of reactor (light is turned off).
294
Figure X. Photo-oxidation Reactor 1 lamp ontop of reactor (light is turned on).
295
Figure XI. Photo-oxidation Reactor 1 beaker and UVB lamp.
296
Figure XII. Photo-oxidation Reactor 2 beaker and sitrrer.
297
Figure XIII. Photo-oxidation Reactor 2 lamp ontop of reactor (light is turned off).
298
Figure XIV. Photo-oxidation Reactor 1 lamp ontop of reactor (light is turned on).
299
Figure XV. Synthesized nanocatalysts (left: Fe-doped TiO2; right: Co-doped TiO2).
300
APPENDIX B RUN PROTOCOLS
301
Run Current Order Time Density pH Dose 1 0 -1 -1 -1 2 5 -1 -1 -1 3 10 -1 -1 -1 4 15 -1 -1 -1 5 20 -1 -1 -1 6 30 -1 -1 -1 7 0 -1 -1 0 8 5 -1 -1 0 9 10 -1 -1 0 10 15 -1 -1 0 11 20 -1 -1 0 12 30 -1 -1 0 13 0 -1 -1 1 14 5 -1 -1 1 15 10 -1 -1 1 16 15 -1 -1 1 17 20 -1 -1 1 18 30 -1 -1 1 19 0 -1 0 -1 20 5 -1 0 -1 21 10 -1 0 -1 22 15 -1 0 -1 23 20 -1 0 -1 24 30 -1 0 -1 25 0 -1 0 0 26 5 -1 0 0 27 10 -1 0 0 28 15 -1 0 0 29 20 -1 0 0 30 30 -1 0 0 31 0 -1 0 1 32 5 -1 0 1 33 10 -1 0 1 34 15 -1 0 1 35 20 -1 0 1 36 30 -1 0 1
302
37 0 0 -1 -1 38 5 0 -1 -1 39 10 0 -1 -1 40 15 0 -1 -1 41 20 0 -1 -1 42 30 0 -1 -1 43 0 0 -1 0 44 5 0 -1 0 45 10 0 -1 0 46 15 0 -1 0 47 20 0 -1 0 48 30 0 -1 0 49 0 0 -1 1 50 5 0 -1 1 51 10 0 -1 1 52 15 0 -1 1 53 20 0 -1 1 54 30 0 -1 1 55 0 0 0 -1 56 5 0 0 -1 57 10 0 0 -1 58 15 0 0 -1 59 20 0 0 -1 60 30 0 0 -1 61 0 0 0 0 62 5 0 0 0 63 15 0 0 0 64 20 0 0 0 65 30 0 0 0 66 0 0 0 1 67 5 0 0 1 68 10 0 0 1 69 15 0 0 1 70 20 0 0 1 71 30 0 0 1 72 0 1 -1 -1 73 5 1 -1 -1 74 10 1 -1 -1
303
75 15 1 -1 -1 76 20 1 -1 -1 77 30 1 -1 -1 78 0 1 -1 0 79 5 1 -1 0 80 10 1 -1 0 81 15 1 -1 0 82 20 1 -1 0 83 30 1 -1 0 84 0 1 -1 1 85 5 1 -1 1 86 10 1 -1 1 87 15 1 -1 1 88 20 1 -1 1 89 30 1 -1 1 90 0 1 0 -1 91 5 1 0 -1 92 10 1 0 -1 93 15 1 0 -1 94 20 1 0 -1 95 30 1 0 -1 96 0 1 0 0 97 5 1 0 0 98 10 1 0 0 99 15 1 0 0 100 20 1 0 0 101 30 1 0 0 102 0 1 0 1 103 5 1 0 1 104 10 1 0 1 105 15 1 0 1 106 20 1 0 1 107 30 1 0 1
Key:
Current Density: -1 = 18.69 A/m2; 0= 24.92 A/m2; 31.15 A/m2 pH: -1 = 4; 0 = 7
304
Dose: -1 = 5 mg/L; 0 = 10 mg/L ; 1 = 15 mg/L
Table I. Run Protocol for RSM Model for Alum.
305
Current StdOrder Time Density pH Dose 1 0 -1 -1 -1 2 5 -1 -1 -1 3 10 -1 -1 -1 4 15 -1 -1 -1 5 20 -1 -1 -1 6 30 -1 -1 -1 7 0 -1 -1 0 8 5 -1 -1 0 9 10 -1 -1 0 10 15 -1 -1 0 11 20 -1 -1 0 12 30 -1 -1 0 13 0 -1 -1 1 14 5 -1 -1 1 15 10 -1 -1 1 16 15 -1 -1 1 17 20 -1 -1 1 18 30 -1 -1 1 19 0 -1 0 -1 20 5 -1 0 -1 21 10 -1 0 -1 22 15 -1 0 -1 23 20 -1 0 -1 24 30 -1 0 -1 25 0 -1 0 0 26 5 -1 0 0 27 10 -1 0 0 28 15 -1 0 0 29 20 -1 0 0 30 30 -1 0 0 31 0 -1 0 1 32 5 -1 0 1 33 10 -1 0 1 34 15 -1 0 1 35 20 -1 0 1 36 30 -1 0 1 37 0 0 -1 -1 38 5 0 -1 -1
306
39 15 0 -1 -1 40 20 0 -1 -1 41 30 0 -1 -1 42 0 0 -1 0 43 5 0 -1 0 44 15 0 -1 0 45 20 0 -1 0 46 30 0 -1 0 47 0 0 -1 1 48 5 0 -1 1 49 15 0 -1 1 50 20 0 -1 1 51 30 0 -1 1 52 0 0 0 -1 53 5 0 0 -1 54 10 0 0 -1 55 15 0 0 -1 56 20 0 0 -1 57 30 0 0 -1 58 0 0 0 0 59 5 0 0 0 60 10 0 0 0 61 15 0 0 0 62 20 0 0 0 63 30 0 0 0 64 0 0 0 1 65 5 0 0 1 66 10 0 0 1 67 15 0 0 1 68 20 0 0 1 69 30 0 0 1 70 0 1 -1 -1 71 5 1 -1 -1 72 10 1 -1 -1 73 15 1 -1 -1 74 20 1 -1 -1 75 30 1 -1 -1 76 0 1 -1 0 77 5 1 -1 0 78 10 1 -1 0 79 15 1 -1 0
307
80 20 1 -1 0 81 30 1 -1 0 82 0 1 -1 1 83 5 1 -1 1 84 10 1 -1 1 85 15 1 -1 1 86 20 1 -1 1 87 30 1 -1 1 88 0 1 0 -1 89 5 1 0 -1 90 10 1 0 -1 91 15 1 0 -1 92 20 1 0 -1 93 30 1 0 -1 94 0 1 0 0 95 5 1 0 0 96 10 1 0 0 97 15 1 0 0 98 20 1 0 0 99 30 1 0 0 100 0 1 0 1 101 5 1 0 1 102 10 1 0 1 103 15 1 0 1 104 20 1 0 1 105 30 1 0 1
Key:
Current Density: -1 = 18.69 A/m2; 0= 24.92 A/m2; 31.15 A/m2 pH: -1 = 4; 0 = 7
Dose: -1 = 5 mg/L; 0 = 10 mg/L ; 1 = 15 mg/L
Table II. Run Protocol for RSM Model for Fe2(SO4)3.
308
Run Time Current Density pH Dose 1 0 -1 -1 -1 2 5 -1 -1 -1 3 10 -1 -1 -1 4 15 -1 -1 -1 5 20 -1 -1 -1 6 30 -1 -1 -1 7 0 -1 -1 0 8 5 -1 -1 0 9 10 -1 -1 0 10 15 -1 -1 0 11 20 -1 -1 0 12 30 -1 -1 0 13 0 -1 -1 1 14 5 -1 -1 1 15 10 -1 -1 1 16 15 -1 -1 1 17 20 -1 -1 1 18 30 -1 -1 1 19 0 -1 0 -1 20 5 -1 0 -1 21 10 -1 0 -1 22 15 -1 0 -1 23 20 -1 0 -1 24 30 -1 0 -1 25 0 -1 0 0 26 5 -1 0 0 27 10 -1 0 0 28 15 -1 0 0 29 20 -1 0 0 30 30 -1 0 0 31 0 -1 0 1 32 5 -1 0 1 33 10 -1 0 1 34 15 -1 0 1 35 20 -1 0 1 36 30 -1 0 1 37 0 0 0 -1 38 5 0 0 -1 39 10 0 0 -1 40 15 0 0 -1
309
41 20 0 0 -1 42 30 0 0 -1 43 0 0 0 0 44 5 0 0 0 45 10 0 0 0 46 15 0 0 0 47 20 0 0 0 48 30 0 0 0 49 0 0 0 1 50 5 0 0 1 51 10 0 0 1 52 15 0 0 1 53 20 0 0 1 54 30 0 0 1 55 0 0 0 -1 56 5 0 0 -1 57 10 0 0 -1 58 15 0 0 -1 59 20 0 0 -1 60 30 0 0 -1 61 0 0 0 0 62 5 0 0 0 63 10 0 0 0 64 15 0 0 0 65 20 0 0 0 66 30 0 0 0 67 0 0 0 1 68 5 0 0 1 69 10 0 0 1 70 15 0 0 1 71 20 0 0 1 72 30 0 0 1 73 0 1 0 -1 74 5 1 0 -1 75 10 1 0 -1 76 15 1 0 -1 77 20 1 0 -1 78 30 1 0 -1 79 0 1 0 0 80 5 1 0 0 81 10 1 0 0
310
82 15 1 0 0 83 20 1 0 0 84 30 1 0 0 85 0 1 0 1 86 5 1 0 1 87 10 1 0 1 88 15 1 0 1 89 20 1 0 1 90 30 1 0 1 91 0 1 0 -1 92 5 1 0 -1 93 10 1 0 -1 94 15 1 0 -1 95 20 1 0 -1 96 30 1 0 -1 97 0 1 0 0 98 5 1 0 0 99 10 1 0 0 100 15 1 0 0 101 20 1 0 0 102 30 1 0 0 103 0 1 0 1 104 5 1 0 1 105 10 1 0 1 106 15 1 0 1 107 20 1 0 1 108 30 1 0 1
Table III. Run Protocol for RSM Model for FeCl3.
311
Dose Current Run pH Coagulant (mg/L) Density 1 -1 blank blank -1 2 -1 -1 -1 -1 3 -1 -1 0 -1 4 -1 -1 1 -1 5 -1 0 -1 -1 6 -1 0 0 -1 7 -1 0 1 -1 8 -1 1 -1 -1 9 -1 1 0 -1 10 -1 1 1 -1 11 -1 blank blank 0 12 -1 -1 -1 0 13 -1 -1 0 0 14 -1 -1 1 0 15 -1 0 -1 0 16 -1 0 0 0 17 -1 0 1 0 18 -1 1 -1 0 19 -1 1 0 0 20 -1 1 1 0 21 -1 blank blank 1 22 -1 -1 -1 1 23 -1 -1 0 1 24 -1 -1 1 1 25 -1 0 -1 1 26 -1 0 0 1 27 -1 0 1 1 28 -1 1 -1 1 29 -1 1 0 1 30 -1 1 1 1 31 0 blank blank -1 32 0 -1 -1 -1 33 0 -1 0 -1 34 0 -1 1 -1 35 0 0 -1 -1 36 0 0 0 -1 37 0 0 1 -1 38 0 1 -1 -1 39 0 1 0 -1
312
40 0 1 1 -1 41 0 blank blank 0 42 0 -1 -1 0 43 0 -1 0 0 44 0 -1 1 0 45 0 0 -1 0 46 0 0 0 0 47 0 0 1 0 48 0 1 -1 0 49 0 1 0 0 50 0 1 1 0 51 0 blank blank 1 52 0 -1 -1 1 53 0 -1 0 1 54 0 -1 1 1 55 0 0 -1 1 56 0 0 0 1 57 0 0 1 1 58 0 1 -1 1 59 0 1 0 1 60 0 1 1 1
Key: pH: -1 = 4; 0 = 7
Coagulant: -1 = Alum; 0 = Fe2(SO4)3; 1 = FeCl3
Dose: -1 = 5 mg/L; 0 = 10 mg/L ; 1 = 15 mg/L
Current Density: -1 = 18.69 A/m2; 0= 24.92 A/m2; 31.15 A/m2
Table IV. Run Protocol for Acid Yellow 11.
313
Dye Cur. Coagulant Run Dye pH weight Den Coagulant Dose 1 0 1 -1 0 -1 0 2 0 1 -1 0 1 0 3 0 0 0 0 0 0 4 0 1 0 0 -1 -1 5 0 1 0 0 -1 1 6 1 0 0 -1 -1 0 7 1 0 1 0 0 1 8 0 0 0 0 0 0 9 1 -1 0 -1 0 0 10 0 0 0 0 0 0 11 -1 0 1 0 0 -1 12 1 0 -1 0 0 -1 13 0 1 1 0 1 0 14 0 0 -1 1 0 1 15 0 -1 -1 0 -1 0 16 -1 0 1 0 0 1 17 0 0 -1 1 0 -1 18 0 1 0 0 1 1 19 1 1 0 -1 0 0 20 1 0 -1 0 0 1 21 -1 0 0 1 1 0 22 1 0 1 0 0 -1 23 0 0 1 1 0 1 24 1 1 0 1 0 0 24 0 -1 1 0 -1 0 25 0 -1 1 0 1 0 26 -1 -1 0 -1 0 0 27 0 0 1 -1 0 -1 28 0 0 1 -1 0 1 29 0 -1 -1 0 1 0 30 0 0 0 0 0 0 31 0 1 0 0 1 -1 32 1 0 0 1 1 0 33 -1 1 0 -1 0 0 34 -1 0 0 -1 -1 0 35 0 -1 0 0 -1 -1 36 1 0 0 1 -1 0 37 0 0 0 0 0 0 38 1 -1 0 1 0 0
314
39 0 0 -1 -1 0 1 40 0 0 -1 -1 0 -1 41 -1 0 -1 0 0 -1 42 -1 -1 0 1 0 0 43 0 -1 0 0 -1 1 44 -1 0 -1 0 0 1 45 -1 1 0 1 0 0 46 0 -1 0 0 1 1 47 -1 0 0 -1 1 0 48 0 0 0 0 0 0 49 1 0 0 -1 1 0 50 0 1 1 0 -1 0 51 0 -1 0 0 1 -1 52 -1 0 0 1 -1 0 53 0 0 1 1 0 -1
Key:
Dye: -1 = Acid Yellow 11; 0 = Acid Orange 7; 1 = Naphthol Green B pH: -1 = 4; 0 = 7
Dye weight: -1 = 20 mg; 0 = 33 mg; 1 = 45 mg
Current Density: -1 = 18.69 A/m2; 0= 24.92 A/m2; 31.15 A/m2
Coagulant: -1 = Alum; 0 = Fe2(SO4)3; 1 = FeCl3
Dose: -1 = 5 mg/L; 0 = 10 mg/L ; 1 = 15 mg/L
Table V. Run Protocol for Response Surface Methodology.
315
Concentration Dose Run (mg/L) Photo-oxidant (g/L) 1 -1 -1 -1 2 -1 -1 0 3 -1 -1 1 4 -1 0 -1 5 -1 0 0 6 -1 0 1 7 -1 blank blank 8 0 -1 -1 9 0 -1 0 10 0 -1 1 11 0 0 -1 12 0 0 0 13 0 0 1 14 0 blank blank 15 1 -1 -1 16 1 -1 0 17 1 -1 1 18 1 0 -1 19 1 0 0 20 1 0 1 21 1 blank blank
Key:
Concentration: -1 = 24 mg/L; 0 = 34 mg/L; 1 = 44 mg/L
Photo-oxidation: -1 = Co-doped; 0 = Fe-doped
Dose: -1 = 0.25 g/L; 0 = 0.5 g/L: 1 = 1 g/L
Table VI. Run Protocol for Photo-oxidation experiments.
316
APPENDIX C RESULTS FROM ELECTROCOAGULATION EXERIMENT WITH ACID YELLOW 11
317
30
25
20 Control (pH = 4) 15 Alum = 5 mg/L 10 Alum = 10 mg/L
Concentration (mg/L)Concentration Alum = 15 mg/L 5
0 0 5 10 15 20 30 Time (mins)
Figure LVII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =4, coagulant = Alum, Current Density = 18.69 A/m2).
318
30
25
20 Control (pH = 7) 15 Alum = 5 mg/L 10 Alum = 10 mg/L
Concentration (mg/L)Concentration Alum = 15 mg/L 5
0 0 5 10 15 20 30 Time (mins)
Figure LVIII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =7, coagulant = Alum, Current Density = 24.92 A/m2).
319
30
25 pH = 4, Alum = 5 mg/L 20 pH = 4, Alum = 10 mg/L pH = 4, Alum = 15 mg/L 15 pH =7, Alum = 5 mg/L 10 pH = 7, Alum = 10 mg/L
Concentration (mg/L)Concentration pH = 7, Alum = 15 mg/L 5 Control (pH = 4) 0 Control (pH = 7) 0 5 10 15 20 30 Time (mins)
Figure LIX. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Alum, Current Density = 18.69 A/m2).
320
30
25
20 Alum = 5 mg/L 15 Alum = 10 mg/L 10 Alum = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 4) 5
0 0 5 10 15 20 30 Time (mins)
Figure LX. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =4, coagulant = Alum, Current Density = 24.92 A/m2).
321
30
25
20 Alum = 5 mg/L 15 Alum = 10 mg/L 10 Alum = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 7) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXI. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =7, coagulant = Alum, Current Density = 24.92 A/m2).
322
30
25 pH = 4, Alum = 5 mg/L 20 pH = 4, Alum = 10 mg/L pH = 4, Alum = 15 mg/L 15 pH =7, Alum = 5 mg/L 10 pH = 7, Alum = 10 mg/L
Concentration (mg/L)Concentration pH = 7, Alum = 15 mg/L 5 Control (pH = 4) 0 Control (pH = 7) 0 5 10 15 20 30 Time (mins)
Figure LXII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Alum, Current Density = 24.92 A/m2).
323
30
25
20 Alum = 5 mg/L 15 Alum = 10 mg/L 10 Alum = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 4) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXIII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =4, coagulant = Alum, Current Density = 31.15 A/m2).
324
30
25
20 Alum = 5 mg/L 15 Alum = 10 mg/L 10 Alum = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 7) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXIV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, pH =7, coagulant = Alum, Current Density = 31.15 A/m2).
325
30
25 pH = 7, Alum = 5 mg/L 20 pH = 7, Alum = 10 mg/L pH = 7, Alum = 15 mg/L 15 pH =4, Alum = 5 mg/L 10 pH = 4, Alum = 10 mg/L
Concentration (mg/L)Concentration pH = 4, Alum = 15 mg/L 5 Control (pH = 4) 0 Control (pH = 7) 0 5 10 15 20 30 Time (mins)
Figure LXV. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Alum, Current Density = 31.15 A/m2).
326
12
10
8
6 Alum = 5 mg/L Alum = 10 mg/L 4
Alum = 15 mg/L Concentration (mg/L)Concentration
2
0 18.69 24.92 31.15 Current Density (A/m^2)
Figure LXVI. Normalized plot Acid Yellow 11 Concentration vs Current Density at Treatment Time = 30 minutes (AY = 25 mg/L, pH = 4, coagulant = Alum).
327
12
10
8
6 Alum = 5 mg/L Alum = 10 mg/L 4
Alum = 15 mg/L Concentration (mg/L)Concentration
2
0 18.69 24.92 31.15 Current Density (A/m^2)
Figure LXVII. Normalized plot Acid Yellow 11 Concentration vs Current Density at Treatment Time = 30 minutes (AY = 25 mg/L, pH = 7, coagulant = Alum).
328
30
25 pH = 4, Fe2(SO4)3 = 5 mg/L 20 pH = 4, Fe2(SO4)3 = 15 10 mg/L
10 pH = 4, Fe2(SO4)3 = Concentration (mg/L)Concentration 15 mg/L 5
0 Control (pH = 4) 0 5 10 15 20 30 Time (mins)
Figure LXVIII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =4, coagulant = Fe2SO4)3, Current Density = 18.69 A/m ).
329
30
25 pH = 7, Fe2(SO4)3 = 5 mg/L 20 pH = 7, Fe2(SO4)3 = 15 10 mg/L
10 pH = 7, Fe2(SO4)3 = Concentration (mg/L)Concentration 15 mg/L 5
0 Control (pH = 7) 0 5 10 15 20 30 Time (mins)
Figure LXIX. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, 2 pH =7, coagulant = Fe2SO4)3, Current Density = 19.38 A/m ).
330
30 pH = 4, Fe2(SO4)3 = 5 25 mg/L pH = 4, Fe2(SO4)3 = 10 mg/L 20 pH = 4, Fe2(SO4)3 = 15 mg/L 15 pH = 7, Fe2(SO4)3 = 5 mg/L 10 pH = 7, Fe2(SO4)3 = 10 mg/L Concentration (mg/L)Concentration pH = 7, Fe2(SO4)3 = 5 15 mg/L Control (pH = 4) 0 0 5 10 15 20 30 Control (pH = 7) Time (mins)
Figure LXX. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Fe2(SO4)3, Current Density = 18.69 A/m2).
331
30 pH = 4, Fe2(SO4)3 = 5
25 mg/L
20 pH = 4, Fe2(SO4)3 = 10 15 mg/L
10
pH = 4, Fe2(SO4)3 = 15 Concentration (mg/L)Concentration mg/L 5
0 0 5 15 20 30 Control (pH = 4) Time (mins)
Figure LXXI. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, 2 pH =4, coagulant = Fe2SO4)3, Current Density = 24.92 A/m ). [Please Note: Values at 10 minutes were not collected for these experiments].
332
30 pH = 7, Fe2(SO4)3 = 5
25 mg/L
20 pH = 7, Fe2(SO4)3 = 10 15 mg/L
10
pH = 7, Fe2(SO4)3 = 15 Concentration (mg/L)Concentration mg/L 5
0 0 5 10 15 20 30 Control (pH = 7) Time (mins)
Figure LXXII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, 2 pH =7, coagulant = Fe2SO4)3, Current Density = 24.92 A/m ).
333
30 pH = 4, Fe2(SO4)3 = 5 mg/L
25 pH = 4, Fe2(SO4)3 = 10
mg/L 20 pH = 4, Fe2(SO4)3 = 15 mg/L
15 pH = 7, Fe2(SO4)3 = 5 mg/L
pH = 7, Fe2(SO4)3 = 10 10
mg/L Concentration (mg/L)Concentration pH = 7, Fe2(SO4)3 = 15 5 mg/L Control (pH = 4) 0 0 5 10 15 20 30 Control (pH = 7) Time (mins)
Figure LXXIII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Fe2(SO4)3, Current Density = 24.92 A/m2).
334
30
25
pH =4 , Fe2(SO4)3 = 5 20 mg/L
15 pH =4 , Fe2(SO4)3 = 10 mg/L 10 pH =4 , Fe2(SO4)3 = 15
mg/L Concentration (mg/L)Concentration 5 Control (pH = 4)
0 0 5 10 15 20 30 Time (mins)
Figure LXXIV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =4, coagulant = Fe2SO4)3, Current Density = 31.15 A/m ).
335
30
25
pH =7 , Fe2(SO4)3 = 5 20 mg/L
15 pH =7 , Fe2(SO4)3 = 10 mg/L 10 pH =7 , Fe2(SO4)3 = 15
mg/L Concentration (mg/L)Concentration 5 Control (pH = 7)
0 0 5 10 15 20 30 Time (mins)
Figure LXXV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 mg/L, 2 pH =7, coagulant = Fe2SO4)3, Current Density = 31.15 A/m ).
336
30 pH =7 , Fe2(SO4)3 = 5
25 mg/L
pH =7 , Fe2(SO4)3 = 10 20 mg/L
15 pH =7 , Fe2(SO4)3 = 15 mg/L 10 pH =4 , Fe2(SO4)3 = 5
mg/L Concentration (mg/L)Concentration 5 pH =4 , Fe2(SO4)3 = 10 mg/L 0 pH =4 , Fe2(SO4)3 = 15 0 5 10 15 20 30 mg/L Time (mins)
Figure LXXVI. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = Fe2(SO4)3, Current Density = 31.15 A/m2).
337
12
10
8
6 Fe2(SO4)3 = 5 mg/L Fe2(SO4)3 = 10 mg/L 4
Fe2(SO4)3 = 15 mg/L Concentration (mg/L)Concentration
2
0 19.38 25.83 32.29 Current Density (A/m^2)
Figure LXXVII. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 4, coagulant = Fe2(SO4)3).
338
12
10
8
6 Fe2(SO4)3 = 5 mg/L Fe2(SO4)3 = 10 mg/L 4
Fe2(SO4)3 = 15 mg/L Concentration (mg/L)Concentration
2
0 18.69 24.92 31.15 Current Density (A/m^2)
Figure LXXVIII. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 7, coagulant = Fe2(SO4)3).
339
30
25
20 pH =4, FeCl3 = 5 mg/L 15 pH = 4, FeCl3 = 10 mg/L 10 pH = 4, FeCl3 = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 4) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXXIX. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =4, coagulant = FeCl3, Current Density = 18.69 A/m ).
340
15
13
11
9 FeCl3 = 5 mg/L 7 FeCl3 = 10 mg/L 5 FeCl3 = 15 mg/L
Concentration (mg/L)Concentration 3
1
-1 18.69 24.92 31.15 Current Density (A/m^2)
Figure LXXX. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 4, coagulant = FeCl3).
341
12
10
8
6 FeCl3 = 5 mg/L FeCl3 = 10 mg/L 4
FeCl3 = 15 mg/L Concentration (mg/L)Concentration
2
0 18.69 24.92 31.15 Current Density (A/m^2)
Figure LXXXI. Normalized plot Acid Yellow 11 Concentration vs Current Density at
Treatment Time = 30 minutes (AY = 25 mg/L, pH = 7, coagulant = FeCl3).
342
30
25
20 pH = 7, FeCl3 = 5 mg/L 15 pH = 7, FeCl3 = 10 mg/L 10 pH = 7, FeCl3 = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 7) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXXXII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =7, coagulant = FeCl3, Current Density = 18.69 A/m ).
343
30
25 pH = 7, FeCl3 = 5 mg/L 20 pH = 7, FeCl3 = 10 mg/L pH = 7, FeCl3 = 15 mg/L 15 pH =4, FeCl3 = 5 mg/L 10 pH = 4, FeCl3 = 10 mg/L
Concentration (mg/L)Concentration pH = 4, FeCl3 = 15 mg/L 5 Control (pH = 4) 0 Control (pH = 7) 0 5 10 15 20 30 Time (mins)
Figure LXXXIII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = FeCl3, Current Density = 18.69 A/m2).
344
30
25
20 pH =4, FeCl3 = 5 mg/L 15 pH = 4, FeCl3 = 10 mg/L 10 pH = 4, FeCl3 = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 4) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXXXIV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =4, coagulant = FeCl3, Current Density = 24.92 A/m ).
345
30
25
20 pH = 7, FeCl3 = 5 mg/L 15 pH = 7, FeCl3 = 10 mg/L 10 pH = 7, FeCl3 = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 7) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXXXV. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =7, coagulant = FeCl3, Current Density = 24.92 A/m ).
346
30
25 pH = 7, FeCl3 = 5 mg/L 20 pH = 7, FeCl3 = 10 mg/L pH = 7, FeCl3 = 15 mg/L 15 pH =4, FeCl3 = 5 mg/L 10 pH = 4, FeCl3 = 10 mg/L
Concentration (mg/L)Concentration pH = 4, FeCl3 = 15 mg/L 5 Control (pH = 4) 0 Control (pH = 7) 0 5 10 15 20 30 Time (mins)
Figure LXXXV. Normalized plot Acid Yellow 11 Concentration vs Time for various pH 2 and coagulant doses (AY = 25 mg/L, coagulant = FeCl3, Current Density = 24.92 A/m ).
347
30
25
20 pH =4, FeCl3 = 5 mg/L 15 pH = 4, FeCl3 = 10 mg/L 10 pH = 4, FeCl3 = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 4) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXXXVI. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =4, coagulant = FeCl3, Current Density = 31.15 A/m ).
348
30
25
20 pH = 7, FeCl3 = 5 mg/L 15 pH = 7, FeCl3 = 10 mg/L 10 pH = 7, FeCl3 = 15 mg/L
Concentration (mg/L)Concentration Control (pH = 7) 5
0 0 5 10 15 20 30 Time (mins)
Figure LXXXVII. Normalized plot Acid Yellow 11 Concentration vs Time (AY = 25 2 mg/L, pH =7, coagulant = FeCl3, Current Density = 31.15 A/m ).
349
30
25 pH = 7, FeCl3 = 5 mg/L 20 pH = 7, FeCl3 = 10 mg/L pH = 7, FeCl3 = 15 mg/L 15 pH =4, FeCl3 = 5 mg/L 10 pH = 4, FeCl3 = 10 mg/L
Concentration (mg/L)Concentration pH = 4, FeCl3 = 15 mg/L 5 Control (pH = 4) 0 Control (pH = 7) 0 5 10 15 20 30 Time (mins)
Figure LXXXVIII. Normalized plot Acid Yellow 11 Concentration vs Time for various pH and coagulant doses (AY = 25 mg/L, coagulant = FeCl3, Current Density = 31.15 A/m2).
350
1.2
1 Alum = 10, pH = 9
0.8 FeCl3 = 10, pH = 9 Fe2(SO4)3 = 15, pH = 7 0.6 Alum = 10, pH =4
Absorbance 0.4 Fe2(SO4)3 = 5, pH = 7 FeCl3 = 10, pH = 4 0.2 Fe2(SO4)3 = 15, pH = 7 0 Fe2(SO4)3 = 5, pH = 7 0 5 10 15 20 30 Time (mins)
Figure LXXXIX. Normalized plot of Absorbance for Acid Orange 7 vs Time for various pH and coagulant doses (AO7 = 20 mg/L, coagulant = FeCl3, Current Density = 31.15 A/m2).
351
30
25
Alum = 0 mg/L 20 Alum = 5 mg/L
15
C Alum = 10 mg/L 10 Alum = 15 mg/L 5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.6893x + 25.696 0.939 5 mg/L -0.8553x + 25.653 0.9642 10 mg/L -0.7137x + 24.277 0.9669 15 mg/L -0.8137x + 26.242 0.9535
Figure XC. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
352
3.00
2.50
2.00 Alum = 0 mg/L
1.50 Alum = 5 mg/L
1.00 ln(C/Co)
- Alum = 10 mg/L 0.50 Alum = 15 mg/L 0.00 0 10 20 30 40 -0.50
-1.00 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.051x – 0.1443 0.9154 5 mg/L 0.0929x – 0.3541 0.9353 10 mg/L 0.0613x – 0.1297 0.9577 15 mg/L 0.076x – 0.2982 0.9153
Figure XCI. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
353
0.70
0.60
0.50 Alum = 0 mg/L 0.40
Alum = 5 mg/L
0.30 1/C Alum = 10 mg/L
0.20 Alum = 15 mg/L 0.10
0.00 0 10 20 30 40 -0.10 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0043x + 0.0213 0.8767 5 mg/L 0.0174x – 0.0667 0.7495 10 mg/L 0.0066x + 0.0102 0.8705 15 mg/L 0.0107x – 0.0241 0.7371
Figure XCII. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
354
30
25 Alum = 0 mg/L 20
Alum = 5 mg/L
C 15 Alum = 10 mg/L
10 Alum = 15 mg/L
5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.4139x + 23.543 0.9419 5 mg/L -0.5178x + 24.947 0.9536 10 mg/L -0.7145x + 25.316 0.9960 15 mg/L -0.7241x + 26.845 0.9395
Figure XCIII. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
355
2.00
1.50
Alum = 0 mg/L 1.00 Alum = 5 mg/L Alum = 10 mg/L
Alum = 15 mg/L ln(C/Co) - 0.50
0.00 0 10 20 30 40
-0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0237x + 0.0392 0.9736 5 mg/L 0.0306x – 0.0376 0.9815 10 mg/L 0.0605x – 0.1957 0.9301 15 mg/L 0.0539x – 0.2153 0.9196
Figure XCIV. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
356
0.30
0.25
0.20 Alum = 0 mg/L
Alum = 5 mg/L
0.15 1/C Alum = 10 mg/L 0.10 Alum = 15 mg/L 0.05
0.00 0 10 20 30 40 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0014x + 0.04 0.9827 5 mg/L 0.0019x + 0.0352 0.9724 10 mg/L 0.0066x + 0.0042 0.7836 15 mg/L 0.0048x + 0.0149 0.8532
Figure XCV. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
357
30
25
20 Alum = 0 mg/L
15 Alum = 5 mg/L
C Alum = 10 mg/L 10 Alum = 15 mg/L 5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.8226x + 23.513 0.9317 5 mg/L -0.9089x + 25.395 0.9109 10 mg/L -0.8395x + 26.894 0.9601 15 mg/L -0.718x + 25.255 0.953
Figure XCVI. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
358
3.50 3.00 2.50 Alum = 0 mg/L 2.00 Alum = 5 mg/L 1.50
1.00 Alum = 10 mg/L
ln(C/Co) - 0.50 Alum = 15 mg/L 0.00 0 10 20 30 40 -0.50 -1.00 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0906x – 0.192 0.9804 5 mg/L 0.1103x – 0.38 0.9367 10 mg/L 0.0815x – 0.3652 0.9056 15 mg/L 0.0626x – 0.2029 0.8901
Figure XCVII. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
359
1.00
0.80 Alum = 0 mg/L
0.60 Alum = 5 mg/L
Alum = 10 mg/L
0.40 1/C Alum = 15 mg/L 0.20
0.00 0 10 20 30 40 -0.20 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0159x – 0.0418 0.8471 5 mg/L 0.0257x – 0.1091 0.8107 10 mg/L 0.0125x – 0.0391 0.7244 15 mg/L 0.0074x – 0.0016 0.7305
Figure XCVIII. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m3.
360
30
25 Alum = 0 mg/L 20
Alum = 5 mg/L
C 15 Alum = 10 mg/L
10 Alum = 15 mg/L
5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.8141x + 26.329 0.9404 5 mg/L -0.7439x + 24.945 0.9384 10 mg/L -1.2755x + 26.295 0.9228 15 mg/L -0.7105x + 24.788 0.9957
Figure XCIX. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m3.
361
2.50
2.00 Alum = 0 mg/L
1.50 Alum = 5 mg/L
Alum = 10 mg/L
1.00 ln(C/Co) - Alum = 15 mg/L 0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0712x – 0.2549 0.9325 5 mg/L 0.0455x – 0.2039 0.9161 10 mg/L 0.0841x – 0.3356 0.9016 15 mg/L 0.0316x – 0.0906 0.9515
Figure C. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
362
0.40 0.35 0.30 Alum = 0 mg/L 0.25 Alum = 5 mg/L
0.20
0.15 Alum = 10 mg/L 1/C
0.10 Alum = 15 mg/L 0.05 0.00 -0.05 0 10 20 30 40 -0.10 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0083x – 0.0032 0.8584 5 mg/L 0.0044x + 0.0019 0.9463 10 mg/L 0.0122x – 0.0389 0.9175 15 mg/L 0.0029x + 0.014 0.9198
Figure CI. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
363
30
25 Alum = 0 mg/L 20 Alum = 5 mg/L 15
Alum = 10 mg/L C 10 Alum = 15 mg/L 5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.6588x + 23.998 0.9708 5 mg/L -0.8121x + 23.703 0.9388 10 mg/L -0.8555x + 23.581 0.9180 15 mg/L -0.7865x + 24.117 0.9657
Figure CII. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
364
3.50
3.00 Alum = 0 mg/L 2.50 Alum = 5 mg/L
2.00 Alum = 10 mg/L
1.50 ln(C/Co) - Alum = 15 mg/L 1.00
0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0763x – 0.1768 0.9613 5 mg/L 0.0987x – 0.2112 0.9744 10 mg/L 0.0606x – 0.062 0.9596 15 mg/L 0.1035x – 0.3304 0.9579
Figure CIII. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
365
0.80 0.70 0.60 Alum = 0 mg/L 0.50 Alum = 5 mg/L
0.40
0.30 Alum = 10 mg/L 1/C 0.20 Alum = 15 mg/L 0.10 0.00 -0.10 0 10 20 30 40 -0.20 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0115x – 0.0217 0.7770 5 mg/L 0.0143x – 0.0344 0.8243 10 mg/L 0.0229x – 0.0873 0.8089 15 mg/L 0.0129x – 0.0318 0.7629 Figure CIV. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
366
30
25 Alum = 0 mg/L 20 Alum = 5 mg/L 15
Alum = 10 mg/L C 10 Alum = 15 mg/L
5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.7554x + 23.634 0.9792 5 mg/L -0.8124x + 22.791 0.9241 10 mg/L -0.7303x + 23.799 0.9087 15 mg/L -0.8623x + 24.464 0.947
Figure CV. Zero-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
367
3.50
3.00
2.50 Alum = 0 mg/L Alum = 5 mg/L
2.00
1.50 Alum = 10 mg/L ln(C/Co) - 1.00 Alum = 15 mg/L
0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.056x – 0.1148 0.9137 5 mg/L 0.0861x – 0.1894 0.9823 10 mg/L 0.1032x – 0.2498 0.9785 15 mg/L 0.0807x – 0.2143 0.9548
Figure CVI. First-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
368
0.90 0.80 0.70 Alum = 0 mg/L 0.60 Alum = 5 mg/L 0.50
0.40 Alum = 10 mg/L
1/C 0.30 Alum = 15 mg/L 0.20 0.10 0.00 -0.10 0 10 20 30 40 -0.20 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0061x + 0.0104 0.7623 5 mg/L 0.0225x – 0.089 0.7516 10 mg/L 0.0062x + 0.0191 0.9275 15 mg/L 0.0236x – 0.1023 0.7597
Figure CVII. Second-order kinetic rate equations for Alum dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
369
30
25
20 Fe2(SO4)3 = 0 mg/L
Fe2(SO4)3 = 5 mg/L C 15 Fe2(SO4)3 = 10 mg/L 10 Fe2(SO4)3 = 15 mg/L 5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.6893x + 25.696 0.9390 5 mg/L -0.7553x + 24.355 0.9965 10 mg/L -0.8042x + 24.419 0.9384 15 mg/L -0.6853x + 24.719 0.9883
Figure CVIII. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
370
3.00
2.50
2.00 Fe2(SO4)3 = 0 mg/L
1.50 Fe2(SO4)3 = 5 mg/L
Fe2(SO4)3 = 10 mg/L
ln(C/Co) 1.00 - Fe2(SO4)3 = 15 mg/L 0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.051x – 0.1443 0.9154 5 mg/L 0.0819x – 0.2776 0.8851 10 mg/L 0.076x – 0.1686 0.9761 15 mg/L 0.0592x – 0.1698 0.9019
Figure CIX. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
371
0.60
0.50
0.40 Fe2(SO4)3 = 0 mg/L
0.30 Fe2(SO4)3 = 5 mg/L
1/C 0.20 Fe2(SO4)3 = 10 mg/L Fe2(SO4)3 = 15 mg/L 0.10
0.00 0 10 20 30 40 -0.10 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0043x + 0.0213 0.8767 5 mg/L 0.0149x – 0.0529 0.6835 10 mg/L 0.0099x – 0.006 0.8934 15 mg/L 0.0067x + 0.0038 0.7422
Figure CX. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
372
35
30
25 Fe2(SO4)3 = 0 mg/L
20 Fe2(SO4)3 = 5 mg/L
C 15 Fe2(SO4)3 = 10 mg/L
10 Fe2(SO4)3 = 15 mg/L
5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.4139x + 23.543 0.9419 5 mg/L -0.5425x + 26.719 0.9686 10 mg/L -0.7892x + 29.241 0.9439 15 mg/L -0.8349x + 30.113 0.9378
Figure CXI. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
373
1.60 1.40 1.20 Fe2(SO4)3 = 0 mg/L 1.00
Fe2(SO4)3 = 5 mg/L 0.80
0.60 Fe2(SO4)3 = 10 mg/L ln(C/Co) - 0.40 Fe2(SO4)3 = 15 mg/L 0.20 0.00 -0.20 0 10 20 30 40 -0.40 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0237x + 0.0392 0.9736 5 mg/L 0.0284x – 0.0766 0.9739 10 mg/L 0.0471x – 0.1978 0.9140 15 mg/L 0.0508x – 0.2377 0.8904
Figure CXII. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
374
1.60 1.40 1.20 Fe2(SO4)3 = 0 mg/L 1.00 Fe2(SO4)3 = 5 mg/L
0.80
0.60 Fe2(SO4)3 = 10 mg/L 1/C 0.40 Fe2(SO4)3 = 15 mg/L 0.20 0.00 -0.20 0 10 20 30 40 -0.40 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0014x + 0.04 0.9827 5 mg/L 0.0026x + 0.0151 0.8993 10 mg/L 0.0046x + 0.0026 0.9628 15 mg/L 0.0051x – 0.0021 0.9435
Figure CXIII. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
375
30
25 Fe2(SO4)3 = 0 mg/L 20 Fe2(SO4)3 = 5 mg/L 15
Fe2(SO4)3 = 10 mg/L C 10 Fe2(SO4)3 = 15 mg/L
5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.8226x + 23.513 0.9317 5 mg/L -0.6351x + 24.834 0.9934 10 mg/L -0.5204x + 24.941 0.9425 15 mg/L -0.5032x + 24.946 0.9602
Figure CXIV. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
376
3.00
2.50 Fe2(SO4)3 = 0 mg/L 2.00
Fe2(SO4)3 = 5 mg/L
1.50 Fe2(SO4)3 = 10 mg/L
ln(C/Co) 1.00 - Fe2(SO4)3 = 15 mg/L
0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0906x – 0.192 0.9804 5 mg/L 0.0485x – 0.1065 0.9273 10 mg/L 0.0335x – 0.0575 0.9095 15 mg/L 0.0303x – 0.0375 0.9494
Figure CXV. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
377
0.60
0.50 Fe2(SO4)3 = 0 mg/L 0.40 Fe2(SO4)3 = 5 mg/L 0.30
Fe2(SO4)3 = 10 mg/L 1/C 0.20 Fe2(SO4)3 = 15 mg/L
0.10
0.00 0 10 20 30 40 -0.10 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0159x – 0.0418 0.8471 5 mg/L 0.004x + 0.0213 0.7988 10 mg/L 0.0023x + 0.0323 0.8476 15 mg/L 0.0019x + 0.0353 0.9226
Figure CXVI. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
378
35
30
25 Fe2(SO4)3 = 0 mg/L
20 Fe2(SO4)3 = 5 mg/L
C 15 Fe2(SO4)3 = 10 mg/L
10 Fe2(SO4)3 = 15 mg/L 5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.8141x + 26.329 0.9404 5 mg/L -0.5425x + 26.719 0.9686 10 mg/L -0.7892x + 29.241 0.9439 15 mg/L -0.8271x + 28.982 0.9519
Figure CXVII. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
379
2.50
2.00 Fe2(SO4)3 = 0 mg/L 1.50
Fe2(SO4)3 = 5 mg/L 1.00
Fe2(SO4)3 = 10 mg/L
ln(C/Co) - 0.50 Fe2(SO4)3 = 15 mg/L
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0712x – 0.2549 0.9325 5 mg/L 0.0284x – 0.0766 0.9739 10 mg/L 0.0471x – 0.1978 0.9140 15 mg/L 0.0542x – 0.2224 0.9193
Figure CXVIII. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
380
0.30
0.25
0.20 Fe2(SO4)3 = 0 mg/L Fe2(SO4)3 = 5 mg/L
0.15
1/C Fe2(SO4)3 = 10 mg/L 0.10 Fe2(SO4)3 = 15 mg/L 0.05
0.00 0 10 20 30 40 -0.05 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0083x – 0.0032 0.8584 5 mg/L 0.0026x + 0.0151 0.8993 10 mg/L 0.0046x + 0.0026 0.9628 15 mg/L 0.0056x – 0.0036 0.9506
Figure CXIX. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
381
30
25
20 Fe2(SO4)3 = 0 mg/L
15 Fe2(SO4)3 = 5 mg/L
C 10 Fe2(SO4)3 = 10 mg/L
5 Fe2(SO4)3 = 15 mg/L 0 0 10 20 30 40 -5
-10 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.7554x + 23.634 0.9792 5 mg/L -0.9555x + 25.198 0.7962 10 mg/L -0.8415x + 26.483 0.9041 15 mg/L -0.7832x + 28.165 0.9854
Figure CXX. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
382
3.50 3.00 2.50 Fe2(SO4)3 = 0 mg/L 2.00
Fe2(SO4)3 = 5 mg/L 1.50 Fe2(SO4)3 = 10 mg/L
1.00
ln(C/Co) - 0.50 Fe2(SO4)3 = 15 mg/L 0.00 0 10 20 30 40 -0.50 -1.00 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0763x – 0.1768 0.9613 5 mg/L 0.121x – 0.3385 0.9346 10 mg/L 0.0681x – 0.198 0.9657 15 mg/L 0.052x – 0.2017 0.9436
Figure CXXI. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
383
1.00
0.80 Fe2(SO4)3 = 0 mg/L 0.60
Fe2(SO4)3 = 5 mg/L
0.40
1/C Fe2(SO4)3 = 10 mg/L
0.20 Fe2(SO4)3 = 15 mg/L
0.00 0 10 20 30 40 -0.20 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0115x – 0.0217 0.7770 5 mg/L 0.0323x – 0.1418 0.8911 10 mg/L 0.0084x – 0.0139 0.9621 15 mg/L 0.0055x – 0.0029 0.9344
Figure CXXII. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
384
30
25 Fe2(SO4)3 = 0 mg/L 20 Fe2(SO4)3 = 5 mg/L 15
Fe2(SO4)3 = 10 mg/L C 10 Fe2(SO4)3 = 15 mg/L
5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.6588x + 23.988 0.9708 5 mg/L -0.7986x + 23.7 0.8822 10 mg/L -0.8445x + 26.329 0.8311 15 mg/L -0.8302x + 25.988 0.9108
Figure CXXIII. Zero-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
385
2.50
2.00 Fe2(SO4)3 = 0 mg/L
1.50 Fe2(SO4)3 = 5 mg/L
Fe2(SO4)3 = 10 mg/L
1.00 ln(C/Co)
- Fe2(SO4)3 = 15 mg/L 0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.056x – 0.1148 0.9137 5 mg/L 0.0605x – 0.2335 0.9128 10 mg/L 0.0667x – 0.153 0.8949 15 mg/L 0.0712x – 0.2103 0.9721
Figure CXXIV. First-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
386
0.35
0.30 Fe2(SO4)3 = 0 mg/L 0.25 Fe2(SO4)3 = 5 mg/L 0.20
Fe2(SO4)3 = 10 mg/L
0.15 1/C Fe2(SO4)3 = 15 mg/L 0.10
0.05
0.00 0 10 20 30 40 -0.05 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0061x + 0.0104 0.7623 5 mg/L 0.0066x – 0.007 0.9555 10 mg/L 0.0077x – 0.003 0.9258 15 mg/L 0.0095x – 0.0223 0.9170
Figure CXXV. Second-order kinetic rate equations for Fe2(SO4)3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
387
30
25 FeCl3 = 0 mg/L 20
FeCl3 = 5 mg/L
C 15 FeCl3 = 10 mg/L 10 FeCl3 = 15 mg/L 5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.6893x + 25.696 0.939 5 mg/L -0.697x + 24.392 0.8931 10 mg/L -0.6855x + 23.451 0.9734 15 mg/L -0.6992x + 23.706 0.9743
Figure CXXVI. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
388
2.00
1.50 FeCl3 = 0 mg/L
FeCl3 = 5 mg/L 1.00
FeCl3 = 10 mg/L ln(C/Co) - 0.50 FeCl3 = 15 mg/L
0.00 0 10 20 30 40
-0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.051x – 0.1443 0.9154 5 mg/L 0.052x – 0.0562 0.9295 10 mg/L 0.0585x – 0.0758 0.9829 15 mg/L 0.0601x – 0.095 0.9807
Figure CXXVII. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
389
0.30
0.25 FeCl3 = 0 mg/L 0.20
FeCl3 = 5 mg/L
0.15 1/C FeCl3 = 10 mg/L 0.10 FeCl3 = 15 mg/L 0.05
0.00 0 10 20 30 40 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0043x + 0.0213 0.8767 5 mg/L 0.0045x + 0.0273 0.9351 10 mg/L 0.0062x + 0.0147 0.8829 15 mg/L 0.0065x + 0.0123 0.8769
Figure CXXVIII. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 18.69 A/m2.
390
30
25 FeCl3 = 0 mg/L 20
FeCl3 = 5 mg/L
C 15 FeCl3 = 10 mg/L 10 FeCl3 = 15 mg/L 5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.4139x + 23.543 0.9419 5 mg/L -0.7732x + 26.951 0.9753 10 mg/L -0.6591x + 26.417 0.9121 15 mg/L -0.489x + 23.488 0.9662
Figure CXXIX. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
391
2.50
2.00 FeCl3 = 0 mg/L 1.50
FeCl3 = 5 mg/L
1.00 FeCl3 = 10 mg/L ln(C/Co) - FeCl3 = 15 mg/L 0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0237x + 0.0392 0.9736 5 mg/L 0.0672x - 0.03055 0.8915 10 mg/L 0.049x – 0.1944 0.8703 15 mg/L 0.0308x + 0.0247 0.9951
Figure CXXX. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
392
0.35
0.30
0.25 FeCl3 = 0 mg/L
0.20 FeCl3 = 5 mg/L
0.15 FeCl3 = 10 mg/L 1/C
0.10 FeCl3 = 15 mg/L
0.05
0.00 0 10 20 30 40 -0.05 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0014x + 0.04 0.9827 5 mg/L 0.0081x – 0.0099 0.7362 10 mg/L 0.0043x + 0.0168 0.7976 15 mg/L 0.0021x + 0.0373 0.9717
Figure CXXXI. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 18.69 A/m2.
393
30
25
20 FeCl3 = 0 mg/L FeCl3 = 5 mg/L
15
C FeCl3 = 10 mg/L 10 FeCl3 = 15 mg/L 5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.8226x + 23.513 0.9317 5 mg/L -0.8025x + 24.239 0.8876 10 mg/L -0.7335x + 25.466 0.9317 15 mg/L -0.7426x + 24.372 0.8912
Figure CXXXII. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
394
3.00
2.50
2.00 FeCl3 = 0 mg/L
FeCl3 = 5 mg/L 1.50 FeCl3 = 10 mg/L
ln(C/Co) 1.00 - FeCl3 = 15 mg/L 0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0906x – 0.192 0.9804 5 mg/L 0.0739x – 0.114 0.8988 10 mg/L 0.0581x – 0.1532 0.9057 15 mg/L 0.0583x – 0.0671 0.9518
Figure CXXXIII. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
395
0.60
0.50
0.40 FeCl3 = 0 mg/L FeCl3 = 5 mg/L
0.30
1/C FeCl3 = 10 mg/L 0.20 FeCl3 = 15 mg/L 0.10
0.00 0 10 20 30 40 -0.10 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0159x – 0.0418 0.8471 5 mg/L 0.0088x + 0.0098 0.8692 10 mg/L 0.0055x + 0.0169 0.8658 15 mg/L 0.0054x + 0.0237 0.9603
Figure CXXXIV. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 24.92 A/m2.
396
30
25 FeCl3 = 0 mg/L 20
FeCl3 = 5 mg/L
C 15 FeCl3 = 10 mg/L 10 FeCl3 = 15 mg/L 5
0 0 10 20 30 40 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.8141x + 26.329 0.9404 5 mg/L -0.7386x + 22.521 0.9263 10 mg/L -0.7231x + 23.38 0.9391 15 mg/L -0.6328x + 23.031 0.9580
Figure CXXXV. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
397
2.50
2.00 FeCl3 = 0 mg/L 1.50
FeCl3 = 5 mg/L
1.00 FeCl3 = 10 mg/L
ln(C/Co) - FeCl3 = 15 mg/L 0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0712x – 0.2549 0.9325 5 mg/L 0.0711x - 0.0497 0.9936 10 mg/L 0.0622x – 0.0586 0.9875 15 mg/L 0.0495x – 0.0122 0.9948
Figure CXXXVI. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
398
0.35
0.30
0.25 FeCl3 = 0 mg/L
0.20 FeCl3 = 5 mg/L
0.15 FeCl3 = 10 mg/L 1/C
0.10 FeCl3 = 15 mg/L
0.05
0.00 0 10 20 30 40 -0.05 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0083x – 0.0032 0.8584 5 mg/L 0.0091x + 0.0046 0.9187 10 mg/L 0.0066x + 0.0165 0.9426 15 mg/L 0.0045x + 0.0262 0.9257
Figure CXXXVII. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 24.92 A/m2.
399
30
25 FeCl3 = 0 mg/L 20 FeCl3 = 5 mg/L
15
C FeCl3 = 10 mg/L 10 FeCl3 = 15 mg/L 5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.7554x + 23.634 0.9792 5 mg/L -0.8409x + 23.09 0.8862 10 mg/L -0.8709x + 24.662 0.9429 15 mg/L -0.8142x + 24.93 0.8911
Figure CXXXVIII. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
400
3.50 3.00
2.50 FeCl3 = 0 mg/L 2.00
FeCl3 = 5 mg/L 1.50 FeCl3 = 10 mg/L
1.00
ln(C/Co) - FeCl3 = 15 mg/L 0.50 0.00 0 10 20 30 40 -0.50 -1.00 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0763x – 0.1768 0.9613 5 mg/L 0.0711x - 0.0497 0.9936 10 mg/L 0.0622x – 0.0586 0.9875 15 mg/L 0.0495x – 0.0122 0.9948
Figure CXXXIX. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
401
0.90 0.80 0.70 FeCl3 = 0 mg/L 0.60 0.50 FeCl3 = 5 mg/L
0.40 FeCl3 = 10 mg/L 1/C 0.30 0.20 FeCl3 = 15 mg/L 0.10 0.00 -0.10 0 10 20 30 40 -0.20 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0115x – 0.0217 0.7770 5 mg/L 0.017x – 0.0339 0.9174 10 mg/L 0.0232x – 0.0957 0.7965 15 mg/L 0.0079x + 0.0112 0.9062
Figure CXL. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 4 for a current density of 31.15 A/m2.
402
30
25 FeCl3 = 0 mg/L 20
FeCl3 = 5 mg/L C 15 FeCl3 = 10 mg/L 10 FeCl3 = 15 mg/L 5
0 0 10 20 30 40 -5 Treatment Time (mins)
Zero Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L -0.6588x + 23.988 0.9708 5 mg/L -0.7213x + 23.23 0.9782 10 mg/L -0.7691x + 22.798 0.9557 15 mg/L -0.82x + 24.014 0.9617
Figure CXLI. Zero-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
403
3.00
2.50 FeCl3 = 0 mg/L 2.00
FeCl3 = 5 mg/L
1.50 FeCl3 = 10 mg/L
ln(C/Co) 1.00 - FeCl3 = 15 mg/L 0.50
0.00 0 10 20 30 40 -0.50 Treatment Time (mins)
First Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.056x – 0.1148 0.9137 5 mg/L 0.0709x – 0.1358 0.9586 10 mg/L 0.0848x – 0.1616 0.9763 15 mg/L 0.0945x – 0.2809 0.9506
Figure CXLII. First-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
404
0.80 0.70
0.60 FeCl3 = 0 mg/L 0.50 FeCl3 = 5 mg/L
0.40
0.30 FeCl3 = 10 mg/L 1/C 0.20 FeCl3 = 15 mg/L 0.10 0.00 -0.10 0 10 20 30 40 -0.20 Treatment Time (mins)
Second Order Kinetics Coagulant Dose Kinetic Equation R2 (mg/L) 0 mg/L 0.0061x + 0.0104 0.7623 5 mg/L 0.0099x – 0.0107 0.7856 10 mg/L 0.0149x – 0.0398 0.7883 15 mg/L 0.0196x – 0.0778 0.7338
Figure CXLIII. Second-order kinetic rate equations for FeCl3 dose (5, 10, and 15 mg/L) at pH = 7 for a current density of 31.15 A/m2.
405
Kinetic Constant (min-1) Coagulant Dose (mg/L) Current Density (A/m^2) pH Zero Order R2 1st Order R2 2nd Order R2 Alum 0 18.69 4 -0.6893 0.939 0.051 0.9154 0.0043 0.8767 Alum 5 18.69 4 -0.8553 0.9642 0.0929 0.9353 0.0174 0.7495 Alum 10 18.69 4 -0.7137 0.9669 0.0613 0.9577 0.0066 0.8705 Alum 15 18.69 4 -0.8137 0.9535 0.076 0.9513 0.0107 0.7371 Alum 0 18.69 7 -0.4139 0.9419 0.0237 0.9736 0.0014 0.9827 Alum 5 18.69 7 -0.5178 0.9536 0.0306 0.9815 0.0019 0.9724 Alum 10 18.69 7 -0.7145 0.996 0.0605 0.9301 0.0066 0.7836 Alum 15 18.69 7 -0.7241 0.9395 0.0539 0.9196 0.0048 0.8532 Alum 0 24.92 4 -0.8226 0.9317 0.0906 0.9804 0.0159 0.8471 Alum 5 24.92 4 -0.9089 0.9109 0.1103 0.9367 0.0257 0.8107 Alum 10 24.92 4 -0.8395 0.9601 0.0815 0.9056 0.0125 0.7244 Alum 15 24.92 4 -0.718 0.953 0.0626 0.8901 0.0074 0.7305 Alum 0 24.92 7 -0.8141 0.9404 0.0712 0.9325 0.0083 0.8584 Alum 5 24.92 7 -0.7439 0.9384 0.0455 0.9161 0.0044 0.9463 Alum 10 24.92 7 -1.2755 0.9228 0.0841 0.9016 0.0122 0.9175 Alum 15 24.92 7 -0.7105 0.9957 0.0316 0.9515 0.0029 0.9198 Alum 0 31.15 4 -0.6588 0.9708 0.0763 0.9613 0.0115 0.777 Alum 5 31.15 4 -0.8121 0.9388 0.0987 0.9744 0.0143 0.8243 Alum 10 31.15 4 -0.8555 0.918 0.0606 0.9596 0.0229 0.8089 Alum 15 31.15 4 -0.7865 0.9657 0.1035 0.9579 0.0129 0.7629 Alum 0 31.15 7 -0.7554 0.9792 0.056 0.9137 0.0061 0.7623 Alum 5 31.15 7 -0.8124 0.9241 0.0861 0.9823 0.02258 0.7516 Alum 10 31.15 7 -0.7303 0.9087 0.1032 0.9785 0.0062 0.9275 Alum 15 31.15 7 -0.8623 0.947 0.0807 0.9548 0.0236 0.7597 Fe2(SO4)3 0 18.69 4 -0.6893 0.939 0.051 0.9154 0.0043 0.8767 Fe2(SO4)3 5 18.69 4 -0.7553 0.9965 0.0819 0.8851 0.0149 0.6835 Fe2(SO4)3 10 18.69 4 -0.8042 0.9384 0.076 0.9761 0.0099 0.8934 Fe2(SO4)3 15 18.69 4 -0.6853 0.9883 0.0592 0.9019 0.0067 0.7422 Fe2(SO4)3 0 18.69 7 -0.4139 0.9419 0.0237 0.9736 0.0014 0.9827 Fe2(SO4)3 5 18.69 7 -0.5425 0.9686 0.0284 0.9739 0.0026 0.8993 Fe2(SO4)3 10 18.69 7 -0.7892 0.9439 0.0471 0.914 0.0046 0.9628 Fe2(SO4)3 15 18.69 7 -0.8349 0.9378 0.0508 0.8904 0.0051 0.9435 Fe2(SO4)3 0 24.92 4 -0.8226 0.9317 0.0906 0.9804 0.0159 0.8471 Fe2(SO4)3 5 24.92 4 -0.6351 0.9934 0.0485 0.9273 0.004 0.7988 Fe2(SO4)3 10 24.92 4 -0.5204 0.9425 0.0335 0.9095 0.0023 0.8476 Fe2(SO4)3 15 24.92 4 -0.5032 0.9602 0.0303 0.9494 0.0019 0.9226 Fe2(SO4)3 0 24.92 7 -0.8141 0.9404 0.0712 0.9325 0.0083 0.8584 Fe2(SO4)3 5 24.92 7 -0.5425 0.9686 0.0284 0.9739 0.0026 0.8993
406
Fe2(SO4)3 10 24.92 7 -0.7892 0.9439 0.0471 0.914 0.0046 0.9628 Fe2(SO4)3 15 24.92 7 -0.8271 0.9519 0.0542 0.9193 0.0056 0.9506 Fe2(SO4)3 0 31.15 4 -0.7554 0.9792 0.0763 0.9613 0.0115 0.777 Fe2(SO4)3 5 31.15 4 -0.9555 0.7962 0.121 0.9346 0.0323 0.8911 Fe2(SO4)3 10 31.15 4 -0.8415 0.9041 0.0681 0.9657 0.0084 0.9621 Fe2(SO4)3 15 31.15 4 -0.7832 0.9854 0.052 0.9436 0.0055 0.9344 Fe2(SO4)3 0 31.15 7 -0.6588 0.9708 0.056 0.9137 0.0061 0.7623 Fe2(SO4)3 5 31.15 7 -0.7986 0.8822 0.0605 0.9128 0.0066 0.9555 Fe2(SO4)3 10 31.15 7 -0.8445 0.8311 0.0667 0.8949 0.0077 0.9258 Fe2(SO4)3 15 31.15 7 -0.8302 0.9108 0.0712 0.9721 0.0095 0.917 FeCl3 0 18.69 4 -0.6893 0.939 0.051 0.9154 0.0043 0.8767 FeCl3 5 18.69 4 -0.697 0.8931 0.052 0.9295 0.0045 0.9351 FeCl3 10 18.69 4 -0.6855 0.9734 0.0585 0.9829 0.0062 0.8829 FeCl3 15 18.69 4 -0.6992 0.9743 0.0601 0.9807 0.0065 0.8769 FeCl3 0 18.69 7 -0.4139 0.9419 0.0237 0.9736 0.0014 0.9827 FeCl3 5 18.69 7 -0.7732 0.9753 0.0284 0.9739 0.0081 0.7362 FeCl3 10 18.69 7 -0.6591 0.9121 0.0471 0.914 0.0043 0.7976 FeCl3 15 18.69 7 -0.489 0.9662 0.0508 0.8904 0.0021 0.9717 FeCl3 0 24.92 4 -0.8226 0.9317 0.0906 0.9804 0.0159 0.8471 FeCl3 5 24.92 4 -0.8025 0.8876 0.0739 0.8988 0.0088 0.8692 FeCl3 10 24.92 4 -0.7335 0.9317 0.0581 0.9057 0.0055 0.8658 FeCl3 15 24.92 4 -0.7426 0.8912 0.0583 0.9518 0.0054 0.9603 FeCl3 0 24.92 7 -0.8141 0.9404 0.0712 0.9325 0.0083 0.8584 FeCl3 5 24.92 7 -0.7386 0.9263 0.0711 0.9936 0.0091 0.9187 FeCl3 10 24.92 7 -0.7231 0.9391 0.0622 0.9875 0.0066 0.9426 FeCl3 15 24.92 7 -0.6328 0.958 0.0495 0.9948 0.0045 0.9257 FeCl3 0 31.15 4 -0.7554 0.9792 0.0763 0.9613 0.0115 0.777 FeCl3 5 31.15 4 -0.8409 0.8862 0.0711 0.9936 0.017 0.9174 FeCl3 10 31.15 4 -0.8709 0.9429 0.0622 0.9875 0.0232 0.7965 FeCl3 15 31.15 4 -0.8142 0.8911 0.0495 0.9948 0.0079 0.9062 FeCl3 0 31.15 7 -0.6588 0.9708 0.056 0.9137 0.0061 0.7623 FeCl3 5 31.15 7 -0.7213 0.9782 0.0709 0.9586 0.0099 0.7856 FeCl3 10 31.15 7 -0.7691 0.9557 0.0848 0.9763 0.0149 0.7883 FeCl3 15 31.15 7 -0.82 0.9617 0.0945 0.9506 0.0196 0.7338
Table XVII. Kinetic Rate constants for various coagulants.
407
1.8 1.6 1.4
1.2
CD = 18.68975, Alum = qt)
- 1 5 mg/L, pH = 4
0.8 CD = 24.91966, Alum = log(qe 0.6 5 mg/L, pH = 4 CD= 31.14958, Alum = 5 0.4 mg/L, pH = 4 0.2 0 0 5 10 15 20 25 Time (mins)
Figure CXLIV. Lagergren pseudo-first order model for best removal efficiencies with Alum added at various current densities (18.69, 24.92, 31.15 A/m2).
408
1.6 1.4 1.2 1
CD = 18.68975, Alum =
0.8 5 mg/L, pH = 4 t/qt CD = 24.91966, Alum = 0.6 5 mg/L, pH = 4 0.4 CD= 31.14958, Alum = 5 mg/L, pH = 4 0.2 0 0 5 10 15 20 25 Time (mins)
Figure CXLV Lagergren pseudo-second order model for best removal efficiencies with Alum added at various current densities (18.69, 24.92, 31.15 A/m2).
409
1.8 1.6 1.4 1.2 CD = 18.68975, Fe2(SO4)3 = 5 mg/L, pH
1 = 4
qt) - 0.8 CD = 24.91966, Fe2(SO4)3 = 10 mg/L, log(qe 0.6 pH = 7 0.4 CD= 31.14958, Fe2(SO4)3 = 5 mg/L, pH 0.2 = 4 0 0 5 10 15 20 25 -0.2
Figure CXLVI. Lagergren pseudo-first order model for best removal efficiencies with 2 Fe2(SO4)3 added at various current densities (18.69, 24.92, 31.15 A/m ).
410
2 1.8 1.6 1.4 CD = 18.68975, 1.2
Fe2(SO4)3 = 5 mg/L, pH
1 = 4 t/qt 0.8 CD = 24.91966, Fe2(SO4)3 = 10 mg/L, 0.6 pH = 7 0.4 CD= 31.14958, 0.2 Fe2(SO4)3 = 5 mg/L, pH 0 = 4 0 5 10 15 20 25 Time (mins)
Figure CXLVII. Lagergren pseudo-second order model for best removal efficiencies 2 with Fe2(SO4)3 added at various current densities (18.69, 24.92, 31.15 A/m ).
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1.8 1.6 1.4 1.2 CD = 18.68975, FeCl3 =
qt) 5 mg/L, pH = 7 - 1 0.8
CD = 24.91966, FeCl3 = log(qe 0.6 5 mg/L, pH = 7 0.4 CD= 31.14958, FeCl3 = 0.2 10 mg/L, pH = 4 0 0 5 10 15 20 25 Time (mins)
Figure CXLVIII. Pseudo first-order kinetics for best removal efficiencies with FeCl3 added at various current densities (18.69, 24.92, 31.15 A/m2).
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3
2.5
2 CD = 18.68975, FeCl3 =
5 mg/L, pH = 7
1.5 t/qt CD = 24.91966, FeCl3 = 1 5 mg/L, pH = 7
0.5 CD= 31.14958, FeCl3 = 10 mg/L, pH = 4 0 0 5 10 15 20 25 Time (mins)
Figure CXLIX. Pseudo second-order kinetics for best removal efficiencies with FeCl3 added at various current densities (18.69, 24.92, 31.15 A/m2).
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Pseudo-first order Pseudo-second order
Current qe k1 qe 2 -1 2 -1 2 Coagulant Density (A/m ) (mg/g) (min ) r (mg/g) k2 (min ) r Alum 18.68975 46.26036 0.1073 0.9379 -22.73 0.00147 0.8647 Alum 24.91966 35.39207 0.1639 0.9024 -17.0068 0.002058 0.9012 Alum 31.14958 28.22427 0.10501 0.9951 -2500 2.04E-07 0.0006 Fe2(SO4)3 18.68975 45.86608 0.0585 0.9847 123.456 0.000133 0.9268 Fe2(SO4)3 24.91966 17.09973 0.0992 0.983 -15.55 0.00226 0.7165 Fe2(SO4)3 31.14958 4.788643 0.2075 0.9943 -60.24 0.000837 0.7896 FeCl3 18.68975 42.93798 0.0686 0.9521 -7.4515 0.00586 0.8078 FeCl3 24.91966 32.42471 0.117683 0.9794 107.526 0.000169 0.8678 FeCl3 31.14958 28.11241 0.1446 0.8982 -28.99 0.000839 0.9149
2 Table XVIII. Comparison of Lagergren model variables (qe, k1, k2, r ) for various coagulants based on current densities (18.69, 24.92, 31.15 A/m2).
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APPENDIX D RESULTS FROM PHOTO-OXIDATION EXPERIMENT
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Figure CL. Band gap energy (from top to bottom: undoped TiO2 , Fe-doped TiO2, and Co-doped TiO2).
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1.6 24 ppm 1.4 34 ppm R2= 0.9454 1.2 44 ppm
1
0.8
ln(C/Co) 2 - R = 0.9568 0.6
0.4 R2= 0.961 0.2
0 0 50 100 150 200 Time (mins)
Figure CLI (a). Normalized –ln(C/Co) vs time (mins) for Co-doped TiO2 with a dosage of 0.25 g/L.
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1.2 24 ppm 1 34 ppm R2= 0.9217
0.8 44 ppm
R2= 0.948
0.6
ln(C/Co) - 0.4 R2= 0.9553 0.2
0 0 50 100 150 200 Time (mins)
Figure CLI (b). Normalized –ln(C/Co) vs time (mins) for Co-doped TiO2 with a dosage of 0.50 g/L.
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1.2 24 ppm 1 34 ppm R2= 0.9217
0.8 44 ppm
R2= 0.9578
0.6
ln(C/Co) - 0.4 R2= 0.9887 0.2
0 0 50 100 150 200 Time (mins)
Figure CLI (c). Normalized –ln(C/Co) vs time (mins) for Co-doped TiO2 with a dosage of 1 g/L.
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2.5 24 ppm
2 34 ppm R2= 0.9655
44 ppm 1.5
R2= 0.9904 ln(C/Co) - 1 R2= 0.9911
0.5
0 0 50 100 150 200 Time (mins)
Figure CLI(d). Normalized –ln(C/Co) vs time (mins) for Fe-doped TiO2 with a dosage of 0.25 g/L.
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3 24 ppm 2.5 34 ppm R2= 0.991
2 44 ppm
R2= 0.9974
1.5
ln(C/Co) - 1 R2= 0.9752
0.5
0 0 50 100 150 200 Time (mins)
Figure CLI (e). Normalized –ln(C/Co) vs time (mins) for Fe-doped TiO2 with a dosage of 0.50 g/L.
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6 24 ppm 5 34 ppm R2= 0.9875
4 44 ppm
R2= 0.9975
3
ln(C/Co) - 2 R2= 0.985
1
0 0 50 100 150 200 Time (mins)
Figure CLI (f). Normalized –ln(C/Co) vs time (mins) for Fe-doped TiO2 with a dosage of 1.0 g/L.
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30
25
20
15
10
Concentration (mg/L)Concentration 5
0 0 50 100 150 200 Time (mins)
Co-doped (0.25 g/L) Co-doped (0.5 g/L) Co-doped (1 g/L) Fe-doped (0.25 g/L) Fe-doped (0.5 g/L) Fe-doped (1 g/L) Control
Figure CLII. Normalized concentration (mg/L) vs time (minutes) for Co-doped TiO2 and Fe-doped TiO2 with Acid Orange 7 dose concentration of 24 mg/L.
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40
35
30 25 20 15
10 Concentration (mg/L)Concentration 5 0 0 50 100 150 200 Time (mins)
Co-doped (0.25 g/L) Co-doped (0.5 g/L) Co-doped (1 g/L) Fe-doped (0.25 g/L) Fe-doped (0.5 g/L) Fe-doped (1 g/L) Control
Figure CLIII. Normalized concentration (mg/L) vs time (minutes) for Co-doped TiO2 and Fe-doped TiO2 with Acid Orange 7 dose concentration of 34 mg/L.
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50 45 40 35 30 25 20 15
Concentration (mg/L)Concentration 10 5 0 0 50 100 150 200 Time (mins)
Co-doped (0.25 g/L) Co-doped (0.5 g/L) Co-doped (1 g/L) Fe-doped (0.25 g/L) Fe-doped (0.5 g/L) Fe-doped (1 g/L) Control
Figure CLIV. Normalized concentration (mg/L) vs time (minutes) for Co-doped TiO2 and Fe-doped TiO2 with Acid Orange 7 dose concentration of 44 mg/L.
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450 400 R² = 0.9616 R² = 0.9793 350
R² = 0.8239 1)
- 300 (0.25 g/L) 250 (0.5 g/L) 200 (1 g/L)
150 1/kexp (min^1/kexp 100 50 0 24 29 34 39 44 Concentration (mg/L)
Figure CLV. 1/kexp (min-1) versus concentration (mg/L) for each Co-doped TiO2 dose.
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160 R² = 0.8482 140 R² = 0.9805
120
1) - 100 R² = 0.9935 (0.25 g/L) 80 (0.5 g/L)
60 (1 g/L) 1/kexp (min^1/kexp 40 20 0 24 29 34 39 44 Concentration (mg/L)
Figure CLVI. 1/kexp (min-1) versus concentration (mg/L) for Fe-doped TiO2 dose.
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1/k -1 -1 -2 -1 Catalyst Dose (mg/L) (min ) 1/kKads k (ppmmin ) Kads (10 ppm )
Co-TiO2 0.25 143.84 11.607 0.086 8.069
Co-TiO2 0.5 50.437 8.469 0.118 16.792
Co-TiO2 1 36.863 10.196 0.098 27.659
Fe-TiO2 0.25 21.384 2.973 0.336 13.902
Fe-TiO2 0.5 27.383 3.845 0.260 14.042
Fe-TiO2 1 69.688 4.566 0.219 6.552
Table XIX. Kinetic Rate constant and Langmuir-Hinshelwood adsorption constant for each catalyst and dose.
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rate of Dye degradation
Catalyst Dose Concentration 1/kexp (r) -3 -1 -1 Catalyst (mg/L) (mg/L) kexp (x10 min ) (min ) (ppm/min)
Co-TiO2 0.25 24 8.00 125.00 0.0192
Co-TiO2 0.25 34 3.70 270.27 0.01258
Co-TiO2 0.25 44 2.80 357.14 0.01232
Co-TiO2 0.5 24 5.70 175.44 0.01368
Co-TiO2 0.5 34 5.20 192.31 0.01768
Co-TiO2 0.5 44 2.90 344.83 0.01276
Co-TiO2 1 24 5.10 196.08 0.01224
Co-TiO2 1 34 3.00 333.33 0.0102
Co-TiO2 1 44 2.50 400.00 0.011
Fe-TiO2 0.25 24 11.70 85.47 0.02808
Fe-TiO2 0.25 34 7.30 136.99 0.02482
Fe-TiO2 0.25 44 6.90 144.93 0.03036
Fe-TiO2 0.5 24 14.70 68.03 0.03528
Fe-TiO2 0.5 34 10.30 97.09 0.03502
Fe-TiO2 0.5 44 6.90 144.93 0.03036
Fe-TiO2 1 24 23.80 42.02 0.05712
Fe-TiO2 1 34 12.30 81.30 0.04182
Fe-TiO2 1 44 7.50 133.33 0.033
Table XX. Kinetic experimental rate constant (1/kexp), 1/kexp, and the rate of decolorization (r) for each catalyst, dose, and dye concentration of Acid Orange 7.
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APPENDIX E PROCEDURES
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ELECTROCOAGULATION PROCEDURES
Sample Preparation for Acid Yellow (AY11) Samples
1. Using Petri Dish, measure 0.20 g of Acid Yellow 11 dye powder and dissolve into 1 L water inside a beaker.
2. Using Petri Dish, measure 20.00 g of salt and dissolve into beaker while stirring. Also measure all coagulants. Ensure complete mixture.
- 3. Add sodium hydroxide (NaOH) or nitric acid (HNO3 ) for the purpose of adjust pH to initial result.
3. Extract sample from beaker and place into Amber glass vial.
5. Take Amber glass vial and place into centrifuge.
6. Using a timer, set timer for 5 minutes and turn on centrifuge.
7. Turn Power Supply connected to the reactor at the desired current (1.5, 2.5, 3.5 A)
8. For ECF reactor, set timer for 5 minutes.
9. Turn off the timer for the centrifuge and remove the vial. Using a plastic pipette (or syringe), extract from the center of the reactor and place into Amber Vial. Place sample into centrifuge and stir for about 5 minutes.
10. Take Amber Vial and place through vacuum filtration, separating the liquid from the solids.
11. Pour filtrate into a plastic container, measure pH and temperature.
12. Pour filtered sample into a test tube. Measure absorbance at 440 nm. Record %A (converted
431 to %T within Excel program)—If %A is higher than previous value, please repeat step 10.
13. Repeat steps 8-12 (with the exception of recording treatment time between 20-30 minutes where one would set time for 10 minutes).
Sample Preparation for Measuring Color Removal by Electrocoagulation (ECF) for Box
Behnken Experiment
1. Using Petri Dish, measure by weight (20, 32.5, 45 mg) of designated dye powder and dissolve into 1 L water inside a beaker.
2. Using Petri Dish, measure 25.00 g of salt and dissolve into beaker while stirring. Also, measure desired coagulant to the desired dose of the experiment (5, 10, 15 mg). Ensure complete mixture. Take mixture from beaker and transfer to one of the ECF reactor.
3. Stir on stirrer for 1 minute.
4. Add salt and coagulant. Stir for 4 minutes.
3. Extract 20 mL from beaker and place into Amber glass vial.
5. Take Amber glass vial and place into centrifuge.
6. Using a timer, set timer for 5 minutes and turn on centrifuge.
7. Turn Power Supply connected to the reactor at the desired current (3, 4, 5 A)
8. For ECF reactor, set timer for 5 minutes.
9. Turn off the timer for the centrifuge and remove the vial. Using a 100 um pipette, extract from the supernatant about 5 mL.
10. Take pH meter and measure temperature and pH within the ECF reactor.
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11. Place test tube into spectrophotometer at the wavelength for each dye. Record %T (converted to %A within Excel program).
12. Turn timer off of ECF reactor. Extract 10 mL from ECF and place into centrifuge. Repeat steps 9-10.
13. Reset timer on ECF reactor and collect samples, at 15, 20, 30 minutes. Repeat the steps for centrifuge and measurement.
PHOTO-OXIDATION PROCEDURES
Stock Solution Preparation
1. Take 0.1 g of Orange II dye and dissolve into 1 L water.
2. Stir on stirrer and store in a volumetric flask in Chemical Storage room (Stock solution lasts about 1-1 ½ months).
Samples for Measuring Color Removal by Photo-oxidation
1. Measure Orange II and place into beaker
2. Dilute with water until reading matches 100 mL
3. Stir on stirrer for 5 seconds
4. Extract sample (3-5 mL) and push through syringe filter into sample test tubes
5. Wipe test tube with paper towel and place into spectrophotometer
6. Measure %T. Type %T into Excel document (converts %T into %A and concentration).
7. Dissolve pre-determined amount of catalyst (0.25, 0.5 or 1 g) into flask
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8. Put in a magnetic bar and cover with plastic wrap to keep evaporation down.
9. Place onto stirrer in reactor. Stir for 5 minutes.
10. Turn on lamp and set timer for 30 minutes.
11. Turn off lamp and take sample (2-4 mL) and filter using syringe filter.
12. Measure %T. Record %T into Excel document.
13. Repeat steps (12-14) for 60 minutes, 119 minutes, and 180 minutes.
ANALYTICAL EQUIPMENT PROCEDURES
TRANSMITTANCE PROCCEDURES
1. After turning the machine on and waiting at least 20 minutes, set the wavelength to 540 nm
using the wavelength selector.
2. Calibrate the spectrometer by filling a sample vial with tap water .
2. Place the tap water sample into the machine and using the %Transmittance knob, turn the
knob until the spectrometer reads “100% Transmittance.” Remove the tap water sample.
3. Pour some of the wastewater sample into another vial. Place the wastewater sample into the
machine and allow it run to determine the transmittance.
6. Record and repeat procedure for all samples tested.
pH PROCCEDURES
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1. Calibrate the pH meter by using the three pH Buffers (4.00, 7.00, and 9.00).
2. Rinse the electrodes off using distilled water.
TEMPERATURE PROCCEDURES
1. Rinse the electrodes off using distilled water.
2. Pour the wastewater sample into a small container.
3. Following stabilization, place the pH meter and record the temperature.
4. Repeat procedure for all samples tested.
STATISTICAL ANALYSIS PROCCEDURES
DEVELOPING BOX BEHNKEN DESIGN
1. Having opened the software, click the Stat option at the top of the page. 2. Move the mouse down to DOE (Design of Experiments). 3. Select Response and Create Response Surface Design. 4. Click the radio button to Box-Behnken Design and choose the number of factors. 5. Click the Designs option to ensure the number of replicates, whether blocking based on replicates, and the number of blocks. 6. Click OK and the experimental design will be created for the individual.
ANALYZE NON-RSM CREATED DESIGN
1. Having opened the software, place all data into the provided worksheet.
2. Click the Stat option at the top of the page.
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2. Move the mouse down to DOE (Design of Experiments).
3. Select Define Custom Response Surface Design.
4. Place the mouse in the “Factors” box and then select all factors designated for this experiment.
5. Click “Low/High” box and indicate the low and high parameters for that particular factor.
6. Click OK.
7. Click Stat-DOE-Response Surface-Analyze Response Surface Design.
8. Place mouse into “Responses” box and select response desired.
9. Click “Terms” and drag over appropriate relationships to be analyzed. Ensure that the full-quadratic is selected at the top of the screen.
10. In “Prediction,” select the same factors as the ones designated for the creation of the design. This will compare what was found in the experiment as compared to what was discovered in the results.
11. Click “Graphs.” Select “Four in One” under “Residual Plots.” Click OK twice.
12. To prepare Contour and Surface plots, select Stat-DOE-Response Surface-Contour and Surface Plots.
13. Check the box for “Contour Plots.”
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14. Select “Setup.” In factors, click the radio button that states, “Generate plots for all pair of reactors” and “In separate panels for the same graph.” Click OK twice.
15. To generate the Surface plots, repeat steps 12-14, with the exception of choosing
“Surface Plots” and then under “Generate plots for all pair of reactors,” check “On
Separate Graphs.”
CREATING THE MODEL
1. Having completed the experiment (making columns in completed design for quantitative values), click “Stat.”
2. Move the mouse down to DOE (Design of Experiments)..
3. Select Response and Analyze Response Surface Design.
4. For response, the experimenter would choose the measured quantity (for example,
%Transmittance).
5. Click Options and choose Four-in-one. This would allow for the understanding of all entities that would generate a regression analysis and also the experimental would be able to determine whether the model would fit a specific linear model.
6. Click OK and regression analysis would be completed.
7. If one returns and instead selects, contour/surface plots in the same option for Analyze
Response Surface Design, one can generate contour and surface plots. Use the steps 11-
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14 from 4.2.2.5.2 ANALYZE NON-RSM CREATED DESIGN to make this determination.
8. Click Setup and then choose Generate plots for all pairs of factors and make sure the radio button in separate panels on the same graph is chosen.
9. Click OK
10. Click OK.
Total Organic Carbon (352)
Turning On and Machine Operation
1. Open the valve on the Compressed Air
2. Turn on machine and printer.
3. Push “F5” on machine to initialize. Wait until the screen has finished finding the home position.
4. Using key pad, hit “F1” to access “Main Menu.”
5. Scroll down on keypad to #3 “General Conditions” and hit “Enter.”
6. Scroll down to “Furnace (On/Off)” to make sure that #1 “TOC” is selected.
7. Push “F2” to return to “Main Menu.”
8. Select to Option 6 “Monitor” to see how the temperature. Do operate machine until temperature reaches 650 C.
9. After temperature reaches 650 C, return to “Main Menu.” Select Option 9 “Auto
Sample” and press “Enter.”
10. Load samples into TOC analyzer.
11. Ensure that the FS (final sample) is equivalent to the number of samples in the
438 analyzer.
12. Hit “F1” to go to the next screen.
13. Repeat Step 12.
14. Hit “Start” on Machine.
Turning Off Machine
1. Return to “Main Menu and select option 3 “General Conditions” and hit “Enter.”
2. Scroll down to “Furnace (On/Off)” to make sure that #2 “Off is selected.
3. Push “F2” to “Main Menu.”
4. Select Option 6 “Monitor” to see the temperature. Do not turn off machine until temperature reaches 350 C.
5. Close the valve on the Compressed Air.
6. Return back to “Main Menu.”
7. Turn off machine and printer.
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VITA
The author, Erick Benjamin Butler, was born on July 3, 1985 in Cleveland, Ohio to
Hiram Benjamin and Patricia Gale Weston Butler. He is the eldest with two sisters, Erin Patrice and Erionna Denise Butler. Erick attended St. Ann School in Cleveland Heights, graduating in
1999. He attended St. Ignatius High School and graduated in 2003. Erick was accepted to the
University of Notre Dame du Lac in Notre Dame, Indiana, attending from 2003 to 2004, leaving following the death of his mother in September 2004. Erick enrolled at Cleveland State
University in 2005 and graduated with a Bachelor of Science in Environmental Science emphasis in Environmental Technology in December 2007. He pursued graduate studies and completed his
Master of Science in Environmental Engineering in December 2009. Erick began his coursework for his doctoral degree in January 2010. After his first semester of doctoral studies, Erick married his girlfriend of seven years, Jennifer Rand, on July 3, 2010 in Richmond Heights, Ohio. Erick has completed his coursework for his doctoral degree in May 2013, will graduate in August 2013, and begin his appointment as a Tenure-Track Assistant Professor of Environmental Engineering at West Texas A&M University, Canyon, Texas, beginning August 2013. He will be returning to
Cleveland State University in December 2013, to participate in the Commencement ceremony.
Permanent address:
Erick Butler
2380 Traymore Road
University Heights, Ohio 44118
Email: [email protected]
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