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A GAS-SOLID SPOUTED BED BIOREACTOR FOR SOLID STATE FERMENTATION

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Ellen M. Silva, M.S.

*****

The Ohio State University 1997

Dissertation Committee: Approved by Professor Shang-Tian Yang, Adviser

Professor Jeffrey Chalmers Adviser

Professor David Tomasko Department of Chemical Engineering UMI Number: 9731715

Copyright 1997 by Silva, Ellen Mae

All rights reserved.

UMI Microform 9731715 Copyright 1997, by UMI Company. All rights reserved.

This microform edition is protected against unauthorized copying under Title 17, United States Code.

UMI 300 North Zeeb Road Ann Arbor, MI 48103 Copyright by Ellen Mae Silva 1997 ABSTRACT

Solid state fermentation (SSF) is the culture of microorganisms on moist solid substrate in the absence of free-flowing water. Composting and silage making are two frequently practiced examples of SSF, but other large scale examples are rare in Western countries, despite the many advantages SSF offers. These advantages include improved productivities and higher product concentrations than in many submerged fermentations and the ability to use simple plant biomass, including agri-industry waste stream material, as a substrate. The drawbacks that discourage industrial application include solids handling difficulties, difficulty in measuring and controlling process parameters, heat and mass transfer limitations in existing SSF bioreactors, and a lack of kinetic and design data.

Spouted bed reactors are well known to have excellent heat and mass transfer characteristics and to easily handle solids of the types used for SSF substrates. For this reason, a novel spouted bed bioreactor for SSF was designed and tested to evaluate its ability to overcome some of the drawbacks previously mentioned.

Several fermentations for the production of amylases by Aspergillus oryzae grown on rice were run using various operating strategies (continual, intermittent, and no spouting, the latter of which is equivalent to packed bed operation) and the results were compared to static SSF fermentations.

11 Static fermentation studies indicated that the optimal conditions, within those tested, for production of amylases from rice by Aspergillus oryzae were 29% initial moisture content, initial pH of 6.2 (no adjustment from that naturally occurring), temperature of 35 -

37° C, and nutrient supplementation of 0.5% (w/w) yeast extract. Inoculum size did not affect production within the ranges tested. Lentils gave superior fermentation results compared to rice and several other substrates.

Fermentation in the gas-solid spouted bed bioreactor was found to be possible and to have several important advantages compared to the currently accepted best-performing reactor, the packed bed reactor with forced aeration. The best results, which were equivalent to the results achieved in the packed bed reactor, were achieved with intermittent spouting at 4 hr intervals. This was sufficiently frequent spouting to confer advantages of improved solids handling and uniformity compared to the packed bed fermentation. Overall protein productivity rates (which correlated to enzyme productivity) of about 6 mg/100 g reactor charge/h were achieved for both 4 hr intermittent spouting and packed bed operation; in comparison, deep static fermentation achieved less than 1 mg/100 g/h.

Hydrodynamic studies showed that existing literature correlations could be used to approximate experimentally determined minimum spouting velocity, Ums. Linear annular speed and fountain height of the spout both increased in a linear manner with spouting velocity, though bed height and reactor geometry affected the exact correlation. Increasing the air inlet diameter from 1.27 cm to 2.54 cm decreased the stability of the spout and increased Ums; increasing the moisture content of rice from about 20% (w/w) to 45% had a similar effect.

Ill to Wilson

IV ACKNOWLEDGMENTS

My grateful acknowledgment of many hours of consultation, advice, and encouragement goes to my adviser. Dr. Shang-Tian Yang. The relationship between an adviser and student can be critical to the success of a doctoral research project. I deeply appreciate the flexibility allowed me in scheduling my research by my adviser; without his understanding of my demands outside of the lab, my work here at the Ohio State University would not have been as fruitful or as pleasant.

I have also had advice and guidance from the members of my dissertation committee. Dr. Jeff Chalmers and Dr. David Tomasko, and I appreciate the additions they have made to this research with their ideas. Even more, I appreciate their advice and their thoughts offered over the years on a variety of topics.

For his assistance in running the static fermentation studies on pH and inoculum size, I thank Mr. Chad Laubenthal.

Early in the development of this project. Dr. John Davidson generously shared his knowledge of spouted beds and helped generate the idea of intermittent spouting as a possible operating strategy. I thank him for this. Other early assistance came from Dr. Ping Cai, who directed me toward pertinent spouted bed literature, and Dr. Peijun Jiang, who helped me get a reactor set up and running. My thanks go to both of these individuals.

Mr. Mike Kukla assisted with the design and construction of the reactor system; without him, I certainly would not have known where to start. Thank you, Mike.

Mr. Carl Scott did a fine job building the second reactor, and I appreciate that, but I thank him most of all for the many times he graciously allowed me to interrupt his work in order to get some small question answered or part fixed.

The staff of the Engineering Region 6 computer facilities, particularly Mr. Geoff

Hulse, Mr. Mike Davis, and Mark Grzesiakowski assisted me many times with both critical and trivial computing problems, and for that I am grateful.

I also appreciate the less direct, but equally important assistance of the administrative staff of the Department of Chemical Engineering, past and present, staff and work study students. Thanks for running the department so that we can run our research.

Dr. Jaques Zakin and Dr. Liang-Shih Fan have helped me many times, mostly in the area of getting fellowships. This is, of course, deeply appreciated, as is their personal fellowship.

My colleagues, both within my research group and in the Department of Chemical

Engineering, have inspired and encouraged me. There were, of course, a special few who offered support when it was needed and a reality check when that was needed, too. Thanks,

Paul, Rachel, Venkat, Suhas, Niki, Trish, and Yan.

VI Financial assistance from the National Science Foundation, the Ohio State

University Graduate Student Alumni Research Association, The Ohio State University, the

Consortium for Plant Biotechnology Research, and the Association for Women in Science is gratefully acknowledged.

Finally, those who are owed the greatest debt of gratitude are my family. Graduate school requires sacrifices of the student, but those are easy. The sacrifices of those for whom the student cares most are the hard ones. My parents, Mrs. Eloise Myers and Mr.

Gib Myers, and my parents-in-law, Mrs. Helen Wilson Gibbins and Dr. Neil Gibbins, have given of themselves in too many ways to list. Perhaps the most important thing they have done has been to forgive me the lack of time spent with them while I pursued this degree.

My daughters, Ms. Karen Gibbins and Ms. Jennifer Gibbins, have grown into fine, young women during this time and have kept my spirits high and my priorities straight. And, certainly, my heartfelt gratitude goes to my husband, Mr. Wilson Gibbins, for so many things; for computer advice, for coffee in the morning, for allowing me to clutter our lives with things like dogs and cats when it was already too cluttered with graduate school, for holding my hand during the tough spots, and for having faith in my ability to finish. I did not expect that one of the things I would learn in graduate school was what a wonderful husband I have.

VII VTTA

July 19, 1958 ...... Bom - Medina, Ohio, USA

1980 ...... B.S. Chemical Engineering The Ohio State University Columbus, Ohio, USA

1980 - 1983 ...... Engineer Diamond Shamrock Corporation Painesville, Ohio, USA

1985 - 1991 ...... Technical Writer Virginia Polytechnic and State University Blacksburg, Virginia, USA

1991 - present ...... Graduate Teaching and Research The Ohio State University Columbus, Ohio, USA

1993 ...... M.S. Chemical Engineering The Ohio State University Columbus, Ohio, USA

PUBLICATIONS

Research Publications

1. Yang, S.-T., H. Zhu, and E. M. Silva. 1993. Production of value-added products from agricultural and food processing byproducts. In Biotechnology In the 21st Century (C. Ayyanna, ed.) pp. 47-68, Tata McGraw-Hill Publishing Company Limited, New Delhi.

viii 2. Yang, S.-T., H. Zhu, Y. Li, and E. M. Silva. 1993. A novel multi-phase bioreactor for fermentations to produce organic acids from dairy wastes. Proceedings of \hc First Biomass Conference of the Americas, Burlington, Vermont, August 30 - September 2, 1993, Vol. 2, pp. 1223-1248.

3. Silva, E.M. and S T. Yang. 1995. Kinetics and stability of a fibrous-bed bioreactor for continuous production of lactic acid from unsupplemented acid whey. Journal o f Biotechnology 41:59-70.

4. Yang, S.T., and E.M. Silva. 1995. Novel products and new technologies for use of a familiar carbohydrate, milk lactose. Journal of Dairy Science 78:2541-2562.

5. Silva, E.M. and S.T. Yang. 1996. Biochemical engineering. In MacMillan Encyclopedia of Chemistry (J.J. Lagowski, ed.). MacMillan Publishing Company, New York.

FIELDS OF STUDY

Major Field: Chemical Engineering

Specialization: Biochemical Engineering

IX TABLE OF CONTENTS

Page

Abstract...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita...... viii

List of Tables ...... xiv

List of Figures ...... xv

Chapters:

1. Introduction ...... I

2. Literature Review...... 6

Introduction ...... 6 Solid state fermentation ...... 6 Introduction ...... 6 Description of SSF ...... 9 Organisms ...... 11 Substrates...... 12 Koji making ...... 13 Fermented foods and beverages from koji ...... 16 Reactor types ...... 18 Industrial processes ...... 19 Properties of SSF ...... 21 Advantages ...... 21 Disadvantages ...... 24 Important Physical Parameters in SSF ...... 26 Water activity...... 26 Substrate properties...... 30 Surface area...... 30

X Particle size and shape...... 30 Grain variety...... 31 Gas concentrations ...... 31 Temperature ...... 33 pH ...... 34 Inoculum size and preparation ...... 34 Transport properties in SSF ...... 35 Effect of reactor type ...... 35 Mass transport considerations ...... 37 Interparticle vs. Intraparticle ...... 39 Oxygen diffusion ...... 41 Enzyme and substrate diffusion ...... 41 Mass diffusivity...... 42 Heat transport considerations ...... 44 Thermal diffusivity...... 46 Determination of biomass in SSF ...... 47 Off-line, direct methods ...... 48 Off-line, indirect methods ...... 49 Glucosamine content ...... 49 Ergosterol and total sugar content ...... 49 DNA content ...... 50 Protein ...... 50 On-line methods ...... 50 Oxygen uptake ...... 51 Off-gas carbon dioxide analysis ...... 52 Light scattering ...... 52 IR analysis...... 53 Pressure drop ...... 54 Microscopic techniques ...... 55 Control and regulation of metabolism and differentiation ...... 55 Improved reactor design ...... 56 Fluidized beds ...... 56 M odeling ...... 59 Models of growth kinetics ...... 60 Models based on product formation ...... 61 Med s based on substrate consumption ...... 67 Models based on oxygen concentration ...... 68 Models based on temperature/heat transfer models ...... 72 Aspergilli ...... 78 Amylases...... 80 Spouted bed technology...... 83 Description of spouted beds ...... 83 Applications ...... 84 Spout formation ...... 85 Design of spouted bed reactors ...... 87

xi Other design considerations ...... 93 Modifications to spouted beds ...... 95 Applicability to SSF ...... 97 Potential ...... 97 Feasibility...... 100

3. Production of amylase by Aspergillus oryzae in static solid state fermentation 106

Abstract...... 106 Introduction ...... 107 Materials and methods ...... 109 Organisms ...... 109 Substrate...... 111 Inoculum preparation ...... 112 Fermentation ...... 114 Analytical techniques ...... 118 Dry weights ...... 119 Glucose analysis ...... 119 Starch analysis ...... 119 Enzyme analysis ...... 120 Enzyme extraction ...... 120 a-Amylase determination ...... 121 P-Amylase analysis ...... 123 Glucoamylase activity ...... 126 Protein analysis ...... 126 Measurement of pH in solid m edia ...... 128 Sample handling study ...... 128 Fermentation Studies ...... 130 Organism study ...... 130 Inoculum quantity study ...... 131 Initial pH ...... 131 Initial moisture content ...... 131 Temperature effect ...... 132 Nutrient supplementation ...... 132 Substrate depth ...... 132 Alternative substrate study ...... 123 Results and Discussion ...... 133 Organism comparison ...... 133 Inoculum size ...... 134 Initial pH ...... 135 Initial moisture content ...... 136 Temperature effects ...... 139 Nutrient supplementation effects ...... 140 Effects of substrate bed depth ...... 141 Alternative substrates...... 143 xii Effects of environmental parameters on sporulation and enzyme production...... 145 Conclusions ...... 149 References...... 149

4. Solid state fermentations in a gas-solid spouted bed bioreactor ...... 184

Abstract...... 184 Introduction ...... 184 Materials and Methods ...... 189 Organism ...... 189 Analysis...... 189 Inoculation and spore germination ...... 191 Reactors ...... 192 Reactor operation ...... 192 Humidification ...... 192 Reactor startup ...... 193 Continual spouting ...... 194 Intermittent spouting ...... 195 Packed bed fermentation (no spouting) ...... 195 Sampling of spouted and packed bed fermentations ...... 196 Static fermentations ...... 197 Runs in reactor B ...... 197 Results and Discussion ...... 198 Static Fermentation ...... 198 Continuous spouting ...... 201 Intermittent spouting ...... 203 Packed bed fermentation ...... 204 Comparison of productivity ...... 205 Conclusions ...... 208 References...... 209

5. Hydrodynamics of gas-solid spouted beds for solid state fermentation ...... 235

Abstract...... 235 Introduction ...... 236 Materials and methods ...... 237 Reactors...... 237 Particles...... 238 Size and Density Measurement...... 238 Experimental Procedures ...... 239 Results and Discussion ...... 240 Effect of moisture content on grain size and density ...... 240 Hydrodynamic studies...... 241 Fountain height ...... 242

xiii Minimum spouting velocity ...... 243 Annular speed and mixing time ...... 245 Summary of measured hydrodynamic parameters...... 247 Spoutability ...... 248 Correlations for minimum spouting velocity ...... 249 Conclusions ...... 255 References...... 260

6. Conclusions and recommendations ...... 283

Conclusions ...... 283 Static fermentation conclusions ...... 284 Spouted bed bioreactor fermentations ...... 284 Reactor hydrodynamics ...... 285 Recommendations ...... 286

Bibliography ...... 287

Appendices:

A. Analytical flowsheet and calculations ...... 301

B. Protein reagents used for analysis ...... 304

C. Correlations for minimum spouting velocity ...... 306

XIV LIST OF TABLES

Table Page

2.1 Advantages of SSF ...... 9

2.2 Examples of solid state fermentations found in the literature...... 14

2.3 Aspergillus spp. used in industrial production of enzymes ...... 81

2.4 Applications of spouted bed technology ...... 85

2.5 Correlations for Ums- ...... 91

3.1 Nutritional analysis of various grain substrates used in this study. Percentages may add to more than 100% because the fiber content is included in the total carbohydrate as well as being expressed separately 113

3.2 Water and rice quantities used to prepare substrates of varying initial moisture content...... 116

3.3 Experimental treatments for study of the effect of substrate bed depth on amylase fermentation...... 118

3.4 Comparative cultivation characteristics of several filamentous fungi. 133

3.5 Effects of fermentation temperature on maximum protein productivity and maximum protein concentration ...... 140

3.6 Effects of nutrient supplementation on maximum protein productivity and maximum protein concentration ...... 141

3.7 Effects of substrate bed depth on maximum protein productivity and maximum protein concentration ...... 142

XV 3.8 Effects of use of alternative substrates on maximum protein productivity and maximum protein concentration ...... 144

3.9 Effect of initial moisture content (MC) on sporulation and growth. + = mycelial patches evident, ++ = light confluent growth, +++ = medium mycelial growth, -H-H- = heavy (cottony) mycelial growth, * = first evidence of sporulation (unpigmented conidiophores), ** = pigmented spores readily visible, *** = dense sporulation, **** = sporulation to the point that rice is not visible...... 146

3.10 Effect of nutrient supplementation with yeast extract on sporulation and growth. + = mycelial patches evident, ++ = light confluent growth, +++ = medium mycelial growth, i i i i = heavy (cottony) mycelial growth, * = first evidence of sporulation (unpigmented conidiophores), ** = pigmented spores readily visible, *** = dense sporulation, **** = sporulation to the point that rice is not visible...... 148

3.11 Effect of temperature on sporulation and growth. + = mycelial patches evident, ++ = light confluent growth, +++ = medium mycelial growth, ++++ = heavy (cottony) mycelial growth, * = first evidence of sporulation (unpigmented conidiophores), ** = pigmented spores readily visible, *** = dense sporulation, **** = sporulation to the point that rice is not visible ...... 148

4.1 Operating parameters for intermittent spouted bed fermentations ...... 196

5.1 Cycle and mixing times estimated for Reactor A; Us = 1.2 Ums, bed height 5 cm ...... 247

5.2 Cycle and mixing times estimated for Reactor B; Us = Ums. variable bed height...... 247

5.3 Summary of some experimental parameters for Reactor A ...... 248

5.4 Summary of some experimental results for Reactor B ...... 249

5.5 Correlations for minimum spouting velocity ...... 250

5.6 Fit of various literature correlations to experimental Ums: diameter used = Dcfr...... 252

5.7 Fit of various literature correlations to experimental Ums: diameter used = Dave...... 253

5.8 Sum of squared residuals for all experimental data ...... 256

XVI 5.9 Sum of squared residuals for data for Dj = 1.27 cm inlet ...... 257

5.10 Sum of squared residuals for data for D; = 1.9 cm inlet ...... 258

5.11 Sum of squared residuals for data for D, = 2.54 cm inlet ...... 258

5.12 Sum of squared residuals for correlations using modified average diameter... 259

B. 1 Protein reagent used for various experiments and effect on slope of standard curve...... 305

xvu LIST OF FIGURES

Figure Page

1.1 Areas investigated in the current study on solid state fermentation in a spouted bed bioreactor ...... 5

2.1 Potential enzyme production process ...... 101

2.2 Processes occurring in SSF ...... 102

2.3 Schematic of diffusion in mycelial pellet ...... 103

2.4 Circulation patterns in a spouted bed; a) gas; b) particles ...... 104

2.5 Schematic diagram of a spouted bed reactor ...... 105

3.1 Spore density determination; a) counting pattem; b) photomicrograph showing counting chamber in use ...... 152

3.2 Standard curve for analysis of reducing sugar by the dinitrosalicylic acid method, glucose as standard...... 153

3.3 Standard curve for analysis of protein by the Bradford method, reagent prepared by OSU laboratory stores ...... 154

3.4 Correlation of protein with a-amylase acitivity ...... 155

3.5 Correlation of a- and ^-amylase activity ...... 156

3.6 Correlation of a- and glucoamylase activity ...... 157

3.7 Effect of extraction method on the determination of a-amylase activity, fermentation time = 2 d...... 158

3.8 Effect of storage method and time on a-amylase activity, fermentation time = 2 d ...... 159

xviu 3.9 Comparison of koji fermentations of A. oryzae and A. niger ...... 160

3.10 Effect of inoculum size on extracellular protein production in an A. oryzae fermentation, 37°C, initial moisture content o f-41%, (w/w), no adjustment of initial p H ...... 161

3.11 A. oryzae fermentation of brown rice at 37“C inoculated with 1.45x 10^ spores per flask (22.5 g dwt rice); a) moisture content, starch, pH, and protein kinetics; b) enzyme and protein kinetics...... 162

3.12 A. oryzae fermentation of brown rice at 37°C inoculated with 1.45x10^ spores per flask (22.5 g dwt rice); a) moisture content, starch, pH, and protein kinetics; b) enzyme and protein kinetics ...... 163

3.13 A. oryzae fermentation of brown rice at 37°C inoculated with 1.45x 10^ spores per flask (22.5 g dwt rice); a) moisture content, starch, pH, and protein kinetics; b) enzyme and protein kinetics ...... 164

3.14 Effect of initial pH on fermentation kinetics in an A. oryzae brown rice fermentation (initial moisture content -40%, 37°C); a) moisture content; b) pH ...... 165

3.15 Effects of initial pH on fermentation kinetics in an A. oryzae brown rice fermentation (initial moisture content -40%, 37°C); a) substrate consumption; b) protein production ...... 166

3.16 Effect of initial pH on fermentation kinetics in an A. oryzae brown rice fermentation (initial moisture content -40%, 37°C); a) a-amylase production; b) P-amylase production; c) glucoamylase ...... 167

3.17 Fermentation kinetics for A. oryzae grown on brown rice with an initial moisture content of 29%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production ...... 168

3.18 Fermentation kinetics for A. oryzae grown on brown rice with an initial moisture content of 39%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production ...... 169

3.19 Fermentation kinetics for A. oryzae grown on brown rice with an initial moisture content of 46%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production ...... 170

XIX 3.20 Fermentation kinetics for A. oryzae on brown rice with an initial moisture content of 49%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production ...... 171

3.21 Kinetics of free glucose present in crude extract from cultures of A. oryzae grown on brown rice at varying initial moisture contents ...... 172

3.22 Effect of initial rice moisture content on A. oryzae fermentation kinetics (no pH adjustment, 37°C); a) protein production; b) substrate consumption 173

3.23 Effects of initial rice moisture content on A. oryzae fermentation kinetics; a) pH; b) moisture content ...... 174

3.24 Effects of initial rice moisture content on enzyme production in A. oryzae fermentation (no pH adjustment, 37°C); a) a-amylase production; b) P- amylase production; c) glucoamylase production ...... 175

3.25 Correlation of protein production to substrate (starch) consumption; data from initial moisture content experiments ...... 176

3.26 Correlation of protein production to substrate (starch) consumption; data from initial pH adjustment experiments ...... 177

3.27 Effect of temperature on protein production in A. oryzae koji fermentation on brown rice (initial rice moisture content, -32.4% ) ...... 178

3.28 Effect of nutrient supplementation on protein production in a koji fermentation of A. oryzae on brown rice (initial rice moisture content -33%, 37°C)...... 179

3.29 Effect of substrate bed depth on protein production in a koji fermentation of A. oryzae on brown rice — study number 1 and 2 combined (initial rice moisture content -40%, 37°C) ...... 180

3.30 Effect of substrate bed depth on protein production in a koji fermentation of A. oryzae on brown rice ~ study number 3 (initial rice moisture content -38%, 37°C); abbreviated fermentation time ...... 181

3.31 HPLC chromatogram indicating presence of fermentative metabolism products at the end of a SSF of A. oryzae on brown rice ...... 182

3.32 A. oryzae culture on various grain substrates ...... 183

4.1 Example of protein standard analysis curve ...... 212

XX 4.2 Schematic diagram of Reactor A ...... 213

4.3 Schematic diagram of Reactor B...... 214

4.4 Schematic diagram of reactor system ...... 215

4.5 Placement of heating tapes on spouted bed bioreactor ...... 216

4.6 Location of sample ports on spouted bed bioreactor ...... 217

4.7 Comparison of protein production kinetics in deep static and shallow static fermentations...... 218

4.8 Typical static fermentation enzyme production kinetics ...... 219

4.9 Production of protein in a continually spouted, spouted bed bioreactor by Aspergillus oryzae grown on rice...... 220

4.10 Comparison of protein production in various regions of a spouted bed bioreactor ...... 221

4.11 Fermentation kinetics for production of amylases and protein by Aspergillus oryzae grown on rice in a spouted bed bioreactor - intermittent spouting with a 1 hour interval ...... 222

4.12 Fermentation kinetics for production of amylases and protein by Aspergillus oryzae grown on rice in a spouted bed bioreactor - intermittent spouting with a 4 hour interval ...... 223

4.13 Fermentation kinetics for production of amylases and protein by Aspergillus oryzae grown on rice in a packed bed bioreactor ...... 224

4.14 Effect of position in reactor column on protein production in a packed bed bioreactor ...... 225

4.15 Effect of position in reactor cone on protein production in a packed bed bioreactor ...... 226

4.16 Comparison of operational strategies for a spouted bed bioreactor -- protein production ...... 227

4.17 Comparison of operational strategies for a spouted bed bioreactor; a- amylase production ...... 228

XXI 4.18 Comparison of operational strategies for a spouted bed bioreactor; p- amyiase production ...... 229

4.19 Comparison of operational strategies for a spouted bed bioreactor; glucoamylase production ...... 230

4.20 Effect of reactor type and operating strategy on protein productivity ...... 231

4.21 Effect of reactor type and operating strategy on enzyme productivity ...... 232

4.22 Effect of operating conditions on protein production in a spouted bed bioreactor - intermittent spouting, 1 hour interval ...... 233

4.23 Demonstration of protein production in Reactor B ...... 234

5.1 The effect of moisture content on rice size and density...... 262

5.2 Relationship between rice density and moisture content ...... 263

5.3 Effect of spouting on moisture content ...... 264

5.4 Relationship between holdup and bed height ...... 265

5.5 Effect of spouting velocity and holdup on fountain height; Reactor B, 1.27 cm inlet; a) 28.8% (w/w) moisture content; b) 32.5%; c) 39%; d) 39.4%.... 266

5.6 Effect of spouting velocity on fountain height; Reactor B, 1.9 cm inlet; Reactor B, 1.9 cm inlet; a) 28.8% (w/w) moisture content; b) 32.5%; c) 39% ...... 268

5.7 Effect of spouting velocity on fountain height; Reactor B, 2.54 cm inlet; a) 28% (w/w) moisture content; b) 32.5%...... 269

5.8 Effect of moisture content and bed height on fountain height...... 270

5.9 Effect of bed height on minimum spouting velocity. Reactor A, 1.27 cm inlet...... 271

5.10 Effect of bed height on minimum spouting velocity. Reactor B, a) 1.27 cm inlet, b) 1.9 cm inlet, c) 2.54 cm inlet ...... 272

5.11 Effect of inlet diameter and bed height on minimum superficial spouting velocity and minimum inlet superficial spouting velocity for various moisture content rice in Reactor B ...... 273

xxii 5.12 Effect of spouting velocity on annular speed at various bed heights, Reactor A; a) 26.6% moisture content (w/w), b) 36.2% moisture content (w/w), c) 37.2% moisture content (w/w) ...... 274

5.13 Overall relationship between annular speed and spouting velocity. Reactor A ...... 275

5.14 Linear relationships between annular speed and spouting velocity for particular bed heights. Reactor B, 1.27 cm inlet ...... 276

5.15 Effect of spouting velocity on annular speed at various moisture contents. Reactor B; a) 1.27 cm inlet, b) 1.9 cm inlet, c) 2.54 cm inlet ...... 277

5.16 Effect of inlet diameter and spouting velocity on annular speed. Reactor B ...... 278

5.17 Relationship between mixing time and bed height. Reactor B, 1.27 cm inlet, 28% moisture content rice ...... 279

5.18 Best overall fit of correlations to experimental data ...... 280

5.19 Best fit of correlations to Reactor A data ...... 281

5.20 Best fit correlations to Reactor B data; a) 1.27 cm inlet; b) 1.9 cm inlet; c) 2.54 cm inlet...... 282

A. 1 Flowsheet of analytical procedures performed on each koji sample; bolded letters/number indicate data points taken...... 302

A.2 Calculations for analysis of koji samples; variables taken from flowsheet in Figure A. 1 ...... 303

C. 1 Correlations for minimum spouting velocity compared to experimental data; Reactor A, 9.8% moisture content (w/w); a) using Deff in calculation, b) using Dave in calculation ...... 307

C.2 Correlations for minimum spouting velocity compared to experimental data; Reactor A, 26.6% moisture content (w/w); a) using Deff in calculation, b) using Dave in calculation...... 308

C.3 Correlations for minimum spouting velocity compared to experimental data; Reactor A, 36.2% moisture content (w/w) ; a) using Deff in calculation, b) using Dave in calculation ...... 309

XXlll C.4 Correlations for minimum spouting velocity compared to experimental data; Reactor A, 37.2% moisture content (w/w) ; a) using Deff in calculation, b) using Dave in calculation ...... 310

C.5 Correlations for minimum spouting velocity compared to experimental data; Reactor B, 28% moisture content (w/w), 1.27 cm inlet; a) using Deff in calculation, b) using Dave in calculation ...... 311

C.6 Correlations for minimum spouting velocity compared to experimental data; Reactor B, 32.5% moisture content (w/w), 1.27 cm inlet ; a) using Deff in calculation, b) using Dave in calculation...... 312

C.7 Correlations for minimum spouting velocity compared to experimental data; Reactor B, 28% moisture content (w/w), 1.9 cm inlet ; a) using Deff in calculation, b) using Dave in calculation ...... 313

C.8 Correlations for minimum spouting velocity compared to experimental data; Reactor B, 32.5% moisture content (w/w), 1.9 cm inlet; a) using Deff in calculation, b) using Dave in calculation ...... 314

C.9 Correlations for minimum spouting velocity compared to experimental data; Reactor B, 28% moisture content (w/w), 2.54 cm inlet ; a) using Deff in calculation, b) using Dave in calculation ...... 315

C. 10 Correlations for minimum spouting velocity compared to experimental data; Reactor B, 34.5% m oisture content (w/w), 2.54 cm inlet; a) using Deff in calculation, b) using Dave in calculation...... 316

XXIV CHAPTER 1

INTRODUCTION

Large amounts of excess biomass are produced by US agri-industry every year. It is desirable to use this as a renewable resource for sustainable chemical production via microbial cultivation. If not used to generate a value-added product, the biomass will remain in the waste stream and generate waste disposal or treatment costs instead.

Furthermore, by substituting microbial processes based on biomass for synthetic routes based on petrochemicals, the national security and economics are enhanced.

Of special interest are processes which combine microbial cultivation simultaneously with the hydrolysis of the biomass, as is found in many solid state fermentation (SSF) systems. Such processes reduce waste disposal costs while producing additional value-added products for the agri-industry, providing economic benefit to US agriculture while enhancing national competitiveness, all in an environmentally friendly way.

In solid state fermentation, microorganisms grow on moist solid substrate in absence of free-flowing water (Ramana Murthy et al., 1993). This is in contrast to submerged fermentation (SmF), in which the substrate is either dissolved or totally

immersed in the liquid fermentation broth. SSF offers higher production rates and easier

product recovery in many cases compared to submerged fermentation, along with the

ability to use many agri-industry waste streams, such as com fiber and bagasse, as

substrates; however, a lack of applications research and design data has prevented

Western industry from taking advantage of its potential. By virtue of its use of plant

biomass as a substrate, SSF can become a sustainable system of production of products

such as enzymes, organic acids, or pharmaceuticals from natural resources, providing

economic benefit to US agriculture and increasing national competitiveness, but more

fundamental knowledge and practical demonstrations of its usefulness are needed.

Methods to overcome problems experienced in conventional SSF systems, including heat

transfer limitations, mass transfer limitations, solids handling difficulties, and difficulty in

measuring process parameters (Ramana Murthy et al., 1993; Moo-Young et al., 1983,

Lambert, 1983), are also needed.

Spouted bed reactors provide good mixing and heat and mass transport and are suited to spouting the coarse, sticky particles typical of SSF substrates ^ a th u r and

Epstein, 1974). Though there have been no literature reports found that describe the use of a spouted bed bioreactor for SSF, there are reports of SSF in fluidized bed reactors that demonstrated improved productivity and control of process parameters over standard packed bed SSF (Moebus and Tueber, 1986). Spouted bed bioreactor SSF should provide similar benefits with the added advantage that it can be applied to larger particles and will require less energy than fluidization does. If effective in overcoming the aforementioned difficulties, a spouted bed bioreactor for solid state fermentation would provide exciting

new technology for the fermentation industry. This project proposes the development of

a spouted bed bioreactor (SBB) to overcome these difficulties.

To reach this goal, the following three objectives were proposed:

1. Study the kinetics of static SSF for an industrially relevant system.In order

to appreciate any benefits conferred on SSF processes by a novel bioreactor, it was first

necessary to understand the conventional cultivation methods. Furthermore, such studies

were expected to augment the scarce data for SSF kinetics that currently exist in the

literature. Production of amylases hy Aspergillus oryzae was chosen as the model system

in this study. The experiments on static SSF are described in Chapter 3, along with a

discussion of the results and their implications.

2. Demonstrate fermentation in a spouted bed bioreactor and study the kinetics

of enzyme production. Though not previously used for fermentations, a spouted bed

reactor was expected to improve SSF productivity because of the high mass and heat

transfer rates typical of such reactors. This new process may produce many industrially

important enzymes from solid plant biomass at reduced costs. Demonstration of a

successful fermentation in the reactor was critical to the project. Efforts to perform a SSF

are described in Chapter 4.

3. Investigate the hydrodynamics of tbe spouted bed bioreactor.Though other workers have investigated the hydrodynamics of ordinary and modified spouted bed reactors, the investigation of spouting in the experimental system at hand was thought to be useful. SSF substrates tend to be stickier than materials to which spouting is usually applied, so the hydrodynamics studies were considered critical to guiding future design

work. The design of the reactors and the results and discussion of the hydrodynamics

studies are given in Chapter 5.

These objectives fit into an overall plan to develop a basic understanding of some

of the important issues in solid state fermentation and to offer a significant improvement

to current tehniques. Figure 1.1 diagrams how the various parts of the research plan

integrate into the whole.

A project of this magnitude must have thorough underpinning in the existing knowledge. Though some background information is given in each of the chapters mentioned above, a more detailed review is useful for novices and experienced workers in the field. To this end. Chapter 2 provides a literature review of solid state fermentation, the particular organisms and products of interest in this work, and spouted bed technology and how it might relate to SSF.

Chapter 6 provides a chance to reflect on the results contained in Chapters 3 through 5 and to integrate them into meaningful conclusions. During the work, numerous new questions arose, and these are also included in Chapter 6 as recommended work for future research.

Given the numerous advantages demonstrated for SSF, it is of great interest to encourage industry to develop this technique. The key to unlocking the industrial potential of SSF is acquiring a better understanding of the process and providing better tools for its application; the research described herein will advance this effort. A Gas-solid Spouted Bed Bioreactor for Solid State Fermentation: Research Overview

Literature Review (Chapter 2)

Static SSF Spouted SSF Hydrodynamics (Chapter 3) (Chapter 4) (Chapter 5)

organism feasibility spoutability inoculum size kinetics moisture content operating strategy annular speed operating mixing time temperature conditions validity of substrate depth advantages correlations alternative difficulties improved use of substrate correlations

Conclusions and Recommendations (Chapter 6)

Figure 1.1. Areas investigated in the current study on solid state fermentation in a spouted bed bioreactor CHAPTER 2

LITERATURE REVIEW

INTRODUCTION

Biochemical engineering dwells in the overlap of disciplines; this dissertation is no exception to that rule. In order to understand fully the implications of the engineering improvements proposed in the novel bioreactor, an appreciation for the biology of the microorganism and the fermentation process involved must be developed. For an area as little studied as solid state fermentation, a detailed literature review is not just desirable, it is necessary. Sufficient background in spouted bed reactor technology is also required to allow an appreciation for the particular attributes of the reactor that suit solid state fermentation so well.

SOLID STATE FERMENTATION

Introduction

In solid state fermentation (SSF), microorganisms grow on moist solid substrate in absence of free-flowing water (Ramana Murthy et al., 1993). This is in contrast to submerged fermentation, in which the substrate is either dissolved or totally immersed in

the liquid fermentation broth. Many food fermentations, such as soy sauce, tempeh, and

rice wine production, use SSF. Other products produced by SSF include enzymes, primary metabolites such as organic acids, and secondary metabolites such as antibiotics.

Waste treatment via composting and some biotransformations also qualify as SSF processes.

SSF offers many advantages over submerged fermentation (SmF) for the development of a sustainable system of chemical production from natural resources

(Mudgett, 1986; Ramana Murthy et al., 1993; Hesseltine, 1972; Moo-Young et al., 1983;

Aidoo et al., 1982; Lambert, 1983). The most compelling advantage of SSF is that it is a more natural state for the organisms being grown, which may have physiological benefits.

For instance, some enzymes are produced by given organisms only in SSF, not SmF (Lin et al., 1993). In addition to the production of certain products only in SSF, not SmF, many instances have been demonstrated where a product which may be produced under

SmF conditions is produced in greater quantity in SSF. More pigments were produced when Monascus spp. were grown in SSF than in SmF (Lin and lizaka, 1982). Some of the earliest modem Western studies of SSF showed that aflatoxin production on rice was much higher in SSF than in SmF (Hesseltine, 1972). Amylase and protease production by

Eiipenicilliiim javanicum increased in a fluidized bed reactor (FBR) compared to SmF

(Tanaka, 1986) and citric acid production in SSF was increased over that in SmF (Aidoo et al., 1982). Table 2.1 lists some additional reported advantages of SSF as compared to

SmF. Unfortunately, the ease with which SmF processes could be developed and controlled and the lack of design information for SSF led to a near total dependence on

SmF in the Western fermentation industry. In Japan, however, continued development of

SSF based on traditional food fermentations occurred, showing that SSF techniques could be used profitably to produce enzymes, fermented foods, and other chemicals (Aidoo et al., 1982).

The major disadvantage of SSF is heat transfer limitations (Ramana Murthy et al.,

1993; Moo-Young et al., 1983, Lambert, 1983). Neither packed bed or tray reactors provide mixing and as a result, both thermal and mass transfer gradients develop

(Saucedo-Castenedo et al., 1990; Gowthaman et al., 1993; Ghildyal et al., 1992).

Spouted bed reactors provide mixing (Mathur and Epstein, 1974) and should eliminate thermal and mass heterogeneties in SSF, at least in the bulk gas. Furthermore, a spouted bed would allow additional nutrients to be mixed thoroughly into the medium, in contrast to the situation in conventional reactors. There have been no literature reports found that describe the use of a spouted bed bioreactor for SSF, or for fermentation of any sort. If effective in overcoming the aforementioned difficulties, a spouted bed bioreactor for solid state fermentation (SBB-SSF) would provide exciting new technology for the fermentation industry. This project proposes the development of a spouted bed bioreactor

(SBB) to overcome these difficulties. Characteristics Advantages/Comments

Low moisture content • lower reactor volume required for a given productivity • lower purification costs because of higher product concentrations • lower costs for treatment of liquid effluent • inhibition of contaminants

High interfacial • aeration is easily achieved surface area to liquid • generally lower power requirements volume ratio

Simulates natural • allows more complete microbial genetic expression microbial environment • some products produced at higher rates in SSF • some products produced only in SSF • yields are reliable and often reproducible

Simple media • often unprocessed grains with minimal mineral supplementation or no supplementation at all • may consist of agri-industry wastes such as corn fiber or sugarcane bagasse

Substrate availability • may actually increase during fermentation, rather than always decreasing as it does in SmF

Table 2.1. Advantages of SSF

Description of SSF

Since the I940's, SSF has taken the back seat to submerged fermentation in terms of research interest and industrial application in Western countries. The simplicity of handling dissolved substrates in SmF and the ease with which process variables such as pH and temperature can be controlled have led to near total domination of Western fermentation industries by SmF. As a result, little information is available on SSF in

9 English. In Japan, however, SSF has remained a subject of great interest to academic and

industrial researchers. Many SSF plants operate in Japan, producing enzymes and food

products. The last 10-15 years have seen an increase in interest in SSF outside of Japan

(Ramana Murthy, et al. 1993), but there is still a dearth of information of SSF process

design in the Western scientific literature.

SSF is worthy of much more research for two main reasons: it may have

significant advantages over submerged culture for production of foods, pharmaceuticals,

and chemicals; and it may provide a method of using excess biomass generated by agri­

industry, converting waste streams into profit sources. SSF is not well understood on a

scientific or an engineering basis (Mudgett, 1986), and further research is necessary to

access all of the possibilities SSF offers.

In solid state fermentation, microorganisms grow on moist solid substrate in

absence of free-flowing water (Ramana Murthy et al. 1993). This is in contrast to

submerged fermentation, in which the substrate is either dissolved or totally immersed in

the fermentation broth. The growth of koji, an enzyme-rich mold grown on shallow trays

of steamed rice, is a classical example of SSF. Steamed rice is inoculated with the spores

of Aspergillus oryzae or Aspergillus sojae and kept in a temperature and humidity controlled room for a number of days. The mold germinates and produces hydrolytic enzymes which act on the rice starch, and when the fermentation is finished, the koji is used for the enzyme treatment of other fermentation substrates or as a substrate for a subsequent fermentation itself. Soy sauce production, for example, begins with a koji fermentation (Fukushima, 1989). Koji production appears in written records in Japan as

1 0 long ago as 1200 BC, and probably was derived from a similar Chinese fermentation even

earlier (Fukushima, 1989).

SSF may be desirable compared to SmF because it simulates the natural

environment in which the microorganism may have evolved, perhaps allowing more

complete expression of the genetic potential in the microbe and production of compounds

of commercial interest. Rotting wood, composting leaves, and food spoiling in the back

of the refrigerator are all examples of natural SSF. In these fermentations, yeast and

bacteria are found on the solid surfaces, while filamentous fungi penetrate far into the

porous substrate (Moo-Young, et al., 1983).

Organisms

SSF organisms include bacteria and yeasts, which grow on the surface of the

substrate, and fungi, the mycelia of which penetrate into the substrate (Moo-Young et al.,

1983). Whatever species, requirements for successful use of an organism in SSF include

not only the production of a desired product, but the ability to metabolize the solid

substrate. This ability may be as a result of exoenzymes produced by the organism which

hydrolyze the substrate into monomers which are then taken into the cell as nutrient, or

may be as a result of exoenzymes produced by another species grown in mixed culture

with the organism which produces the desired product. Natural SSF processes frequently

are of this sort (Pandey, 1992), a mixed culture in which several species exhibit symbiotic

relationships. Composting and ensiling are examples of natural SSF processes.

Pure culture SSF processes are often used industrially to exert greater control over product formation. Mixed culture processes are possible here as well, but the mixture is

11 defined and may be only two species, one to hydrolyze the substrate and one to produce a

product, as opposed to the dozens that occur in natural SSF processes.

Either bacteria or fungi may be used for SSF. Fungi are especially suited to SSF

because of their tolerance of low water activity. PhycomycetesfAf«cor and Rhizopus),

ascomycetes {Aspergillus and Pénicillium), and basidiomycetes (white rot fungi,

Polyporus) have been the most frequently used fungi for SSF. Numerous example

applications demonstrating the many possibilities for SSF with these and other organisms

are given in Table 2.2.

Substrates

In SSF, the solid may have the role solely of support or may provide nutrients including carbon, nitrogen, and growth factors, as well. The latter case is of great interest as it includes polymeric substrates which are often generated in large quantities by agricultural processing industries; these substrates include polysaccharides (cellulose and starch), protein, lignin, nucleic acids, pectate, hemicellulose, alginate, and chi tin. These polymers must be enzymatically hydrolyzed into subunits which can pass through the cytoplasmic membrane into the cell. This enzymatic process is strongly affected by the particle size, shape, surface-to-volume ratio, crystallinity, and porosity of the substrate.

Because of the heterogeneous nature of typical SSF substrates, model substrates are often used to study microbial growth kinetics (Mitchell et al., 1986; Georgiou and

Shuler, 1986).

12 Koji making

Koji, as mentioned above, is a cereal upon which mold has been grown in order to produce enzymes, such as amylases and proteases, in preparation for further fermentations. Koji is mixed with additional substrate and the enzymes hydrolyze the substrate, providing nutrients for various organisms, functioning similar to malt in beer brewing. This provides the basis for many oriental food fermentations, the particular enzymes required for a given fermentation being customized by varying starter varieties of the mold, usually an Aspergillus oryzae variety, and by altering process conditions to favor formation of particular enzymes (Ebine, 1989).

Several enzymes are produced at various times in the course of a koji fermentation.

Amylases are produced to degrade the starch in the substrate to provide glucose for the organism to use for growth. Invertase hydrolyses any sucrose present, which can account for the increase in reducing sugar sometimes noted in a koji fermentation before any amylase activity is present, an event that usually occurs at about 24 hours (Lotong, 1985).

A typical koji fermentation progresses from inoculation to the appearance of white spots of mycelia on the substrate surface at about 20 hours, at which time growth proceeds rapidly with an accompanying increase in heat generated, requiring stirring or other means to reduce the temperature of the fermenting mass. As the fermentation continues, the mass becomes more and more compact and the characteristic smell of koji, probably produced by leucinic acid, develops (Lotong, 1985; Hara et al., 1992).

13 Organism Substrate Product Reference

Pénicillium chrysogenum sugar cane bagasse penicillin Barrios-Gonzilez et al.. 1993

Trichoderma viridelS sugar beet pulp protein Durand & Chereau. 1988

Chaelomium cellulolyticum straw animal feed Hecht et al.. 1985

Aspergillus ochraceus wheat ochratoxin A Lindenfelser & Ciegler. 1975

Polyporus spp. sugar cane bagasse feed Nigam. 1990

C. cellulolyticum paper mill wastes feed Pamment et al.. 1979

C. cellulolyticum sawdust feed Pamment et al. 1978

Pestalotiopsis versicolor bagasse cellulase Rao. et al.. 1983

Aspergillus Jlavus NRRL 2999 rice aflatoxin Hesseltine. 1972

Pénicillium candidum wheat bran lipolytic enzymes Ortiz-Vdzquez, et al. 1993

Aspergillus niger wheat bran acid protease Villegas et al.. 1993

Saccharomyces spp. sweet potato residue animal feed Yang. 1993

Scliwanniomyces occidentalisBS'i

Candida lipolytica

Aspergillus niger Tainan

Rhizopus sp. NRRL-688. NRRL-695. TBR

Aspergillus oryzae rice koji for soy sauce Wood & Min. 1975

Rhizopus spp. soybean tempeh Wood & Min. 1975

Aspergillus spp. rice & soybean miso Wood & Min. 1975

Aspergillus spp. barley & soybean mi so Abiose et al.. 1982

Monascus kaolingP-2 (ATCC rice pigment Lin & lizaka. 1982 25264)

Phanerochaete chrysosporium and wood chips biopulping Wall et al.. 1993 Ceriporiopsis subvermispora

Unidentified soil organism kudzu vines bioretting Tanner et al.. 1993

Calletotrichm truncatum perlite + com meal + biofungicide Silman et al.. 1993 liquid medium

Aspergillus awamori NRRL 4869 wheat bran ot-galactosidase & Silman. 1980 invertase

Beauveria bassiana clay granules + liquid bioinsecticide Desgranges et al.. 1993 medium

Table 2.2. Examples of solid state fermentations found in the literature. (continued)

14 Table 2.2. continued.

Organism Substrate Product Reference

Candida utilis wheat bran, sugar cane biomass for Christen et al., 1993 bagasse, or Amberlite® biotransformations nesin

Aspergillus spp.Rhizopus spp. alfalfa celluloses & pectinases Bajracharya & Mudgett. for enhanced protein 1979 recovery

Rhizopus oligosporusNRRL 2710 grain pure culture tempeh Wang et al.. 1975 inoculum (spores)

Saccharomyces cerev’wiatfNRRL Y- sweet sorghum ethanol Kargi et al., 1985 11572

Mucor miehei wheat bran fungal rennet Thakuretal.. 1990

Bacillus subtilis wheat bran amylases Beckford et al.. 1945

Aspergillus spp. wheat bran amylases Hao et al.. 1943

Aspergillus oryzae wheat bran amylases Underkofler et al.. 1939

A. oryzae wheat bran amylases Takamine. 1914

Streptomyces sp. X-119-6 wheat bran L-glutamate oxidase Kusakabe et al.. 1983

A. oryzae rice amylase Narahaia et al.. 1982

Rhizopus sp. and Citrobacterfreundii soybean tempeh Bisping et al.. 1993

Acremonium strictum T1 wheat bran oligosaccharide oxidase Lin et al.. 1993

A. niger sugar cane bagasse + ciuic acid Lakshminarayana et al.. liquid medium 1975

Thermomanospora strain 29 coffee processing waste thermostable Srivastava. 1993 hemicellulase

Thermophilic fungi sugar beet pulp protein Grajek, 1988

Chaelomium cellulolyticum alkali-pretreated protein Pomment et al.. 1978 sawdust

Trichoderma viride wheat bran cellulase Toyama & Ogawa, 1975

Phanerochaete chrysosporium Buids birch wood biopulping Mudgett & Paradis. 1985 ME446

Rhizopus oligosporusot Monascus carob pods high-ptotein feed Kokke. 1977 ruber

Aspergillus ellipticus and Aspergillus lignocellulosic waste cellulase and Gupte & Madamwar, fumigatus glucosidase 1997

Trichodemut reesei or T. wheat straw SCP Laukevics et al.. 1984 reesei/Endomycopsis fibuliger coculture

15 Both labor-intensive traditional methods and highly automated industrial methods

are used to produce koji in Japan. In the indigenous, manual method, steamed rice is

mixed by hand with the tane-koji, or spore starter culture, then placed in stacked shallow

trays. Occasionally the rice is stirred manually to control temperature. The industrial

methods employ large koji-making machines which stir the koji automatically and control temperature and humidity by computer. Pregerminated spores may be used. These approaches reduce labor, fermentation time, and costs while increasing hygiene, but may result in a loss of characteristic flavor and aroma of traditional koji, important in food fermentations (Ebine, 1989).

Fermented foods and beverages from koji

Though today fermented foods are often valued for the variety they add to the diet, the traditional role has been that of preservation, especially of proteinaceous foods otherwise subject to rapid putrefaction (Yokotsuka, 1985). Filamentous fungi grown in solid state fermentation have played a considerable part in this effort. Molded cereals, legumes, or other plant materials, known as koji in Japanese or chu, shui, or qu in Chinese, are often used as finished foodstuffs or as the starter for other foods, including soy sauce, various alcoholic beverages, and miso. This technology is recorded as early as 3000 years ago in the Chou dynasty. While "koji mold" has a particular meaning in Japanese use, referring specifically to Aspergilli molds (Yokotsuka, 1985), the more general use of molded plant material will be used throughout this dissertation.

In the west, soy sauce is the best known and most widely consumed oriental food prepared by fermentation. Its preparation is based on an initial koji fermentation of

1 6 soybeans, wheat, and either A. oryzae or Aspergillus sojae, followed by SmF of the koji

with added brine by yeasts and lactic acid bacteria. Records show large scale production

of soy sauce in Japan in the 1600’s, making it a pioneer in industrial fermentations

(Fukushima, 1989). There has been a radical change in soy sauce production in Japan

since the 1950’s when scientific investigations of the process were begun. Production

improvement include improved mold strains, mechanization, automation, and application

of continuous production to the process.

Miso, used in soups and as a condiment, is produced by combining koji (usually

A. oryzae and rice) with cooked, ground soybeans followed by further fermentations

(Ebine, 1989). Sake, or rice wine, is prepared from rice koji mixed with sake yeasts and

additional rice and water (Yoshizawa and Ishikawa, 1989).

Various alcoholic beverages start with a koji fermentation, which functions in much

the same way that malting does in Western beer or wine making. The amylolytic enzymes

produced by the growing mold saccharify the starch substrate, providing sugars upon which

alcohol-producing yeasts can grow. Some of the fermentations use fungi that produce

alcohol as well as enzymes, and no yeasts are needed. Members of the genova Aspergillus,

Rhizopus, and Mucor have traditionally been used for this purpose (Yokotsuka, 1985).

Of equally great importance are fermentations that yield stable and sometimes more

easily digestible or more appealing protein foods, such as soy sauce, miso (fermented

soybean paste), or tempeh (fermented soybean cake) (Yokotsuka, 1991. Many other oriental foods begin with a soybean koji fermentation using Aspergillus oryzae, A. sojae, or

Mucor molds, including products such as natto (fermented, sticky, spiced soybeans, Japan),

17 douchi (fermented, salted, and powdered soybeans, Korea, China), and sufu (fermented

tofu, China) (Yokotsuka, 1991). Various other koji fermented protein foods have been

reviewed (Yokotsuka, 1991).

Reactor types

Traditional SSF reactors consist of nothing more than simple wooden trays, usually

with perforated bottoms for gas exchange. These trays, still in use for many SSF processes

for Asian food production, are filled with inoculated substrate and stacked in staggered

piles, which facilitate aeration, in a temperature and humidity controlled room, where the

fermentation progressed over the course of three to four days (Ebine, 1989; Fukushima,

1989; Yoshizawa and Ishikawa, 1989). Temperature in the koji rises rapidly at first,

requiring cooling by stirring by hand at about 5 to 20 hours after inoculation. A second

stirring about five hours later provides additional temperature control. Without stirring, or

with late stirring, high temperatures could lead to either reduced production or death of the

mold (Fukushima, D., 1989; Yoshizawa and Ishikawa, 1989). The entire process, though effective, is cumbersome and requires much hand labor.

Currently used equipment improves over the traditional techniques by using large, perforated, shallow vats in closed chambers, forced air with temperature and humidity control, and mechanical turning devices (Sakaguchi et al., 1992). Improved temperature control leads to improved koji, with 35°C being preferred for koji growth at first and 38°C better for amylase production in the end of the fermentation. Koji-making machines can produce this temperature change to some degree (Yokotsuko, 1991). Protease activities have been found to increase for koji produced in such machines and undesirable, flavor-

18 spoiling contaminants have been reduced (Fukushima, 1989). Soy sauce yield has been

increased by 65 to 95% when such machines are used to produce the koji (Lotong, 1985).

Batch and continuous machines are available.

Another sort of koji-making machine used in miso manufacture uses augers to

transfer the rice from one rotating circular bed to another and to remove the rice from the

reactor; during the transfer the rice is stirred. Conditioned air (temperature and humidity) is

passed over and through the rotary beds (Ebine, 1989). Culturing time can be reduced by 6

- 8 hours using koji-making machines.

Koji-making rooms, in which the floor is of perforated stainless steel, allowing

forced air to pass through the bed of substrate heaped on the floor, are also used (Yoshizawa

and Ishikawa, 1989). Kikkoman Corporation has built an enormous, circular koji-making

facility that can operate continuously, with an hourly capacity of 4.150 tons. It looks like a

large building (Fukushima, 1989). The existence of koji-making machines does not imply

that all industrial koji manufacture has been modernized. Many still use the traditional

methods of either koji beds or koji trays with hand stirring to control temperature and to

expell carbon dioxide (Yoshizawa and Ishikawa, 1989). There is not currently a consensus as to the extent to which automation of koji-making has been embraced by the industry

(Lambert, 1983).

Industrial processes

The lack of scale-up data for SSF processes is one reason that there is a lack of commercial exploitation of SSF advantages in the US. Few studies on scale up are available. Silman et al. (1979) discuss the successful scaling-up of aflatoxin production

19 from 4 inch diameter columns to 18 ft diameter bin reactors. Saucedo-Casteneda et al.

(1992) demonstrated that maintenance of heat and water balances could be used as

successful scale-up criterion for the production of ethanol by Schwanniomyces castellii in

SSF on sugar cane bagasse plus basal liquid medium. Conditions were found to be

conducive to higher productivities at larger scales than at smaller scales, demonstrating

that laboratory successes in SSF may well be repeatable at industrial scale. The same

group suggests in other work that the Biot and Peclet numbers be used as scale-up criteria

to maintain expected heat transfer conditions (Saucedo-Casteneda et al. 1990). Durand et al. (1993) discuss the pilot plant used to scale up several processes for animal feed and enzyme biosynthesis.

Despite our lack of fundamental understanding of SSF systems, there are several examples of industrial processes using SSF. Kikkoman operates a soy sauce plant in

Wisconsin, as well as many in Japan in which koji are grown in large rooms with automated stirring. Other food fermentations common in Japan which use koji fermentations include the production of miso, sake, and tempeh.

LYVEN, a subdivision of GENERALE SUCRIERE (Cagny, France) has produced pectinases since 1989 via SSF (Durand et al., 1993). CALLIOPE (Noguères,

France) was scheduled to open a biopesticide plant in 1993, producing Beawvena bassiana (Durand et al., 1993).

Numerous composting plants exist throughout the US. Though these are essentially SSF fermentations, the parameters of interest are not product formation, but rather substrate stabilization to further degradation. Composting studies should provide

20 insight into mass and heat transfer in SSF, but other approaches are necessary to

understand product formation kinetics and control of cell physiology.

Properties of SSF

Advantages

Numerous advantages have already been demonstrated for SSF over SmF in certain fermentations (Mudgett, 1986; Ramana Murthy, et al., 1993; Hesseltine, 1972;

Moo-Young, et al., 1983; Ramesh and Lonsane, 1990). Aeration is easily achieved; SSF typically has high interfacial area-to-liquid volumes compared to sparged SmF. Compared to SmF with agitation, SSF generally has lower energy requirements (Aidoo, 1982).

Foaming, often a problem in Smf, is eliminated in SSF (Pamment et al., 1978).

Low moisture content in SSF leads to several advantages, including lower reactor volume required for a given productivity than for SmF, lower costs for purification because of a higher concentration of product compared to SmF where the large liquid volume dilutes the product, and lower costs for treatment of liquid effluent than for SmF.

Any precursor molecules required are not diluted by culture broth, resulting in requirement of smaller amounts and/or higher yields based on precursor amount (Bauer,

1986). The solid residues may be useful as nutritionally improved plant biomass for animal feeding. Furthermore, contaminants are inhibited at low moisture contents.

The media used in SSF are simple though heterogeneous, often being no more than unprocessed grain with minimal mineral supplementation or no supplementation at all. Agri-industry waste streams such as com fiber have potential use in SSF. Even with such heterogeneous media, yields are reliable and reproducible (Hesseltine, 1972).

21 Substrate availability may increase during fermentation (or may decrease or remain

constant, as well) rather than always decreasing as it does in SmF (Knapp and Howell,

1980).

The most compelling advantage of SSF is that it is a more natural state for the

organisms being grown, which may have physiological benefits. For instance, some

enzymes are produced by given organisms only in SSF, not SmF (Lin, et al., 1993). In

addition to the production of certain products only under SSF, not SmF, conditions, many

instances have been demonstrated where a product which may be produced under SmF

conditions is produced in greater quantity in SSF. Some of the earliest modem Western

studies of SSF showed that aflatoxin production on rice was much higher in SSF than in

SmF (Hesseltine, 1972). Glutaminase activity was found to be higher from SSF rather

than from SmF by Aspergillus sp. Amylase and protease production by Eupenicillium javanicum increased in a SSF fluidized bed reactor (FBR) compared to SmF (Tanaka,

1986) and citric acid production in SSF was increased over that in SmF (Aidoo et al.,

1982). The reasons for the increase in SSF productivity over that of SmF are unclear.

Evans and Wang (1984) hypothesize that the provision of a 3-dimensional support may affect cell physiology, based on their work comparing cells in SmF to cells in alginate beads as a simulated SSF.

It has been proposed that enzyme productivity may also be increased because the localized drop in substrate concentration as the cultivation proceeds leads to rapid use of sugars enzymatically liberated from the solid substrate as they are solubilized, preventing catabolite repression types of inhibition from occurring (Ramesh and Lonsane, 1991).

22 They showed that though 5aa7to licheniformis M27 grown on 1.0% soluble starch in

SmF produced less than 10% of the enzyme it produced when grown on 0.2% soluble

starch, probably because of enzyme end-products inhibiting the formation of the enzyme,

when grown in SSF the enzyme production increased nearly 29 times when the

concentration of soluble starch saturating the SSF wheat bran medium was increased 42

times. They also propose that the intimate contact between cells and media and lack of shear may have increased productivity.

Figments are one type of product that is often produced more successfully in SSF than in SmF. As the trend toward increasing demand for natural products in foodstuffs continues, greater and greater demand for naturally-derived, including fermentatively produced, pigments will result. Currently, the use of fermentatively produced pigments is limited in the food market because of the higher cost of pigment production by fermentation; improved organisms, as well as improved fermentation processes, including the development of better SSF systems, should benefit the pigment industry. More pigments were produced when Monascus spp. were grown in SSF than in SmF (Lin and lizuka).

Among the pigments of interest to the food industry, those produced by organisms of the genus, Monascus, have one of the longest histories of food use. These red, yellow, and purple pigments have been produced in SSF in the Orient for hundreds of years

(Jacobson and Wasileski, 1994). These valuable, heat- and pH-stable pigments have been shown to be of higher quality when produced by SSF rather than by SmF. Though the tray method of SSF currently used is expensive, submerged cultures produce only 10% of the

23 pigment produced by the former (Evans and Wang, 1984); other studies confirm the

productivity advantage of SSF (Lin and lizaka, 1982). In a comparison of SmF and SSF,

the latter as surface agar culture and as roller bottle culture, found the best production in the

latter reactor, with roughly a 10-fold increase in production over the other methods.

Possible reasons suggested for this increase were the availability of solid support and better

gas exchange, but it was noted in the report that scale-up of a roller bottle reactor is difficult

(Maketal., 1990).

In the production of citric acid by Aspergillus niger in submerged culture, the

formation of pellets is critical to a successful process (Crueger and Crueger, 1982; Martin

and Demain, 1978). Perhaps the degree of heterogeneity provided by pellet formation in

contrast to loose, finely divided, filamentous submerged morphology is great enough to

allow for a differentiation or metabolic cue that triggers citric acid formation. The

heterogeneity of SSF automatically provides such cues in solid state fungal culture.

Another possibility for why SSF improves productivity over SmF is offered by

Hong et al. (1989) who suggest that the greater yeast growth observed in SSF may be

caused by the adsorption of secreted yeast mating pheromone (a-factor) onto the solid

substrate, making it unavailable to arrest budding.

Disadvantages

There are several disadvantages to SSF, chief among them being heat transfer problems. Aeration rates affect heat transfer rates, and must be considered jointly

(Matsuno, et al., 1993). Temperatures in tray fermentations have been controlled by various methods on a laboratory scale, including water baths, jackets, and sprinkling the

24 outer shell of a cylindrical reactor, but all of these techniques are impractical (Ramana

Murthy et al. 1993) and would have scale up problems. Packed bed heat control by

pumping air through the bed is better, but the aeration which controls the temperature

also affects moisture content (Ramana Murthy, et ai., 1993).

Heterogeneous microenvironments in the SSF substrate make it difficult to

measure process variables such as pH, oxygen concentration, biomass content,

temperature, and product yields (Moo-Young, et al., 1983). Because of this, few studies

exist on the kinetics of product formation in SSF reactors (Ramana Murthy et al., 1993).

Little design information is available for SSF plants, and much of that available is in

Japanese (Lambert, 1983).

Oxygen transfer may be a problem in SSF reactors without agitation or forced

convection (Matsuno, et al., 1993), however, it has shown that oxygen transfer is

higher in SSF than in SmF (Mudgett, 1980). There is no information relating product

formation kinetics and mass transport phenomena (Ramana Murthy, et al., 1993).

Furthermore, few models of any sort exist for SSF. Such models would be invaluable for

reactor design.

Additional disadvantages are that the size of the spore inoculum required may be large (though no studies are available to confirm this) and that pretreatment of the substrate may be necessary (Hesseltine, 1972). Furthermore, SSF fermentations are limited to organisms tolerant to low moisture levels and which are able to use the polymeric substrates. Genetic engineering should be able to address these problems.

25 Important Physical Parameters in SSF

Water activity

Though free-flowing water is not present in SSF, the amount of water available to

support microbial growth is still important. In fact, for SmF, in contrast to SSF, water

availability is not an important parameter, because water is, of course, always available.

In SSF, the moisture content varies during the cultivation and has a large effect on

microbial growth and product formation kinetics.

Water activity, a measure of the amount of water available for microbial

metabolism, is defined as the atmospheric relative humidity in equilibrium with the

substrate. It is related to the moisture content of the substrate and can be defined

mathematically as follows:

— nM(f) a„, = P, 555 where n = number of ions formed, M = molar concentration of solute, (() = molar osmotic coefficient, 55.5 is the molar concentration of an aqueous solution of pure water (Pandey,

1992), and Ps is the equilibrium vapor pressure of the substrate, and Po is the equilibrium vapor pressure of pure water at the same temperature as the substrate (Ramana Murthy et al., 1993). As the solute concentration increases, a^ decreases. Pure water has an aw of

1.00. Water activity is inversely proportional to temperature, probably because of increased solute solubility with in^'reased temperature. At thermodynamic equilibrium, water activity can be defined as follows:

26 ERH , a». = = 100 m^. +m, where ERH is the equilibrium relative humidity, is the activity coefficient, a’ is the molar fraction of the solute, m« is the moles of water in solution, and m; is the moles of solute in solution.

The water activity of a mixed substrate is determined by the product of the water activities of the component substrates (Ross, 1975, cited in Ramana Murthy et al., 1993), as follows:

a* = (a.,)(a.2)(a.3)"(awm)

Water activity provides a measure of the water potential, or energy state of a substrate. Both matric (capillary forces) and osmotic (caused by solutes) types of water potential contribute to the overall potential. Water activity is importai.t in regulating metabolism and so the control of it is important to favor the production of desired metabolites (Grajek and Gervais, 1987). It has been shown (Lindenfelser and Ciegler,

1975) that the initial water activity is of especial importance, providing for both microbial growth and swelling of the substrate to allow better use of it by the organism.

Ortiz-Vazquez et al. (1993) found that an initial moisture content of 61.5% was best for production of lipolytic enzymes by Pénicillium candidum grown on wheat bran plus salts. At a moisture content of 75% there was no free water visible, but enzyme activity was reduced. This may be because pores within the bran were filled with water, limiting oxygen transport. This study further noted that by saturating the ambient air with

27 moisture, no dried zones occurred in the medium and enzyme productivity started sooner

and produced more than for cultivations without saturated ambient air.

Protease production decreased in a cultivation of Aspergillus oryzae on rice when

moisture level increased above 30%, but a-amylase production in the same cultivation

increased with increase in moisture to 35% (Narahara et al., 1984). Water content in the

medium was successfully controlled at 35% by changing the ambient temperature in

accordance with an equation based on material and energy balances, resulting in increased

a-amylase production.

Nigam (1990) found that moisture content affected substrate consumption by the

white rot, Polyporus, grown on sugar cane bagasse, perhaps by improving diffusion of

enzymes into the substrate at higher moisture contents. Biomass and protein production,

however, had a slightly lower moisture optimum, possibly because at higher moisture

the medium compacted somewhat, possibly resulting in steric hindrance or oxygen

transport limitations.

Because the reduced potential contamination by bacteria at the low moisture

contents of typical SSF processes has been widely cited as an advantage of SSF

(Hesseltine, 1972; Ramana Murthy et al., 1993), it may be supposed that bacterial

systems are not suited to SSF or at the least may require higher moisture content.

However, in a cultivation of Bacillus licheniformis on wheat bran medium it has been

shown that cultivation at high moisture content (95%) without agitation reduced growth and enzyme formation, probably because of reduced oxygen transfer (Ramesh and

Lonsane, 1990). The greatest enzyme activity was produced in cultivation with medium

2 8 of only 65% moisture content. Equivalent enzyme productivity could be obtained at 95%

moisture content only if agitation was supplied, thereby reducing oxygen transfer

limitations. It was noted that intermediate water contents (75% and 85%) resulted in

higher enzyme titers at 24 hours than did the 65% medium, probably because cell growth

was indeed faster at these somewhat higher moisture contents. Though not suggested by

the authors of this study, this pattern suggests that starting a cultivation with relatively

moister medium, then allowing it to dry during the first 24-48 hours by passing dry air

through the medium until it reached a more desirable level for enzyme formation, may be

a desirable operating strategy.

Bacteria generally require relatively high values of a*,, while fungi and yeasts may

grow and survive at values as low as 0.6 to 0.7 (Pandey, 1992) though this also depends

on temperature (Silverman et al., 1983). Small fluctuations in the water activity of the

substrate cause large variations in microbial metabolism. High water activities appear to

favor sporulation, while lower activities appear to encourage spore germination and mycelial growth (Ballio et al., 1964; Molard et al., 1985, both cited in Pandey, 1992).

Water activity can be controlled during the cultivation by removing moisture and heat via air cooling of the substrate.

Water activity also affects cell death rates. Sato et al. (1983) found that the detrimental effect of decreasing moisture content because of evaporation was greater in yeasts, which require relatively high water activity, than in molds. The following model relating cell death kinetics to a^ has been proposed (Pandey, 1992):

29 f k = A:„a„.exp RT

where the constants k„ and Ea are determined experimentally, R is the gas constant, and T

is the absolute temperature.

Substrate properties

Surface area. For SSF, the rate of biomass growth and product formation tend to be

proportional to available surface area rather than weight or concentration. However, not

all of the surface is always truly available, as in cellulose hydrolysis in which crystallinity

and lignin content affect the susceptibility of the substrate to hydrolysis.

Biomass yield is generally lower in SSF than in SmF, though volumetric

productivity is higher. This may caused by limited surface area and volume for cell

growth. Calculations based on fungal mycelia indicate that the maximum biomass

concentration to be expected in SSF in a packed bed or tray reactor is only 10-30 g/L

(Laukevics et al., 1985). These calculations do not, however, account for increases in

available volume as substrate is consumed.

Particle size and shape. As particle size decreases, specific growth rate is observed to

increase (Mitchell et al., 1988). This may be related to increase in available area, though

this has been shown to not necessarily be the case (Humphrey 1977). In a study using

Avicel, a regenerated cellulose which is highly porous and so has little surface area dependence on particle size, Humphrey (1977) found specific growth rate to still increase with decreased particle size. Instead, they propose that mass transfer limitations are decreased at the smaller particle size. It has been suggested that though small particles

30 may decrease mass transport limitations within the particle, improving growth, larger

particles may be desirable if secondary metabolite formation is desired. It has been

suggested that the supposed diffusion limitations in fie larger particles may trigger the

idiophase and sustain it for long term secondary metabolite production, though this was

not shown in accompanying experimental work, possibly because the point of diffusion

limitation had not been reached (Barrios-Gonzalez et al., 1993). It was found that

increased packing density did increase penicillin production hy Pénicillium chrysogenum

on sugar cane bagasse, possibly because of steric hindrance to conidia formation (Barrios-

Gonzalez et al., 1993) or from diffusion limitation induced idiophase. If packing density

is increased by mashing the substrate so that available surface area is dramatically

decreased, as demonstrated by comparing chipped cassava to mashed cassava for the

growth of Rhizopits oligosporus, growth decreases significantly (Mitchell et al., 1988).

Particle size changes during cultivation because of biofilm growth on the surface which initially increases the particle size, which is then followed by particle shrinkage caused by substrate consumption (Gumbira-Sa'id et al., 1993).

Grain variety. When a grain is used as a substrate, one must recognize the variation between particular varieties. For koji making, for instance, one particular variety of rice has been found to have an interior cavity which provides additional surface area for mycelial growth (Ebine, 1989).

Gas concentrations

It is now understood that aeration is required both to provide oxygen to the cultivation and to remove excess carbon dioxide formed during cellular metabolism. The

31 levels of both oxygen and carbon dioxide strongly affect metabolism, providing

opportunities to control cellular metabolism to produce desired primary and secondary

metabolite products (Mudgett, 1980; Bajracharya and Mudgett, 1980). Above a low

critical level, oxygen concentration does not affect growth except that at high

concentrations growth may be inhibited. Growth inhibition has also been observed at low

carbon dioxide levels, possibly because of limited carbon dioxide available for

biosynthetic pathways, but high carbon dioxide levels may inhibit both biomass and

product formation.

Production of cellulase and pectinase was shown to increase with increases in

oxygen partial pressure from 0.21 to 0.42 atm (Mudgett and Bajracharya, 1980). As

carbon dioxide concentration was increased from 0.005 to 0.05 atm, cellulase production

rose and pectinase production rose at first but then stayed constant, indicating different

enzyme synthesis regulation mechanisms. Acid protease production was seen to have a

maximum with respect to carbon dioxide concentration in SSF of Aspergillus niger on

wheat bran, with enzyme activity at 4% carbon dioxide being almost 50% greater than

that at 0% or 8% carbon dioxide (Villegas et al., 1993). Carbon dioxide evolution was

directly proportional to enzyme activity and also varied with controlled carbon dioxide

concentration, indicating that the carbon dioxide concentration was affecting cell

metabolism.

Production of a-amylase was also shown to be stimulated with increasing oxygen concentration and was much greater than that seen in SmF, suggesting that either the production of this enzyme had stringent oxygen requirements or the oxygen penetration

32 zone was deeper, allowing for further growth and enzyme production (Bajracharya and

Mudgett, 1980). The respiration quotient (RQ, the moles of carbon dioxide produced per

mole oxygen consumed) was also observed to increase with decreasing oxygen

concentrations, suggesting a shift to anaerobic metabolism. On the other hand, RQ

increased as partial oxygen pressure approached 1 atm, perhaps as the result of additional

energy requirements to overcome oxygen toxicity. Increases in carbon dioxide

concentration above 0.01 atm at a constant high oxygen concentration decreased enzyme

productivity, while a maximum biomass production was seen at 0.02 atm carbon dioxide.

This suggests that carbon dioxide concentration can control metabolism, shifting it between enzyme production and biomass growth. This effect is organism dependent; for some species, high carbon dioxide concentrations may induce increased enzyme production while for others it may inhibit enzyme production (Mudgett, 1980). It would be interesting to investigate the effect of oxygen and carbon dioxide concentrations on sporulation (Bajracharya and Mudgett, 1980). Work done with mycelial pellets from

SmF suggests that Aspergillus niger mycelia reversibly adapts to low oxygen levels within hours (Kobayashi, et al. 1973).

Temperature

Temperature affects productivity of both biomass and products, as it does for

SmF, as was noted in the discussion of heat transfer. It has been shown that immobilization of cells in SmF as a biofilm may provide some resistance to temperature sensitivity (Silva, 1993). It has also been shown that high temperatures suspended but did not permanently halt the production of aflatoxin; when the temperature was reduced,

33 aflatoxin production resumed (Silman et al., 1979). Further investigations of this effect

in SSF may prove of interest.

Ortiz-Vazquez et al. (1993) demonstrated a strong effect of temperature on growth

and lipolytic enzyme production by Pénicillium candidum in SSF, finding that growth

and enzyme production were best at 29“C, growth was evident but enzyme production did

not occur at 4°C, and no growth occurred at 40°C. The experimental temperatures were

too widely separated to come to any conclusion about where the maximum productivity

may occur, but it is obvious that temperature affects enzyme production and biomass

growth somewhat independently and the optimum for one may not equal the optimum for

the other.

Nigam (1990) showed that increased temperatures could induce premature

sporulation of white rot fungi grown on sugar cane bagasse.

eh

Ortiz-Vazquez et al. (1993) demonstrated that for the production of lipolytic

enzymes by Pénicillium candidum grown on wheat bran plus salts, the best pH for

enzyme activity was 7.0, with the range tested being 6.0 to 7.0. They noted a rise in pH

during the fermentation, probably because of deamination of amino acids in the wheat bran. Nigam (1990) also demonstrated that pH strongly influences biomass production and enzyme productivity in SSF.

Inoculum size and preparation

When inoculum is in the form of a finely separated units, such as individual spores or finely divided hyphae, rather than in pellets as would result from a submerged

34 shake flask preparation, the result is a more even dispersion and better growth (Nigam,

1990). Relatively large inocula grown out on similar substrate as for the cultivation result in more initial biomass as well as more enzyme for greater immediate substrate availability (Nigam, 1990).

Transport properties in SSF

Little is known about transport properties in SSF. The reactor type certainly affects transport phenomena in SSF, and several studies have begun to clarify the situation in each (Ramana Murthy, et al., 1993). Still, few data on transport properties for various SSF substrates exist, or on the characteristics of the various reactor types. Moo-

Young and Chisti (1988) suggest the use of "cold" reactors containing killed fermentation broth in the investigation of these parameters in submerged fermentation. A similar approach may be useful for SSF.

Effect of reactor tvpe

Tray reactors, based on the traditional koji fermentation, are the simplest SSF reactors. Typically, the solid substrate is placed in stacked shallow trays in a controlled environment room. The top surface is exposed to the ambient air. The bottom surface may be perforated, there is no forced air circulation through the trays. Oxygen transfer is by diffusion only (Ramana Murthy, et al., 1993). Critical factors for transport are the media porosity and the gap between the trays. Heat transfer is by conduction, evaporation

(as the latent heat of vaporization), and by natural convection from surfaces.

Rathbun and Shuler (1983) have experimentally studied temperature and gas concentration gradients in tray fermentors. They deliberately established a temperature

35 gradient in a tray containing various depths of medium, then measured temperature and concentration as cultivation proceeded over time. The vertical temperature gradient was larger than the lateral gradient as a result of the heating. When thin (0.5 cm) beds were compared to relatively thick beds (-3.2 cm), little or no transport resistance was found for the shallow beds, allowing intrinsic growth parameters to be determined. Thicker bed had noticeable transport limitation effects, developing considerable gradients in both temperature and oxygen concentration. It was further noted that as the upper layers of thick beds shrank and cracked as cultivation progressed, growth increased at the bottom of the bed with the increase in oxygen transport through the cracks.

Finger et al. (1976) investigated temperature and concentration gradients in compost piles (heaped beds). They found significant temperature gradients, with the temperature rising rapidly just under the surface of the bed and reaching a maximum at the bed center.

Packed bed reactors allow forced convective mass transfer as gas (typically air) is pumped through the bed (Saucedo-Castanedo et al., 1990). Smaller gradients than those found in trays are found in packed bed reactors, but reduction in bed porosity with time can be a problem (Ramana Murthy et al., 1993). Axial temperature and gas concentration gradients have been documented in packed columns (Gowthaman et al., 1993), though they are much smaller than those observed in tray fermenters (Ghildyal et al., 1992).

Increased air flow increased enzyme activity in the cited study (Gowthaman et al., 1993), but enzyme activity still decreased with bed height at the maximum air flow used, possibly because of evaporative moisture loss. Similar cultivations performed in tray

36 reactors for comparison purposes had the lowest enzyme activity recorded. Moisture loss during aeration may have been a problem. Similar results were seen in the production of aflatoxin on com; increases in air flow increased aflatoxin production, but moisture below 20% caused reduction in aflatoxin production (Silman, et al., 1979).

Saucedo-Casteneda et al. (1990) found radial temperature gradients in a packed bed reactor equipped with a cooling jacket. The radial gradients were probably more marked than the axial gradients because of the heat transfer supplied by the water flowing through the jacket.

Rotating drum reactors also reduce transport gradients by tumbling the solid substrate within a baffled drum at a rate of about 1 - 2 rpm, thereby reducing the heterogeneity within the mass by mixing. Slowly rotating drums have been shown to produce rapid growth of microorganisms (Underkofler et al, 1939; Takamine, 1914), thought mycelial damage may be a problem in some cultures (Lonsane et al., 1985;

Saucedo-Castenedo et al., 1990; Ramana Murthy, et al., 1993). They may be difficult to scale up (Mak et al., 1990).

Ryo et al. (1991, cited in Ramana Murthy et al., 1993) describe a rocking drum reactor in which the drum is rocked without disturbing the substrate. It provided a uniform distribution of air and moisture, preventing channeling and drying and therefore achieving higher biomass productivities than other SSF reactors.

Mass transport considerations

In studies of mass transport in SmF, oxygen concentration is considered to be uniform in the bulk gas (Ramana Murthy, et al., 1993). A concentration gradient exists in

37 the thin films at bubble interface and solid/liquid interface. As the fermentations

proceeds, increased viscosity may decrease oxygen transport through the films to the

microbes. The mass transfer coefficient increases with agitation and aeration of the broth.

SSF, however, is a much more heterogeneous system. Oxygen transfer is through

a liquid film on the substrate surface or may be directly from the air to the microbe

(Mudgett, 1980). No bulk mixing is possible, therefore interfacial area available for transfer and the partial pressure of oxygen determine the effectiveness of oxygen transfer.

The process is diffusion controlled. It is also growth controlled; as oxygen is taken up by microbes, gradients result. This occurs along the penetration depth. The penetration depth is the zone of active metabolism within a substrate particle. Oxygen concentration may decrease to zero along it (Ramana Murthy et al., 1993). The penetration depth can be increased by increasing oxygen transfer, for instance, by increasing the partial pressure of oxygen in the aeration gas (Mudgett and Bajracharya, 1979). The penetration depth has been observed to be up to 100 pm for oxygen (Mudgett, 1980). It is also possible to get oxygen transfer directly by gas/microbe contact, which is the ideal situation for microbial growth (Ramana Murthy et al., 1993).

Oxygen transfer rates have been observed to be greater in SSF than in SmF

(Mudgett and Bajracharya, 1980; Mudgett, 1980). This is likely because oxygen transfers directly from the bulk gas to the thin film surrounding the microorganism, rather than from air to air/liquid interface to bulk fluid to fluid/organism interface, as in SmF.

Estimates of the overall mass transfer coefficient, Ki,a, are higher for packed beds with forced aeration than for SmF (Gowthaman et al., 1993). The interfacial area for oxygen

38 transfer is believed to be much greater in SSF than in SmF (Ramana Murthy, et al., 1993;

Mudgett, 1980). Even without agitation, no mass transfer limitations were observed for

Bacillus licheniformis growing on wheat bran at 65% moisture, as was demonstrated by

identical growth curves for cultivations with the ratio of the volume of the culture flask

relative to the volume of the medium varying from 1.92 to 16.0 (Ramesh and Lonsane,

1990).

Interparticle vs. intraparticle. Oxygen transport in SSF may occurs as interparticle mass

transfer and as intraparticle mass transfer (Ramana Murthy et al., 1993; Moo-Young et

al., 1983). In interparticle mass transfer, the oxygen diffuses from the void space (air

space) directly to the microorganisms. Mixing of the substrate and forced aeration

increase transfer rates by increasing the oxygen concentration in the void space, though

these factors are not as critical at high void fractions. It is thought that mixing may keep

substrate particles separate, thereby decreasing the diffusion length necessary and

increasing surface areas (Hesseltine, 1972; Mudgett, 1980). It may, however, damage

cells (Takamine, 1914). Without mixing, gradients are observed in packed beds and compost piles (Ramana Murthy et al., 1993). Steep oxygen gradients observed in tray

fermenters (Rathbun and Shuler, 1983) and can be correlated with enzyme activity gradients, thus limiting the useful depth of medium in these reactors (Ghildyal et al.,

1992). Carbon dioxide gradients can also be steep in deep tray fermenters, with carbon dioxide concentrations reaching as high as 19% at a depth of 240 mm. Ghildyal et al.

(1992) propose a critical bed depth, the depth at which gains in enzyme production made

39 by deepening the bed and providing additional substrate are balanced by decreases in

overall enzyme yield caused by mass transfer limitations.

Intraparticle transfer is the transport of nutrients and enzymes within the substrate

itself (Ramana Murthy, et al., 1993). The situation is complex. Oxygen transport into

biomass-containing substrate affects the growth of the biomass and the production of

enzymes which degrade the substrate, which in turn affects oxygen diffusion within the

substrate. In mycelial pellets grown in SmF, oxygen uptake rate has been shown to

depend on both the diffusion of oxygen and the adaptation of the mycelia to reduced

oxygen concentrations within the pellet (Kobayashi et al., 1973). A similar situation

could exist for SSF.

One approach to dealing with intraparticle oxygen transfer is to apply an

effectiveness factor (Ef, the observed rate relative to that possible if no substrate

concentration gradients existed) and Thiele modulus (

consumed to the rate substrate is supplied) as used for heterogeneous catalysis

(Satterfield, 1970; Weisz and Prater, 1954; Bischoff, 1967). This has not been done for

SSF, but investigations into the application of these criteria to SSF would be valuable

(Ramana Murthy, et al., 1993). In SmF, it has been observed that the specific respiration

decreased with increasing mycelial pellet size (Kobayashi et al., 1973). A similar

investigation for SSF would help clarify the degree of mass transfer limitation imposed

by intraparticle diffusion.

If mycelial pellets have a sufficiently large radius, mass transfer resistance will alter the shape of a Lineweaver-Burk plot for Michaelis-Menten kinetics (Ngian et al.,

40 1977). The same is undoubtedly true for SSF and should be considered in studies to

determine reaction rate constants. Bischoff has proposed a general criterion for avoiding

mass transfer limitations which uses only easily observable quantities, providing the

general form of the rate equation is known (Bischoff, 1967). Application of such a

criterion to SSF would provide valuable design information.

Oxygen diffusion. Though modeling of oxygen diffusion within a substrate particle has

been infrequent, many studies of oxygen diffusion within mold pellets in SmF have been

performed. These studies and the resulting models help in understanding the SSF process, but are insufficient for applied design work (Ramana Murthy et al., 1993; Moo-

Young et al., 1983). It has been shown that diffusive processes limit the rate of gro\/th, especially within the substrate (Mitchell, et al., 1990). Oxygen must diffuse through biomass which consumes it before it can reach the inner substrate. This may be one source of difficulty in achieving complete consumption of the substrate. Other information is sparse (Ramana Murthy, et al., 1993). The experimental oxygen mass transfer coefficient, Kta, is difficult to measure because of the complexity of the three phase system, though a new procedure based on measuring the response of the system to a step input change in carbon dioxide concentration has been suggested (André et al.,

1981). With corrections for the difference in diffusivity of oxygen and carbon dioxide, this method has shown good agreement with conventional transient dissolved oxygen technique.

Enzyme and substrate diffusion. Enzyme diffusion is even more complex. Extracellular enzymes must degrade insoluble substrate into soluble ones before the organism can use

41 them. It has been suggested that this is the rate controlling step (Ramanamurthy et al.,

1993). Enzymes must diffuse into the substrate through pores, then the solubilized

substrate must then diffuse out. Depending on the porosity, much of the degradation may

be at the surface (Knapp and Howell).

Mass diffusivity. Pick’s second law is generally applicable to SSF systems, as follows:

dC d^C d^c" = D dt dx d.2 y "I" n _ dz 2

where C is concentration, t is time, D is the diffusion coefficient, Rt is the biochemical

reaction rate. The situation is somewhat more complex than the above equation, though.

The solid matrix may include pores containing gas and liquid through which the solute

(enzyme, product, or substrate subunit) also diffuses. Furthermore, D may not be

constant, as assumed in Pick’s second law as stated above, for the highly concentrated

systems found in SSF. For oxygen, however, which usually has a low concentration,

constant D is a safe assumption (Georgiou and Shuler, 1986). Furthermore, apparent D

may change because it is dependent on the porous structure of the particle which changes

during the fermentation.

Within the substrate, diffusion may be through the solid material or through pores

within the solid; as such, the mass diffusivity will vary as the porosity changes (Ramana

Murthy, et al., 1993). It is assumed constant, independent of concentration within the version of Pick’s law given above, but in actuahty may vary with concentration. For SSF, the oxygen concentrations are expected to be low enough within the substrate so that D is, indeed, independent of concentration.

42 There are few data available on oxygen diffusivity in SSF systems. Overall

oxygen transfer rates and volumetric transfer coefficients (kta) are, likewise, unavailable

for most systems. André et al. (1981) suggests a method for determining mass transfer coefficients in three phase SmF systems which involves measuring the outlet response to a step input change of carbon dioxide. The advantage of using carbon dioxide is that the relatively large solubility of carbon dioxide in the liquid phase leads to a slower, and more easily discernible response. A correction factor is applied to account for the difference in diffusivity of oxygen and carbon dioxide. It is possible that this work could be applied to SSF.

The effective diffusion coefficient, D, has been determined for carbon dioxide and oxygen in a solid state system in ^hich. Aspergillus niger grows on an inert support saturated with growth medium (Auria et al., 1992). It was found that the diffusion coefficient was strongly dependent on biomass concentration; as the biomass increased, the porosity of the packed bed reactor decreased, reducing the diffusion coefficients to as low as 5% of the original values.

Not only is the diffusivity difficult to measure and predict, the interaction of kinetics of the reaction with transport properties through the diffusivity is unknown.

Ramana Murthy et al. (1993) have called for a systematic study of diffusivity in SSF systems to improve the understanding of SSF. Coupled with an accurate model of cell growth kinetics on solids surface, such information will aid in design of large scale SSF processes (Rathbun and Shuler, 1983).

43 Heat transport considerations

Temperature is critical to metabolism, controlling such functions as growth,

sporulation, and product formation. Metabolic heat, reportedly produced in the range of

80 to 3800 kcal per kg dry matter (Saucedo-Casteneda et al., 1992), can result in large

gradients, as seen in compost piles which approach 70°C in their interior (Ramana

Murthy et al., 1993). There are significant heat gradients even in shallow beds (Rathbun

and Shuler, 1983). Because of the low moisture content of SSF substrates, thermal conductivity is low. In packed bed SSF, conduction through the bed appears to be the main resistance to heat transfer rather than convection at the reactor wall or convection, as shown by estimates of the Biot number (hRa/k, where h is the convective wall heat transfer coefficient, Ra is the reactor radius, and k is the thermal conductivity) and Peclet number (ot/dpU, where a is the thermal diffusivity, dp is the particle diameter, and u is the air velocity), respectively (Saucedo-Casteneda et al., 1992). As the cultivation proceeds, particle porosity and void space usually decreases, worsening heat transfer problems.

As bed depth increases for tray type fermenters, steeper temperature gradients are observed which correlate with steep enzyme activity gradients, inverse to temperature

(Ghildyal et al., 1993). Temperature inside the reactor was 21.2°C higher than ambient at the maximum, which occurred at a tray depth of 160 mm, and at the same depth, enzyme activity was 86% lower than at the top of the tray. Oscillations in temperature were observed, indicating that as high temperatures inhibited growth rate, metabolic heat production decreased allowing the medium to cool to a point at which growth rate increased and metabolic heat generation and therefore temperature increased again.

44 Improvement of heat transfer through equipment design has largely been ignored,

with the result that heat dissipation is considered to be one of the chief disadvantages of

SSF compared to SmF (Ramana Murthy, et al., 1993).

Attempts at control of heat transfer have been primarily in packed bed reactors

where increased aeration reduces temperature gradients, but also decreases moisture

content of the bed. As a rule of thumb, approximately 7 g of water evaporate when dry air

is used to remove the heat from oxidizing one gram of starch (Saucedo-Casteneda et al.,

1992). Partially humidified air may be used to overcome this problem (Durand et al.,

1993). When increased temperature is desired, reduced aeration will allow accumulation of metabolic heat, but oxygen concentrations will decrease. Several workers have investigated the use of air to control the temperature in packed bed reactors (Grajek,

1988; Narahara et al., 1984). In their study of temperature gradients in packed beds,

Saucedo-Castenedo et al. (1990) used air flow together with a water-cooled jacket to control temperature.

The metabolic heat generated can be related to the substrate consumed as

= S-Mj where Eh is the total metabolic heat generated, g is the heat/unit mass dry matter consumed, and My is the mass dry matter consumed. This must be removed to prevent excessive temperatures in the reactor. The amount of air necessary to do this is calculated by

45 where AH is the difference in outlet and inlet air enthalpy and Lc is the amount of cooling

air required.

Grajek (1988) gives a relationship for enthalpy of air with temperature and

moisture content,

H = 1.006 r + 1.86 r + 2500%,,

allowing AH to be estimated as follows:

A// = 2.866(r,-7;) + 2500(%„,-% „)

where 1.006 is the specific heat of air (kJ/kg-K), 1.86 is the specific heat of water

(kJ/kg-K), Xw is the maximum water content in the air (kg/kg) at temperature, T, and

2500 is the latent heat of water (kJ/kg), and the subscripts" 1 " and "2" indicate inlet and

outlet conditions, respectively.

Thermal diffusivity. Thermal diffusivity is also of interest, and again, little work has been

done in this area. Thermal diffusivity has been measured in various foodstuffs by steady

state and transient methods (Barrera and Zaritzky, 1983; Gordon and Thome, 1990), yet

literature data on thermal diffusivity of typical solid substrates is scanty. Lai et al. (1989)

demonstrated a transient method for use with SSF substrates which consists of measuring the rate of change in temperature of a sample packed in a copper tube, both at its surface and at its center, when it is moved from one constant temperature bath to another of a different temperature. They calculated thermal diffusivity by the following equations:

46 T.-T, T.-T. JM)

f a 2303 R i ' A '

where Ta is the temperature of the water bath, Tc is the center temperature of the sample,

Tj is the surface temperature, is a zero order Bessel function. Pi is the value of the first

positive root of the Bessel function,/is the slope index of the heating or cooling curve (in hours, equal to the time required for a one logarithmic decade decrease in the value of Ta

- Tc), a is the thermal diffusivity (m%), and Rd is the radius of the diffusivity cell (m).

The value of thermal diffusivity that they determined for sorghum brewing mash, a equal to -3.9 m"/h, was not dependent on fermentation time.

Studies of growth kinetics, product formation kinetics, inoculum size, and nutrient requirements are needed to provide data for modeling and design. Limited data are currently available for testing the few models that have been formulated (Laszio and

Silman, 1993).

Determination of biomass in SSF

Numerous methods have been suggested for monitoring cell growth during cultivation in SSF. The presence of the solid substrate is the major difficulty in following cell growth, especially on-line. Direct measurement as well as correlation with cell components have been suggested. It has been suggested that with whatever method is used, if it requires SmF culture of biomass to prepare a standard curve, the liquid broth be matched as closely as possible to the solid substrate composition, perhaps by preparing a

47 liquid digest of the actual solid substrate (Matcham et al., 1985). Determination of

viability and growth phase is also of interest; in order to simultaneously determine

biomass, viability, and growth phase, generally several methods must be used together.

The final selection may be based as much on procedural considerations such as time

required to perform the analysis as on accuracy of results; for example, chi tin content

estimation by colorimetric methods required seven hours for processing 16 samples in

one study, while ergosterol measurement by GLC required only five minutes per sample

(Matcham et al., 1985).

Off-line, direct methods

Biomass dry weight was directly determined by cu\\.\xnngAspergillus oryzae on a cellophane membrane placed on koji juice agar (Huang and Hsu, 1993). Though this would appear to cause mass transfer limiladons of substrate through the membrane, growth parameters, such as maximum specific growth rate and the Arrhenius constant for the same, obtained in this manner compared quite well with those determined for the fungi grown directly on rice.

Mitchell et al. (1986) attempted another direct determination of mycelial dry weight by growing Rhizopus oligosporus on a model substrate of cassava starch in kappa- carrageenan gel. By dissolving the kappa-carrageenan, the mycelia could be recovered by centrifugation, allowing kinetic studies of fungal growth. However, incomplete mycelial recovery was noted, though growth was adequately followed.

A further attempt to directly measure biomass was made by Gordon et al. (1993), who used Fourier transform infrared (FTIR) analysis to determine cell mass in SSF

48 samples. Though they were successful in estimating biomass concentration, sample

preparation was tedious, requiring 24 hours drying followed by pulverization under liquid

nitrogen, then preparation of sample plus potassium bromide disks.

Off-line, indirect methods

Glucosamine content. Acid hydrolysis of fungal chi tin to N-acetylglucosamine followed

by assay of glucosamine content by colorimetric methods allows an indirect

determination of fungal biomass (Aidoo et al., 1981). However, no determination of

viability or activity of that cell mass can be made. The glucosamine content remained constant during fungal development but varied with medium content, therefore it can not be used to compare growth in different media (Desgranges et al., 1991a). Roche et al.

(1993) found that glucosamine content also varied with strain, as well as with carbon and nitrogen sources, but were able to relate the glucosamine to cell growth for a given strain in a given medium by the following equation:

B = a O + b where B is fungal dry matter (mg), G is the quantity of glucosamine Qig), a is the slope

(mg/|ig), and b is a constant (mg).

Ergosterol and total sugar content. Ergosterol content, measured after extraction by

HPLC by the method of Seitz et al.(1979, cited in Ramana Murthy et al., 1993), could be used as shown in one study to determine spore quantity and sporulation of Beauveria bassiana, but not biomass amount because it appeared only at sporulation. It was affected by media composition (Desgranges et al., 1991a). In a study ofAgaricus bisporus grown on grain, however, ergosterol was found to be directly proportional to

49 biomass for up to 28 days after inoculation (Matcham et al., 1985). Total sugar content

can be measured by colorimetric methods, but was found to be dependent on fungal age

and medium composition and so was an unreliable measure of biomass, except during the

sporulation phase when it can be used to compare biomass in media with the same

composition of carbon and nitrogen sources (Desgranges et al., 1991).

DNA content. DNA content can be determined by sonicating a sample to release DNA

without affecting substrate, then assaying the DNA by fluorometry. Correlation of DNA

to biomass by use of a standard prepared from cells grown on soluble substrate makes the

incorrect assumption that DNA content is constant within the cells throughout growth

(Desgranges et al., 1991a). For best correlation of DNA to biomass, measurements

should be taken at the end of the fermentation when growth rates are slow and DNA

levels relatively constant (Kennedy et al., 1992). Though somewhat inaccurate at other times, measurement of DNA can assist in determining transition to stationary phase growth, and so can compliment techniques such as measurement of carbon dioxide evolution that do not accurately reflect this transition (Kennedy et al., 1992).

Protein. A number of researchers have used protein measurement, usually by the

Lowry method (Lowry et al., 1951), to measure protein in SSF and have correlated it with biomass.

On-line methods

The quest for on-line determination of biomass is as old as industrial fermentation, but it has yet to be totally satisfactorily achieved. The advent of inexpensive and powerful computers is helping reach this goal, as was predicted early in

50 the microcomputing revolution (Humphrey, 1977). By monitoring process data and

performing mass balances on easily measurable species such as carbon dioxide and

oxygen, computers allow quick estimation of biomass. These techniques continue to be

improved as better understanding of the stoichiometry of microbial growth is developed.

Several methods are available for measuring gas concentrations on-line.

Ramstack et al. (1979) suggest the use of a gas chromatograph (GC) for measuring

oxygen, carbon dioxide, and other volatiles. Wang (1993) developed an artificial nose

composed of piezoelectric crystals coated with bullfrog olfactory receptor proteins and

found different oscillation profiles for gas samples from compost taken at different stages.

Such an application may not be useful currently in its crude stage of application, but it is

this sort of novel idea which may result in a successful method to easily and accurately

monitor SSF.

Oxygen uptake. The growth of Candida lipolytica, a yeast, on pulp medium

(glucose solution plus pulp) was simulated using oxygen uptake rate (OUR) as a

parameter, demonstrating that estimating growth of aerobic organisms in SSF is possible

by measuring OUR (Sato et al., 1983) and relating it to cell growth as follows:

OUR = ^ M o * where Yx/o is cell yield with respect to oxygen, m is the maintenance coefficient for oxygen, X is cell mass, t is cultivation time, and Mo is initial dry mass of the solid substrate. In this treatment, endogenous metabolism is considered to be constant, though this is not necessarily true. Sato et al. (1983) also demonstrated the application of their

51 final model of cell grov/th as a function of OUR to the growth of mold data for CO?

evolution when the RQ is approximately equal to one. Measurement of OUR by gas

chromatography (GC) was demonstrated to be a useful method of monitoring SSF

processes (Ramstack et al., 1979).

Off-gas carbon dioxide analysis. Carbon dioxide measurement in the off-gas has the

advantage that data are available irmnediately, on-line, and it is not affected by the

presence of solid substrate. It fails, however, to detect the stationary phase because cells

continue to produce carbon dioxide through endogenous respiration in the stationary

phase because maintenance metabolism continues to produce carbon dioxide. Sugama

and Okazaki (1979, cited in Ramana Murthy, 1993) correlated carbon dioxide evolution

with glucosamine content and obtained a value of 1 ml CO? per 2.12 mg mycelia. They

estimated endogenous carbon dioxide evolution separately and found that monitoring

carbon dioxide could detect even low activity, but was dependent on medium

composition. Ramstack et al. (1979) measured carbon dioxide evolution with a GC,

allowing them to monitor SSF processes. They note that RQ is a function of moisture,

temperature, and oxygen concentration. Desgranges et al. ( 199 lb) found good correlation between carbon dioxide and glucosamine, which they had previously shown to correlate well with biomass for Beauveria bassiana grown on clay bead supports, though they note that carbon dioxide estimation can not be applied for comparison of growth on media of different composition.

Light scattering. Kennedy et al. (1992) proposed using light scattering to measure biomass in solid substrate cultivation in SmF. This technique is based on the

52 phenomenon that at lower wavelengths, cells scatter more light than substrate and at

higher wave lengths cells scatter less light than substrate. At a certain point, both cells

and substrate scatter light equally; this is called the invariant region. The region location

is independent of substrate concentration but is affected by cell concentration and so can

be used to find biomass concentration based on a predetermined relationship. Though

this was applied to the SmF of solid substrate, it should be possible to adapt it to SSF. If

mycelia are present within the solid substrate, however, this method will not accurately

reflect cell growth. Morphology changes in the cells during fermentation will affect how

the cells scatter light and w ill, therefore, cause difficulties in measuring cell concentration (Kennedy et al., 1992).

IR analysis. Silman et al. (1983) demonstrated that reflectance IR analysis has some promise as a nondestructive technique for monitoring SSF’s, obtaining high correlation coefficients between IR peaks and the results of chemical assays for starch, oil, protein, and water, though the effects of biomass were not included. Desgranges et al. (1991b) also suggested that IR may be well adapted to SSF cell growth measurement. This method is based on the measurement of reflected light and was found to successfully estimate cell components, such as glucosamine and ergosterol, and medium residues, such as sugars and nitrogen, allowing fast estimation of fungal biomass without pretreatment of samples. Optical fibers and IR spectrophotometers currently used in the food industry can most likely be adapted to on-line measurements of SSF systems.

However, interference of medium components with IR analysis may require other methods be used.

53 Pressure drop. Gumbra-Sa'id, et al. (1993) investigated the use of pressure drop

measurement to determine cell growth in a packed bad, and found that when the solid

substrate is consumed during the fermentation, as opposed to when it acts merely as a

support for a biofilm, pressure drop is not a reliable measure of growth, but protein

measurements are of some use. However, pressure drop may provide an inexpensive

method of monitoring a packed bed SSF.

Gumbira-Sa'id, et al. (1993) found the apparent bed voidage by using the Ergun equation, as follows:

L - where Ap is the pressure drop (g weight/cm"); L is the bed height (cm); gc is a conversion factor (980 g-cm/g weight-sec"); e is the bed voidage; p. is the gas viscosity (g/cm-sec); Uo is the superficial gas velocity in the absence of packing (cm/sec); 4>s is the sphericity of the particle; dp is the diameter of the particle (cm); and Pg is the density of the gas

(g/cm^). This approach may prove useful in monitoring the change in bed voidage and relating it to mass transfer during SSF.

Villegas et al. (1993) related pressure drop to stages of mold differentiation in a

SSF of Aspergillus niger on wheat bran, but could not directly correlate total biomass with pressure drop.

54 Microscopic techniques

Direct visual monitoring of cell growth within the solid substrate may be possible in some cases with the use of scanning confocal laser microscopy (SCLM), a technique which allows horizontal and vertical sectioning of biofilms. Wolfaardt et al. (1993, 1994) have demonstrated the use of SCLM in visualizing biofilm structures, and its use in clarifying the relationships between species in degradative consortia, a technique which may prove useful for mixed culture SSF processes. Most SSF substrates are opaque and therefore would interfere with these techniques, but use of model substrates which are transparent, such as calcium alginate, may allow application of SCLM techniques to the study of growth in three dimensions, as exists in SSF.

At present there is no one accepted method of determining biomass concentration in SSF processes. This has been one of the greatest difficulties facing engineering design of SSF reactors and processes.

Control and regulation of metabolism and differentiation

Studies of the control and regulation of cell metabolism and differentiation are needed. Control of cell physiology to maximize production of the compound of interest while delaying sporulation (in the case of fimgi) so as to convert the maximum amount of substrate would be desirable. Methods to increase the nutrient availability from the substrate are also of interest. Strategies such as the use of mixed cultures to avoid catabolite repression (one culture consumes the solubilized substrate at a rate which prevents the other culture from turning off its enzyme production) and alteration of fermentation conditions to suppress sporulation are possible avenues of research. Liquid

55 movement, as in roller bottles, appears to suppress conidial growth (Mak et al., 1990) and

agitation of solid substrates also affects sporulation. Sporulation did not occur in an

agitated rice SSF, though the speed and type of agitation (circular vs. linear) was important (Hesseltine, 1972).

Improved reactor design

Improved reactor systems should be a fruitful area of research. Compared to SmF systems, almost no work has been done developing novel SSF reactors. In particular, reactors which address heat dissipation difficulties and allow continuous production have not been investigated. There is vast room for improvements in these areas.

Bajracharya and Mudgett (1980) describe a SSF system with a closed aeration loop which allows careful control of oxygen and carbon dioxide concentrations. They propose that such a system would be useful for investigating the effects on metabolism of oxygen and carbon dioxide concentrations and would allow the control o f cell metabolism through such effects.

Fluidized beds

Though literature on this topic is not abundant, several workers have reported successful use of fluidized bed SSF. Kokitkar and Tanner ( 1990) reviewed several studies and found that in general, gas fluidized bed reactors provided higher productivity along with easier control of pH, temperature, and moisture content than could conventional SSF systems. Spore germination is more uniform and inhibitory products can be stripped from the fermentation mash by the air stream. The increased mass

56 transfer and heat transfer lead to shorter fermentation times. However, they point out that

more data is necessary to confirm these points.

Kikkoman Company (Tokyo, Japan) is reported to use an air fluidized bed reactor

with agitation to produce Aspergillus sojae for soy sauce production (Kunii and

Levenspiel, 1991). A similar reactor was used by Tanaka et al. (1986) to produce

Saccharomyces cerevisiae biomass and enzymes of Eupenicillium javanicum from wheat

bran powder. They report increased productivity over static SSF in both cases; however,

no aeration was supplied to the static culture, so it is not known how these organsims

would produce in packed bed SSF with forced aeration. They note that the fluidized bed culture has a large effective surface area for microbial growth, can be maintained at uniform culture conditions, is easy to supply with additional moisture, nutrients, or pH- control by injecting a mist with the fluidizing air, and removes metabolic heat and carbon dioxide effectively. Furthermore, there would be no transport phenomena difficulties in scale up.

Matsuno et al. (1993) employed a vibrating air-solid fluidized bed reactor for the cultivation of yeast cells {Saccharomyces cerevisiae HUT 7099) for biotransformation of ketones. They also used their reactor to cultivate Aspergillus sojae and found that solid fluidized culture (SFC) resulted in higher protease and amylase activity, though lower glutaminase activity, than either static SSF or SmF. The yeast cells grown in static SSF had higher biotransformation activity than did cells grown in SFC, which had higher activity than those grown in SmF. The volume of the SSF reactor was much larger than

57 the volume of the solid substrate charged, however, possibly minimizing the heat and

mass transfer limitations which are usually observed in static SSF.

Bauer (1986) has developed a fluidized bed reactor for SSF and has previously

demonstrated its potential for ethanol production and cell growth. The same reactor has

also been shown to facilitate the biotransformation of glutamate, cysteine, and glycine to

glutathione at high yields, up to 40 mole% compared to 4 to 8 mole% observed in SmF.

This is attributed to the lack of dilution of precursors by culture broth as would occur in

SmF, as well as the ability to uniformly add the precursors in the form of a spray at the

top of the fluidized bed.

Hong et al. (1989) used an air-fluidized bed fermenter for the SSF of baker’s yeast

{Saccharomyces cerevisiae). They observed that some yeast proteins were entrained in

the fluidizing air, probably within microdroplets of water within the air stream. Such a

phenomenon should be accounted for in the event that lower than expected protein

production is observed in fluidized bed culture.

It is of interest that at least two reports have been made of altered cell morphology in fluidized SSF. Matsuno et al. (1993) found that S. cerevisiae cells decreased in cell size and changed shape from ellipsoid to spherical when grown in a fluidized bed SSF reactor. These cells were also enriched in mitochondria, exhibited lower glucose uptake, and higher oxygen consumption rates than cells grown in static SSF, possibly indicating that cells grown in fluidized SSF were better adapted to aerobic conditions than those grown in static SSF. Tanaka et al. (1986) obtained similar results for 5. cerevisiae grown in an agitated fluidized bed SSF reactor. The meaning of these changes is unclear, but it

58 may be related to the observed advantages of fluidized bed culture and, as such, should be monitored in future work.

Modeling

Few examples of modeling are reported in the literature because of the scarcity of data and the difficulty in measuring physical parameters in the heterogeneous SSF environment. The models that do exist are based on measurable quantities such as product formation, substrate conversion, and oxygen concentration in the media. A need for improved models for nutrient diffusion and biomass growth exists. Research could be directed initially towards checking the application of diffusion limitation criteria for modeling as has been used for mold pellet models (Kobayashi et al., 1973; Aiba and

Kobayashi, 1971). Two-dimensional cellular automata models which base growth on a limited set of logical rules have somewhat successfully modeled colony growth on an agar plate (Laszlo and Silman, 1993) but this is quite different from 3-D growth in cruder, more irregular substrates.

One of the deterrents to the development of useful models is the complexity of solid state fermentation. As depicted schematically in Figure 2.2, many interrelated processes are taking place simultaneously. Oxygen diffuses into the substrate particle, both via pores and through the solid matrix. It is consumed by the microorganisms, at a rate which may be proportional to the oxygen concentration attained. The cells produce heat and carbon dioxide as they grow, both of which can adversely affect growth rates.

The cells also produce enzymes which must diffuse into the solid medium and catalyze the hydrolysis of the enzyme-degradable components of the heterogeneous solid. These

59 now soluble components must diffuse back to the cell for use in cell growth and product formation, and the concentration of the soluble substrate will also affect cell growth and enzyme production. Product inhibition, pH, and water activity may also affect cell growth. And this is only one particle of substrate in the reactor - in a packed bed reactor, it is likely that ambient conditions (temperature, bulk gas concentrations) will vary depending on position in the reactor.

Growth kinetics have been approached by several workers. Ngian et al. (1977) report that prediction of kinetic parameters requires elimination of mass transfer resistance, making the parameters difficult to measure. Approaches taken to measure the kinetics have included models based on product formation, models based on substrate consumption, and models based on oxygen concentration. Heat transfer and temperature gradient modeling has also been attempted.

Models of growth kinetics

Several workers have attempted to model mycelial growth on solid substrates.

Trinci (1971) found that colony growth of Aspergillus nidulans followed a pattern of four growth phases; lag, exponential, deceleration, and constant growth rate. In comparisons of A. nidulans, Mucor hiemalis, and Pénicillium chrysogenum growth in SSF and SmF,

Trinci found that growth rate for SmF was not correlated to growth rate in SSF; the fastest growing organism in SmF was not the fastest of the three in SSF. Perhaps this is because of differences in oxygen saturation constants; for SSF the oxygen concentration is much higher and so organisms with a high oxygen saturation constant may be released

60 from oxygen limitation. If the specific growth rate for that organism is otherwise high, it

may thereby exhibit faster growth in SSF than in SmF relative to other organisms.

Models based on product formation

Sugama and Okazaki (1979, cited in Ramana Murthy, 1993) based a model on

carbon dioxide evolution during log phase growth of A. oryzae on solid media. Their

work resulted in the following mathematical expression of cell growth related to carbon

dioxide formation rate:

dA _ d{A -a.ç) da.^ dt dt dt

m = nig 6*“

where A is the amount of CO 2 evolved per unit dry matter consumed (mg/g), ac is the

amount of CO? evolved for endogenous respiration per unit dry matter consumed (mg/g),

t is time (h), m is the amount of dry mycelia produced per unit dry matter consumed

(mg/g), nio is the initial amount of mycelia per unit dry matter (mg/g), andji is the

specific growth rate (h'*). Substitution and integration leads to the following expressions:

m n

m //

where k[ is the amount of CO? evolved by nonendogenous respiration per unit dry weight of mycelia formed (mg/mg) and k? is the amount of CO? evolved by endogenous respiration per unit dry weight of mycelia formed (mg/mg). Estimation of the numerical

61 values of the kinetic parameters allowed estimation of the proportion of CO 2 resulting

from endogenous respiration.

Okazaki et al. (1980) modified these results, using the logistic equation to account

for mycelial growth in the stationary phase. This resulted in the following model:

dA k^Nfi ke~*“ ^ k^N dt \ + ke'^' l-\-ke-^' l + ’

where k is equal to the quantity, N/mo - 1, with N equal to the maximum value in mg of

the dry mycelial mass per g dry matter, and rnj is the initial value of dry mycelial mass

(mg/g dry matter).

Upon integration, this yields

k,N k.N +k k.N A = . - .... In------l + ke-^' n l + k l + k

If t is large enough, the derivative form simplifies to

1 dA

and N and dA/dt can be estimated experimentally. The growth of koji mold on rice and on wheat bran was well represented by the model. The authors note that by using the respiratory quotient (RQ, the moles carbon dioxide consumed divided by the moles oxygen consumed), these equations can be converted to terms of oxygen consumption.

Carrizalez et al. (1981) also based a model on carbon dioxide production, relating it to the specific rate of biomass growth of Apergillus niger on cassava flour, assuming that carbon dioxide evolution is growth associated. Their resulting model and its simplification followed by integration are as follows:

62 dp I f P = Ke'“

log/> = l o g f ^

where K = (QpXo)/(p.V), Qp equals the specific rate of carbon dioxide formation, Xq

equals initial biomass, P equals the carbon dioxide concentration in the gas phase, and V

is the volume of sodium hydroxide solution consumed. They used their results to

determine growth constants for A. niger by plotting log P vs. time which results in a

straight line with slope equal to \i/2.3. This model does not apply during lag, stationary,

or death phases of cell development.

Mitchell et al. (1991) modeled Rhizopus oligosporus for growth on a model

substrate, directly relating enzyme activity and biomass production. They based their

model on the stoichiometry of glucose consumption, assuming no accumulation of

glucose. This yielded the following equations:

E = r,t 0r, where E is the enzyme activity (mg glucose/hr cm“) and is experimentally determined by the given equation, Y% is the yield coefficient (mg dry wt/mg glucose), X is the biomass density (mg dry wt/ml), t is time (h), with ti the time at which maximum enzyme activity is achieved, r, is the rate of increase in enzyme activity until the maximum is attained at

63 time t, and ko is the exponential decay constant for enzyme activity (ti‘). The lag period

is ignored and time equals zero at the time at which enzyme first appears.

Combining the rate of change of biomass concentration with the enzyme activity

equation yields

dX ^ — = Tfr,r 0

^ = y , ( r , , 2 , ,

After integration

X = Xo + Y x r ,Y 0

where X q is the initial biomass concentration. It may be possible to improve this model by accounting for enzyme degradation from time zero or by incorporating a logistic equation which may allow for the lag period. As it stands, it does show general agreement with the logistic model of Okazaki et al. (1980), but has the advantage of being based on enzyme activity, which can be directly measured, and not on biomass.

In further work, Mitchell et al. (1991) proposed a mechanistic model which describes the steps which take place in SSF processes. Their system consisted of

Rhizopus oligosporus growing on a membrane filter atop a model cassava starch medium.

The steps required for growth and the equations which describe them are as follows:

1. The enzyme is released at the mycelial surface.

,1 , \ dX'^ ~ ^ ~ V

64 where Je I g is the flux of enzyme across the membrane filter, E is the enzyme

concentration, and De is the effective diffusivity of the enzyme. H(fe-t) is a Heaviside

function such that at t less than tE, H(t) equals one and enzyme diffusion equals the rate of

enzyme production, and at t greater than or equal to %, H(t) is equal to zero, reflecting a

halt in enzyme production.

2. The enzyme diffuses through the substrate to the starch molecule.

d E d -E * ■ ax^

where x is the vertical position in the substrate slab.

3. The starch is enzymatically hydrolyzed to glucose. as k„Es a t K,+s

where S is the substrate concentration. Km is the apparent Michaelis-Menten constant,

and kcat equals 1.0 because E is expressed in terms of activity.

4. The glucose diffuses to the mycelium.

a c k „ E s a'-G at K^+s ° ax^

where G is the concentration of glucose, and Do is the effective diffusivity of glucose in the medium

5. The mycelium uses the glucose to produce more mycelium (growth).

65 Ks+G\s P(X) = X+iX^-X)H(X-XJ

where Jo 15 is the flux of glucose through the membrane filter, qm is the maximum specific rate of glucose uptake, Ks is the saturation constant for glucose uptake, and G | , is the glucose concentration at the surface. H(X-Xc) is another Heaviside function and is equal to zero when X is less than Xg and equal to one when X is greater than Xg. This means that once X reaches a critical value, it becomes constant at that value.

The above equations describe the diffusion of glucose to the surface. The actual use of glucose by the mycelia for production of additional mycelia is given by the following equation:

+G\s where Y x /g is the yield coefficient for biomass on glucose and, therefore, based on the earlier definition of qm, Yx/o-qm equals p-max-

Various relevant parameters such as enzyme diffusivity and maximum specific substrate uptake rate were estimated and used in equations proposed for each step, but the model proposed above failed to agree with experimental data unless the aforementioned parameters were further adjusted. Assumptions used in the model may have also caused some error; for instance, the model assumes a constant yield coefficient. Under starvation or low nutrient conditions, which may have occurred in this fermentation as X increases

6 6 and G decreases, yield coefficients are not necessarily constant (Silva, 1993).

Furthermore, it was assumed that the filter offered negligible resistance to diffusion, which may not be accurate. Additionally, the glucose uptake rate is based on measurements for SmF grown mycelia and may not reflect the actual rates in SSF because of limited surface area for uptake in the solid case.

Otherwise, the mechanistic model seems reasonable. If parameter estimation can be made more accurate and flaws in the assumptions overcome, a model such as this which accounts for the complexity of the system may ultimately be preferable to simplified models of just one or two equations.

Models based on substrate consumption

Nutrient concentration, especially of nitrogen and oxygen, is important in modeling mold growth because it directly affects cell differentiation. Mold differentiation is one of the few areas in which our understanding is greater for SSF than for SmF (Ramana Murthy, 1993).

Georgiou and Shuler (1986) modelled colony growth on a surface with no penetration of mycelia into the substrate. The model was based on nutrient uptake and the interrelationship between various undifferentiated states. Linear extension rate, IQ, was related to peripheral growth zone width, w, and the specific growth rate of the organism, |x, by the equation

= w/r as proposed by Trinci (1971), and so concentric rings of biomass were considered to be the same age.. Biomass was classified as one of four types; Xi, vegetative; Xi,

67 competent (able to sporulate); X3 , conidiophore; and X4 , conidial biomass. Differential

equations were developed to account for both the mechanistic processes such as nutrient

uptake and the interrelation between various differentiated states and for diffusional processes, assuming that only glucose was limiting.

Though limited in application (SSF processes of interest generally include mycelial penetration into the substrate, include the enzymatic hydrolysis of a polymeric substrate, and often will include oxygen limitation as well as glucose limitation) the model is of interest in the way that it relates the various differentiated states on a basis of time and nutrient concentrations.

Models based on oxygen concentration

For aerobic cultivations, models for oxygen gradient prediction would be useful in reactor design in which it is desirable to minimize mass transfer limitations. Oxygen concentration affects biomass and enzyme activity and is critical to reactor performance.

Little information exists on mass transfer in SSF. There are, however, numerous models of oxygen transfer in mycelial pellets have been suggested (Yano et al., 1961 (cited in

RM93); Aiba and Kobayashi, 1971; Kobayashi et al., 1973). Application of these models to SSF is complicated because of particle heterogeneity and mass transfer resistance, along with increasing biomass concentration and increasing porosity as the cultivation progresses, but understanding of diffusion within mycelial pellets can provide some insight into SmF processes (Ngain et al., 1977).

Figure 2.3 provides a schematic diagram of a mycelial pellet. Oxygen diffusion within that pellet can be described by an oxygen balance as:

68 â s 2 d s r ' dr -P n ü where S is substrate concentration, t is time. De is effective oxygen diffusivity within the pellet, r is the radius, pm is the pellet density, and Q is the specific respiration rate, a function of substrate concentration. Kobayashi et al. (1973) give the relationship for Q and S as:

e= (2 „

q = p m S. . where Q is the specific respiration rate at the bulk oxygen concentration, Sy, and Km is the Michaelis constant. Analytical solution is not possible without simplifications, but a numerical solution can be obtained. Yano et al. (1961 cited in Ramana Murthy et al.,

1993) made the assumption of zero order kinetics, so that respiration rate was no longer a function of oxygen concentration, leading to an analytical solution. This allowed calculation of a critical radius at which oxygen concentration approached zero. It also allowed determination that if

> R Pn,Q] there would be no intraparticle concentration gradients. This expression is comparable to

the Thiele modulus (Bailey and Ollis, 1986) for which it can be shown that if

69 or

0-3DA R<3

. ^0 ,

there will be no mass transfer limitations within an immobilized catalyst particle.

Assuming Km/Sy equal to one, Aiba and Kobayashi (1971) were able to use a

modified Runge-Kutta technique to produce a numerical solution. Bhavaraju and Blanch

(1975, cited in Ramana Murhy et al., 1993) followed a similar technique for

0.1

Oxygen concentration gradients in compost has been modelled by Finger et al.

(1976) with the assumptions that there was no product formation and oxygen diffusion

was the limiting reaction. Though the model provides insight into SSF modeling, it does

not provide product formation kinetics nor does it account for the influence of physical

factors such as particle size, shape, surface to volume ratio, or porosity, or changes in any

of these parameters as the fermentation progresses. Assumptions made include uniform

substrate, steady state, only bioreactions occurring, energy for growth is from

biooxidation, oxygen is the limiting substrate, and heat transfer is by diffusion only.

Mass transfer limitations were taken into account and effects of pile geometry, density,

and external oxygen concentration were predicted.

Assuming that at steady state the total rate of biooxidation is equal to the oxygen transfer rate from the bulk gas to the liquid or solid phase containing the organism results in the following equation:

R^ = *^a(c‘ - Q )

70 where Rc is the total rate of biooxidation, kg is the oxygen transfer coefficient, a is the

interfacial area, C is the gas phase oxygen concentration (g/L), and Q, is the interfacial

oxygen concentration (g/L). Assuming C* »C l and an Arrhenius relationship for kg

results in

where A’ is the Arrhenius constant for k, Ea is the activation energy, R is the gas constant, and T is the temperature. The rate of substrate consumption, Rs, can be related to Rc through the yield, (g Oi consumed/g substrate consumed) resulting in an /s expression for substrate consumption in terms of gas phase oxygen concentration as follows:

~ ^0,/S ' - E \ a C Rs = A'exp

Pick’s law gives the rate of oxygen diffusion into the pile (for steady state and in one dimension) as :

- R , = - D

B.C. dC' ^ y = 0, - r — = 0 dy y = i, C* = Co*

By numerical integration, oxygen concentrations were found for each point within the pile.

71 Few models are available regarding the influence of physical factors such as

particle size, shape, surface to volume ratio, crystallinity, porosity of substrate, accessible

area, or change in accessible area. Humphrey et al. (1977) developed a model which

assumed accessible area was equal to the surface of spheres whose size and therefore

surface area decreased with time. Their predictions agreed only qualitatively with

experimental results.

Models based on temperature/heat transfer models

Models which could predict temperature gradients within the reactor would be

useful in reactor design. Because biological activity is highly sensitive to temperature,

such information is essential to proper design, but few such models exist.

Saucedo-Castenada et al. (1990) developed a model for a cylindrical packed bed

SSF reactor and using it estimated that conductive heat transfer resistance in the media is much greater than convective heat transfer resistance in the gas flowing through the bed.

The model was based on 3 variables; X, biomass; S, sugar content, and COi, carbon dioxide evolved. Microbial growth rate, Rx, was modelled as a logistic equation, the rate of sugar consumption, Rs, was considered proportional to growth plus maintenance, and carbon dioxide evolution rate, Rg, was assumed equal to a portion of the sugar consumed. The use of a logistic equation for growth is justified by the limitation of solid fermentation by available surface area, resulting in the concept of Xmax, the maximum possible biomass density; in some media where mycelia freely penetrate the substrate, this assumption may have to be modified.

72 With the specific growth rate, p., assumed independent of substrate concentration

because the residual sugar was found to be much higher than the substrate saturation constant, the following equations were developed to describe microbial growth and product formation.

1 — at t = 0, X = X q max /

o - d S \ ^ R^=—— = — Rx+ntX, at t =0, S = Sq dt ix/5

dC,0, at t = 0, CO-, = 0 dt -

The specific growth rate, Pmox, was related to temperature through an expression proposed by Esener et al. (1981, cited in Saucedo-Castenada et al., 1990),

Aexp(-E„,//?r) Amax - I + iBexp(-£^, //?r)

The necessary parameters were determined by fitting experimentally determined values for Pmax. along with supplemental "data" determined by interpolating the experimental data fitted to a polynomial, to the expression.

The researchers considered heat accumulation to be the major limitation on microbial growth and therefore ignored the mass balance. Selecting a pseudohomogeneous model to handle the heterogeneous nature of the substrate/gas packing and neglecting axial dispersion (the length to particle diameter ratio, L/dp, was

73 around 80, resulting in a high Peclet number), the researchers developed the following energy balance: dr .fd^r \ dr^ — upC + p i- AH)R p c , - ^ = t Jr" r' â r '

B.C. a tr ' = /?„, - k ^ = h{r-T,)

0>r' at r = 0, - 7-7 = 0 dr I.e . a tt' = 0 , r = 7; arZ' = 0, T ' = T, where p is the apparent density, Cp is the heat capacity, T’ is temperature, t’ is time, k is thermal conductivity, r’ is radial position, is the reactor radius, Tb is the bulk temperature, T, is the initial temperature, Z’ is the reactor axial position, u is the air velocity, and AH is the reaction heat.

The variables in the above equation can be expressed in dimensionless form by the following conversions:

r ' r Z ' t' L r = - , 7- = - , Z = - , 4. = -, where d> is the dimensional characteristic time for the system.

Algebraic manipulation of the equation to include the geometric reactor ratios,

L/Ra and Rg/dp, the thermal diffusivity, a, the Peclet number, the Biot number, and the

Damkohler number, and including the dry mass fraction in the heat generation term to simulate fermentation results in the following equation:

74 dT 'd -T 1 dT ffT 1 dt ~ K^Pe dr- ^ r dr I.e. t = 0. T = T .fT , z = 0. r = i B.C. dT r = l. = B i(T -l) dr dT r = 0. 0 dr ~

where L is the reactor length, dp is the particle size, Ra is the reactor radius, Pe is the

Peclet number, T is the dimensionless temperature, r is the dimensionless radius, Fdm is

the fraction of dry matter. Da is the Dahmkohler number (-AHpLRgj/upCpTb), Rgi is the

initial carbon dioxide reaction rate, Rg is the carbon dioxide reaction rate, Tj and Tb are

the initial and incubation temperatures, respectively, z is the dimensionless axial distance,

and Bi is the Biot number. The reaction rates are related to the microbial growth as

follows:

d S 1 -— Rs +mXO d t yX/J ,

n ^ ^X - 1 —

Assuming no axial variation in temperature, which was justified experimentally and explained by the low flow rate of air through the column (which resulted in conductive transport dominating convective heat transfer), and complete symmetry, resulted in the following equation:

75 dT Ld^ d^T 1 dT + d t R /P e dr r dr %

Boundary and initial conditions remained unchanged.

Saucedo-Castenada et al. (1990) compared experimental and calculated

temperature profiles and found satisfactory agreement, though parameter values required

adjustment from estimated values to obtain this agreement. The Peclet number

determined by the model showed resistance to heat transfer by conduction to be much

greater than that for convection. The Biot number showed that resistance was in the bed,

not the reactor wall. This is intuitively satisfactory because the wall should have had

enhanced heat transfer because it was cooled by circulating water, resulting in a large

driving force (Grajek, 1988; Ramana Murthy et al., 1993). Saucedo-Castenada et al. did

not measure transport parameters in the substrate and wall. It is also possible that much

of the resistance is because the substrate thermal conductivity is much lower than that of

the wall.

Finger et al. (1976) developed a conceptual model for heat transfer in compost

piles with aerobic microbial growth simultaneously with the mass transfer model

discussed above. This model provided some insight into the process, showing that

temperature is strongly affected by substrate consumption rate, but neglected heat generated by anaerobic growth which may be significant in composting processes.

Fourier’s law described heat diffusion into the interior of the pile as follows:

HrRs

76 where Hr is the heat of reaction, Rg is the substrate consumption rate, p is the bulk

density, Cv is the average heat capacity, T is temperature, t is time, and k= is the thermal

conductivity. For steady state in one dimension, using dimensionless coordinates.

d -T H^R,=-kc-~Y dy‘

and including the Arrhenius dependence of kc.

r E /"I d^T /R — - -QGXP d y- T y

y = y/L, C =

- dl - - B.C. y = 0, —= = 0 and T = T dy y = 1: C = 1

where L is the thickness of the pile, CJ is the oxygen concentration in the atmosphere, T„

- To is the maximum temperature range anticipated, a is the interfacial area. A’ is the

Arrhenius constant, R is the gas constant, HR is the heat of reaction, k is the thermal diffusivity, and C* is the oxygen concentration in the gas phase.

For non-steady state, two dimensions, where the length is much greater than the width or height, the resulting equations for both heat and mass transfer are

pC , [dy^ dz^

— = Aexp aC + D I RT

77 where a is the interfacial area. A ’ is the Arrhenius constant, is the activation energy, C

is the gas phase oxygen concentration, R is the gas constant, kc is the thermal

conductivity, D is the oxygen diffusivity, and y is the horizontal coordinate. This

equation can be solved numerically. It relates the dependence of temperature on oxygen

concentration through the relationship of bioreaction rate on both.

ASPERGHJJ

Aspergillus spp. belong to the group, Deuteromycetes, or Fungi imperfecti, so

known because of the lack of sexual spore formation in this group. These fungi are septate

and are commonly found in soil and on decaying plant material, and for the pathogenic

species, on the surfaces of animal bodies, such as in the case of athlete’s foot disease (Brock

and Madigan, 1991).

Aspergilli metabolize glucose through both the hexose bisphosphate (Embden-

Meyerhof-Pamas) and hexose monophosphate (pentose phosphate) pathways. Dissolved

oxygen tension, pH, and type and concentration of carbon source are among the many controls on the various metabolic possibilities, determining whether metabolites such as citric acid will accumulate in high amounts (Roehret al., 1992). Formation of metabolites associated with the TCA cycle is increased with high dissolved oxygen tension. Lower oxygen tensions may promote polyol, such as glycerol, formation. Medium pH affects which particular organic acids accumulate, and TCA cycle acid formation is promoted by the use of easily assimilated sugars for a carbon source.

78 Kojic acid, a characteristic metabolite of koji fermentations, providing what is

thought to be important flavor to finished food fermentations, does not fit into the typical

glycolytic pathways (Haraet al., 1992). The primary metabolic source of kojic acid, 5-

hydroxy- 2-hydroxymethyl-gamma-pyrone, is direct conversion of glucose, but may be

formed from other substrates (Zidwick, 1992).

Members of the genus Aspergillus form conidia or asexual spores sequentially from

special conidiogenic cells, phialides, and the conidia form persistent chains as additional

spores are produced (Smith, 1978). The phialides form on aerial mycelia; the resulting

spores are resistant to drying and easily spread through the air, resulting in dispersal of the

fungus. Environmental conditions affect the timing of sporulation; it appears that the

metabolic shift from mycelial growth to sporulation is caused by nutrient limitation or some other limiting condition resulting from the environment of the organism. Temperature has a dramatic effect on sporulation as well, inducing or inhibiting the transition or affecting the morphology of sporulation (Anderson, 1978).

Though the two states, vegetative growth and differentiation to spore producing structures, are not totally incompatible, it is reasonable to see them as competing for resources present as metabolic intermediates (Smith, 1978). Because of this, sporulation may be undesirable for a given solid state fermentation. For example, if the desired product is produced only during vegetative growth, yield will be reduced by any resources shifted to spore formation.

The genus Aspergillus encompasses the fungal species most widely used for enzyme production (Lambert, 1983). Examples of industrially exploited Aspergillus species and the

79 enzymes they produce are listed in Table 2.3. In particular, production of amylase by

Aspergillus oryzae is of great interest. High amylase (both alpha- and gluco-) producing

strains of A. oryzae have been constructed through genetic engineering (Sakaguchi et al.,

1992). The transformed organisms have not yet been demonstrated for koji-making, but the work looks promising for such applications (Hara et al., 1992)

Though amylases are currently produced in submerged culture, strains that produce higher yields in surface culture are ftequently isolated (Cmeger and Crueger, 1982). For instance, a strain that produced 295 U/100 ml in SmF produced 8580 U/100 g in SSF.

Some proteases and pectinases produced hy Aspergillus and Rhizopus spp. are produced in much higher yield in SSF and so are industrially produced in this manner (Lambert, 1983).

AMYLASES

Amylases are enzymes that hydrolyze the bonds in starch, both as amyloses and amylopectins. They are classified by their action as either saccharifying or starch- liquefying, with the former producing free sugars and the latter merely breaking the starch molecule into smaller sections. The food industry uses amylases from several sources, including plants, bacteria, and filamentous fungi to produce glucose and low and high dextrose syrups from starch, to improve loaf quality in bread baking, to improve beer and liquor production, and to clear the haze from fruit juices. Among the fungi that produce amylases are members of the genera Aspergillus, Pénicillium, Cephalosporium, Mucor,

Candida, Neurospora, and Rhizopus (Bigelis, 1992; Crueger and Crueger, 1982).

80 Enzyme Species Biochemical reaction Example of industrial use a-amylase Aspergillus random hydrolysis of a- high dextrin syrup oryzae 1,4 bonds in starch production glucoamylase A. oryzae hydrolyzes single glucose dextrose syrup unit off non-reducing end production of starch or dextrin glucose oxidase Aspergillus oxidizes glucose to blood glucose niger gluconolactone measurement catalase A. niger decomposes hydrogen removal of H 2O2 peroxide to oxygen and from milk after water chemical sterilization P-glucanase A. niger hydrolyzes P-l,3-glucans filtration of beer cellulose Aspergillus hydrolyzes P-1,4-bonds in production of spp. cellulose fermentation substrate from biomass pectinase Aspergillus hydrolyzes a-l,4D- clarifying juice spp. galactosiduronic bonds in and wine pectin protease A. oryzae hydrolyzes many peptide soy sauce bonds production lipase Aspergillus removes fatty acids from cheese flavor spp. glycerol esters improvement lactase A. niger hydrolyzes lactose to digestive aid galactose and glucose from Lambert, 1983

Table 2.3. Aspergillus spp. used in industrial production of enzymes.

81 Starch is a polymer of glucose, common in two forms, amylopectin and amylose.

Amylopectin is highly branched with a - 1,6 branch points; amylose is simply a linear

glucose polymer with a -1,4 glycosidic linkages (Horton et al., 1993). Plant starches often

exist as a combination of the two forms.

The four amylases of interest are a-amylase, glucoamylase, P-amylase, and

pullulanase. Each of these enzymes attacks the starch molecule at different sites. Random

breaks in the linear strand are caused by a-amylase (1,4-a-glucan-glucanohydrolases);

though the branching does not affect its action on other sites, a-amylase will not hydrolyze

fhe a - 1,6 bond. Glucoamylase (a-1,4-glucan-glucohydrolase) acts at a nonreducing end of

a starch molecule to cleave a single glucose molecule from the polymer. Several forms may be produced by the same organism; though extremely similar in amino acid content, the forms vary in their ability to hydrolyze raw starch compared to soluble starch or maltodextrins (Sakaguchi et al., 1992). A double glucose unit, maltose, is cleaved from the non-reducing end of the starch by P-amylase (a - 1,4-glucan-maltohyrolase). Pullulanases will break the a - 1,6 bond and are, therefore, necessary to completely enzymatically hydrolyze amylopectin to glucose (Crueger and Crueger, 1982; Bigelis, 1991b). The use of the various amylases in the production of high fructose com syrup, a commodity product in the food industry, makes the study of improved amylase production of intense interest to the food industry (Bigelis, 1992).

82 SPOUTED BED TECHNOLOGY

Description of spouted beds

Originally developed to dry wheat (Gishler, 1983; Epstein, 1983), spouted beds

achieve similar advantages, such as high rates of mass and heat transfer, for coarse

particles (diameter greater than 1 mm) as does fluidization for fine particles and powders.

To form a spouted bed, a stream of high velocity gas is injected into a bed of coarse

particles; it jets through the middle of the bed, carrying a stream of particles which then

rain back onto the bed. The rise of the particles in the middle of the bed is accompanied

by the sinking of particles in the annular region (Mathur and Epstein, 1974), as shown in

Figure 2.4a. Gas passes through both the spout region and the annular region, as depicted

in Figure 2.4b. Particles reenter the spout region all along the spout/annulus interface.

The particles are generally well mixed (Mathur and Epstein, 1974), though some evidence

has been seen of particle size and density segregation for particles with wide size and

density distributions (Bridgwater, 1985; Uemaki et al., 1983). The rapid mixing of the solids leads to uniform solids temperature (Viswanathan et al., 1986).

Because spouted bed processing results in several advantages, spouted beds now have many applications in areas such as granulations, combustion, drying, and coating

(Mathur and Epstein, 1974). The advantages of spouted bed technology include the following:

• Heat and mass transfer are improved over that in a packed bed (Kunii and

Levenspiel, 1991).

• Solids mobility is good (Viswanathan et al., 1986).

83 • Solids are well mixed (Viswanathan et al., 1986).

• Agglomerations of particles are broken apart by high velocity impacts in the core

region, making the process suitable for sticky materials (Epstein, 1983;

Bridgwater, 1985).

• Solids within the reactor are nearly isothermal (Viswanathan et al., 1986).

The flow pattern of solids in a spouted bed is uniform, not random as in a fluidized

bed, allowing a degree of control over solid and fluid flow that is not possible in a fluidized

bed (Claflin et al., 1986). This allows a straight-forward correlation between cycle time and

mixing time, such that at retention times of six times the cycle time, mixing is near perfect

for a conventional spouted bed (Mathur and Epstein, 1974). Cycle time can be measured

simply by timing the descent of a particle in the annulus. The total pressure drop in a

spouted bed is also always lower than that in a fluidized bed, with a ratio of spouted bed

pressure drop to fluidized bed pressure drop of 0.637 to 0.75 (Mathur and Epstein, 1974).

Disadvantages include the limitation of the technique to materials which are not especially friable or do not produce excess dust. Scanty data in the literature makes scale- up design methods risky (Bridgwater, 1985), though many successful large scale applications have been reported (Bridgwater, 1985).

Applications

Since the discovery of spouted beds in the early 1950’s (Gishler, 1983), many applications have been developed to exploit the advantages that this technology offers.

Table 2.4 lists several of these along with the particular advantage of using spouted beds for that application. Of special interest are the use of spouted bed for cooling fertilizers,

84 in which the excellent heat transfer properties of the bed are exploited, and drying of grains and wood chips, in which the ability of the technique to spout coarse and nonuniform particles is critical.

Application Relevant advantage of spouted bed technology Granulation and drying of Agitation of solids prevents overheating; sticky granules agglomerates disintegrate in spout. Tablet coating Coating solution is misted onto bed; flow pattern of particles is cyclic resulting in even coating. Preheating coal Large particles spoutable. Cooling of fertilizers High heat transfer rates. Drying of grain Large, nonuniform particles spoutable; no damage to grain.

Table 2.4. Applications of spouted bed technology.

Spout formation

Upon starting air flow to a bed of particles, the pressure will rise as the flow rate increases. An internal spout may form before the entire bed begins to spout. Finally, the spout will break through the surface, and the pressure will undergo an immediate drop, to as little as one third of the maximum (Bridgwater, 1985). Definition of the minimum spouting velocity, Ums, is based on the air flow at which the spout dies, not on the initial spouting air flow rate. It is reported as the superficial velocity based on the reactor cross- section, not on the inlet cross-section (Mathur and Epstein, 1974). Figure 2.5 shows a

85 schematic diagram of a spouted bed reactor that illustrates the various dimensions of

interest in describing spouted beds.

The large maximum pressure drop is attributed to the force necessary to break up the consolidated bed before spouting begins (Mathur and Epstein, 1974). If the gas velocity is allowed to gently decrease to zero, then increased to Ums again, the bed again exhibits a maximum in pressure drop, though it is not as large as the maximum associated with the initial spouting event.

Upon entering the reactor, the air flow flares out so that much of it is passing through the annulus. The proportion passing through the annulus depends on the inlet diameter, Di,(increasing Dj causes portion passing through the annulus to increase), included cone angle (increasing angle causes decrease in annular portion), bed depth, Hb, (increasing

Hb causes decrease in annular portion), and gas flow rate (increasing flow rate causes decreasing annular portion) (Mathur and Epstein, 1974). The portion passing through the annulus can be over half of the air flow. Regions of backflow in the lowest part of the bed, likely caused by solids downflow and the venturi effect near the orifice, have been experimentally observed (Rovero et al., 1983)

In a draft tube spouted bed for drying rice, the drying rate was found to be determined by heat transfer in the zone just above the orifice and in the draft tube; surface temperature and moisture underwent rapid change in the spout, then the mass and thermal gradients equilibrated in the annulus (Khoe and van Brakel, 1983). In another draft tube spouted bed study, this one to provide thermal disinfestation of grain, a higher thermal efficiency was found for a conventional spouted bed compared to a solid draft tube spouted

86 bed, while a bed with a porous draft tube had intermediate results (Claflin and Fane, 1983;

Claflin et al., 1986). This is a result of the greater annular air flow in the conventional

reactor. Despite the higher thermal efficiency in a conventional spouted bed reactor, the

disinfestation time was longer than for a draft tube spouted bed. This was attributed to the

broader residence time distribution found in the conventional reactor, a result of particles

frequently entering the spout region before making an entire cycle to the bottom of the

reactor. In the draft tube reactor, each particle must travel to the bottom before entering the

spout.

As air flow rates increase over that required for minimum spouting, the spout can

become unstable, moving towards the reactor walls in erratic fashion instead of staying

centered, and eventually the top of the bed may fluidize.

Design of spouted bed reactors

The first question to answer in the design of a spouted bed is whether or not the

particle of interest is spoutable. Determination of this in a laboratory scale reactor is easily

undertaken, and basic hydrodynamic and process information on the particles can be

obtained at the same time. Features that should be investigated include spoutability,

attrition, minimum spouting velocity at various bed heights, maximum bed depth (if the

reactor has sufficient freeboard space to allow it), fountain height, and downward particle

velocity at the wall (Epstein and Grace, 1984). Agreement with existing correlations of this

test data will assist in selection of appropriate correlations for larger scale reactors.

The minimum spouting velocity, Ums, and the maximum spoutable bed height,

Hm, are two key hydrodynamic characteristics of spouted beds. Uns is the velocity at

87 which a spouted bed just ceases as the spouting gas velocity is reduced. Hm is the bed

height at which it is no longer possible to spout the bed, even with increasing gas

velocity. Above this height the bed fluidizes, bubbles or slugs rather than spouts. The

maximum bed height comes about as a result of the increasing minimum spouting

velocity required to spout increasing bed heights, which at some point reaches the

maximum spouting velocity possible before fluidization or other instabilities arise

(Becker, 1961). As an example of the typical values for Hm and Ums, for a spouted bed reactor with column diameter of 15.2 cm, inlet diameter of 2.54 cm, and with wheat as the spouting particles, Hm is approximately 70 cm and Ums is approximately 1.05 m/'s.

The most important parameter to determine in the design of a spouted bed reactor is the minimum velocity required to spout the substrate. Numerous correlations exist for this purpose; some are better suited to some reactor types and reactor sizes than others

(Mathur and Epstein, 1974). Table 2.5 lists several of ± e more useful correlations and includes comments on their development and applicability. Unlike the situation in fluidization, which, though complex, is somewhat amenable to theoretical analysis, development of Ums correlations have been much more rooted in empirical work. The division of air flow between the spout and the annular region and the entry of solids into the spout along the entire annulus/spout interface adds a degree of complexity that makes purely analytical approaches more difficult; the most valid spouted bed correlations remain empirical in nature.

88 The choice of particle diameter to use in the correlation can dramatically affect

results. Mathur and Epstein (1974) recommend using the arithmetic mean of screen

apertures for closely sized particles and the reciprocal mean diameter,

for beds of mixed size particles, where x, represents the weight fraction of particles of

diameter dpi, and dpi is the arithmetic average of the screen apertures. Further complexity

is introduced when the particles are not spherical or nearly so; in this case, the use of the

smaller dimension is recommended because the particles have been observed to align

vertically in the spout in experiments with wheat. Other shapes may require use of an

equivolume sphere diameter, d«, or the determination of the effective value of dp in a

laboratory spouting apparatus. Others suggest use of an effective diameter, d^m such that

where Ap is the surface area of the particle and A^s is the surface area of a sphere of

equivalent volume to the particle (Kunii and Levenspiel, 1991).

Though the Ums correlations do not generally contain a factor for the effect of the cone angle, this geometric parameter does have an effect on the hydrodynamics (Mathur and Epstein, 1974), with decreased Ums observed for increased included cone angle. The change of the exponent on the Mathur and Gishler equation term, D,/Dc, from 0.333 for included cone angles of 45°, to 0.23 for 60°' and to 0.13 for 85° has been recommended

(Mathur and Epstein, 1974), especially for column diameters of 61 cm or greater.

89 Mathur and Epstein (1974) have carried out an examination of the validity of two

of the more popular correlations, those of Becker and Mathur and Gishler, and showed

that as column diameter increases, for a given Dc/Di ratio, eventually the predicted orifice

velocity will be lower than the terminal velocity of the particle. This is not physically possible; hence, the accuracy of the correlations are questionable for larger columns.

More work is needed in this area to allow confident design of large-scale reactors.

Correlations also exist for the maximum spoutable bed height, Hm- That of

Littman et al. (1977) is frequently cited. It predicts Hm as:

where ds is the diameter of the spout within the annulus, which can be estimated by

McNab’s correlation (1972, cited in Bridgwater, 1985) as follows:

where G is the mass flow of air to the bed (kg/s), Pp is particle density (kg/m^), and Dc is column diameter (m), and Di is inlet diameter (m). This gives good results for estimation of Hm for particles up to 9 mm in diameter and columns up to 30.5 cm in diameter.

Though this and other correlations for Hm exist, they are not particularly practical as most beds are operated below Hm- The main value of predicting Hm is that the upper loading limit can be roughly determined by this estimate. Furthermore, the equation only predicts instability as the result of the fluidization of the annular solids limits penetration of the spout into the bed, so it would not predict other forms of instability, such as choking in the spout or growth of a surface instability in the spout/annulus interface

90 Reference Correlation and Comments Bed geometry Solids Mathur and Gishler, H/Dc= 1.3-6.7 (partial list) 1955 Dc/D, = 3.3 - 6.7 2 l [Pr-P,) Wheat (3.2 mm x 6.4 mm, pp = 1.38 g/cm*) Dc up to 61 cm Rape seed ( 1.8 mm, pp = 1.10 g/cm’> P f e = 30 to 90”. Sand (-20 +30 mesh, Pp = 2.32 g/cm’) development based on dimensional analysis Gravel (-4 +8 mesh, Pp = 2.63 g/cm’) tested for wide variety of columns and materials and good fit found in most cases Coal, rounded (-6 +12 mesh, pp = 1.43 g/cm^) SI units Polystyrene (-6+16 mesh, pp = 1.05 g/cra’) Galena (-20 +35 mesh, Pn = 7.44 g/cm ') Becker, 1961 HÆ>c>l Com (dp s 6.9 mm, pp = l.25g/cm’) D/Dc<0.01 Un. = (/m/ 1 + 0.0071(1)^^ ^IjRcrin Peas (dp = 6.6 mm, pp = 1.38g/cm') NO \UmJ Dc= 15.25 -61 cm Barley (dp = 3.99 mm, Pp = 1.32 g/cm’> 0 = 90 to 120" 1.6cxp(-0.0072Re„) Wheat (dp = 3.38 mm, pp = 1.38 g/cm*) ^2600^ Flax seed (dp = 2.1 m m ,pp= 1.14g/cm ') + 22 = 42 Rape seed (dp = 1.61 mm, pp= 1.12g/cm ') Ottawa sand (dp = 0.76 mm, pp = 2.66 g/cm’)

valid for Ren of 10 - 100 theoretical development that proceeds from prediction of bed properties that are based only on particle and gas properties to those based on column geometry, bed depth, and position within bed ______Charlton et al.. u. = kv°^p'.0.6^ O0.2 Dc = 7.5 cm Glass, steel, copper, and lead spheres Di = 1.27 - 9.5 mm dp = 0.5 - 6.4 mm units: cm-g-s H = 2.5 - 20 cm Pp = 2.6 - ll.O g/cm ’ V is bed volume, k = 10 or 12 depending on base configuration e = 30”, 90” exponents on V and D, were fitted to particular system shallow bed - barely above conical base ______

Table 2.5. Correlations for minimum spouting velocity. (Continued) Table 2.5, continued.

Reference Correlation and Comments Bed geometry Solids Unman and 2 / wide variety of data wide variety of data from the literature Morgan, 1983 from the literature f 1 + 1 + /. V< / j fi, fi, and Cp are functions given in the reference Umf must be determined from other correlations based on theoretical analysis ______Smith and Reddy, I/Î Dc = IS cm Alundum, sand, ciyslolon, and polystyrene, all Uj-1.76 1964 g V % D, = 9 - 19 mm with wide spread in density and size [Pp-Pf) £l 0.64 + 26.8 Hb = 33 - 58 cm P,^c D, / J[\ d C / 0 = 60" • good for mixed sizes, but not closely sized material • SI units g Btunelloet al., 1974 Dc = 30.5 cm mixture o f sorghum (dp = 3.61 mm, p = 1.31 [Pp-Pf) D| s 5.08 cm g/cm’) and soybean (dp = 6.27 mm, p = 1.19 Hb = 50 - 70 cm g/cm’) Pt 0 = 35" CCS units Brunello found surface length mean diameter provided best fit empirically developed for widely mixed panicles ______0.324 Uemaki el al., 1983 0.274 Dc = 20 cm binary mixtures of silica sand (mean diameter of 0.655,0.961, 1.52, and 2.23 mm, p = 2.65 U„, = 0.977 2gH, jPp-Pf) D| = 2.2 - 3.0 cm Hb = 25 - 50 cm g/cm’) Pf 0 = 60" • SI units • empirical improvement to Mathur and Gishler correlation, developed for mixed particles by statistical fitting of data ______(Mathur and Epstein, 1974, Littman et al., 1977). At the point at which spouting velocity

reaches fluidization velocity, some workers consider the regime to be that of the jet

fluidized bed (Filla et al., 1983).

Other Design considerations

Mathur and Epstein (1974) suggest that in addition to estimating the minimum spouting velocity and maximum spoutable bed height for a given particle and sizing the main column and gas supply, the following additional features be incorporated into a spouted bed design:

• Allow 10-12 pipe diameters of straight pipe before the gas enters the bed to

smooth flow instabilities.

• Design the gas inlet so that it can be made larger or smaller as needed.

• Provide a coarse screen over the inlet to prevent particles from entering the inlet

when the gas flow is stopped.

• Provide a spout deflector to minimize carry over of particles in exit gas.

• Consider placing a cyclone at the gas exit to collect any particles which do carry

over.

Attrition of particles may be a concern in scale up. Laboratory scale tests can give insight into whether this will be a problem. At any rate, the most important parameter affecting attrition is the orifice velocity, IL, so that if attrition is anticipated as a problem, efforts should be made to maximize the size of the inlet diameter (Mathur and Epstein,

1974).

93 Spouting stability is affected by the ratio of the inlet diameter to the column diameter; increased orifice diameter for a given column diameter results in reduced Hm.

Ratios of as little as 0.1 for sand of dp = 0.6 mm to 0.35 for wheat have been suggested

(Mathur and Epstein, 1974).

Cone angle is important not only for its effect on Ums, but also for its effect on solids flow. Flat bases or shallow cone angles result in dead zone with little particle movement; excessively steep cones cause instability because the entire bed tends to be lifted. Frictional characteristics of the particles to be spouted also affect the choice of cone angle (Mathur and Epstein, 1974).

Inlet design affects spout stability. Improved results have been achieved with inlet designs that work to reduce deflection of the air stream before it enters the particle bed. Intrusion of the inlet tube up into the particle bed, and converging nozzles have all been successful in this regard (Mathur and Epstein, 1974).

The existing correlations have been developed primarily for columns of diameters less than 30 cm; as a result, scale-up to production size must proceed with caution. A recent study proposed a number of dimensionless scale-up factors for use in this effort, similar to the Glicksman scaling parameters suggested for fluidized beds; however, it is cautioned in the same study that particle characteristics such as sphericity , particle stickiness, and surface friction coefficients play a major role in scale-up and are not included in the suggested factors (He et al., 1997). The factors suggested are:

94 £ i _ E l. IL £s.

and the dimensionless particle size distribution, and dimensionless bed geometry, where

g is the accleration due to gravity, dp is the particle diameter, U is the superficial fluid

velocity, p is density, with subscripts f and p pertaining to fluid and particle, respectively,

H is bed height, Dc is column diameter, |i is the fluid viscosity, eo is the loose packed

voidage, and <|) is the particle sphericity. These factors were used successfully to scale down from a 0.914 m to a 0.152 m column; however, the investigators were free to alter particle properties to suit scaling efforts. As is the case in fluidized bed catalytic reactors, where the particle size and density is predetermined, SSF spouted beds may have an overconstrained problem preventing the complete application of these factors.

Modifications to spouted beds

Draft tubes are occasionally inserted into spouted beds. The tube may be solid, slotted, or constructed of wire mesh. Regardless of construction, the insertion of a draft tube reduces the minimum spouting velocity and increases the maximum spoutable height. Segregation of the annulus from the spout is increased, solids flow pattern is more uniform, residence time distributions for the gas are narrowed, and operation is more flexible (Bridgwater, 1985; Viswanathan, 1986; Mathur and Epstein, 1974). The slotted tube design has the characteristics of better solids-gas contact than the solid tube design, which totally prevents solid entrainment in the spout, and better solids mixing than the screen, which allows gas to pass to the annulus but does not allow solids to become entrained in the spout (Viswanathan, 1986). Design of the draft tube and air inlet

95 strongly affect the gas distribution and solids circulation rate for draft tube spouted bed

reactors (Yang and Keaims, 1983).

Continuous operation is possible in a spouted bed. Draft tube reactor are

particularly well suited to such an arrangement, in that they can be arranged so that

particles are conveyed from cell to cell in a multi-cell reactor or to the exit by draft tubes

(Claflin et al., 1986).

It may be necessary to provide a spout deflector above the fountain region of the

spouted bed to prevent entrainment of the solid particles in the exit gas (Viswanathan et

al., 1986; Mathur and Epstein, 1974). Such a deflector also induces symmetrical

distribution of the particles from the fountain onto the annular region (Mathur and

Epstein, 1974).

Multiple spout beds are possible and may be desirable when the column diameter is too large for a single spout to be sufficient (Mathur and Epstein, 1974.) These may be rectangular or circular in cross-section, and may have vertical baffles between the spouts to improve stability. Multiple spout beds provide more vigorous agitation than single spout beds, whether matched for bed volume or volumetric air flow rate. Gas residence time is shorter in a multiple spout bed. This would make a multiple spout bed more suitable for an application such as drying a heat-sensitive material where agitation would prevent hot spots from occurring; however for an application such as coating, the single spout with slower solids cycling time would allow more time for drying between coats.

Spouted fluidized beds are also possible (Mathur and Epstein, 1974), in which in addition to the gas flow which causes the spout to form, gas is provided around the spout

96 to fluidize the annular region. These hybrid beds have improved solids circulation

compared to fluidized beds and improved mass and heat transfer compared to spouted

beds. This hybrid reactor is especially useful for sticky solids (Sutanto et al., 1985).

An internally circulating fluidized bed, a modified draft tube reactor in which the

tube is affixed directly to the inlet and openings in the wall of the tube at its base allow

entrainment of solids, has been designed especially for high temperature applications

(Milne et al., 1992). As there have not been as many high temperature applications as

there have low temperature ones, this is a welcome addition to spouted bed technology.

Applicability to SSF

Potential

No work has been found in the literature which uses a spouted bed for SSF, though several reports of gas/solid fluidized bed cultivation have been reported. Particles which can not be fluidized because they are too large can often be spouted. Somewhat finer particles that can be fluidized, but only at high gas velocities, can be spouted at lower gas velocities (Kunii and Levenspiel, 1991). Therefore, the use of spouting rather than fluidization may have the advantage of being applicable to larger substrate particles

(such as grains or bran flakes) without requiring grinding and may require less energy for air compression during the fermentation. If the advantages of fluidized bed fermentations cited before hold for spouted bed reactor solid state fermentations, the spouted bed bioreactor (SBB) will be an exciting advance in SSF technology.

A spouted bed bioreactor could provide many advantages in SSF. Both heat and mass transfer (aeration) would be improved over tray reactors, and possible over packed

97 bed reactors. As has been demonstrated for packed bed (Gumbra-Sa'id, et al. 1992;

Bauer, 1986), a spouted bed reactor would allow drying of product m situ, but would have the advantage of preventing mycelial caking. Handling of the solid particles, which would remain separate from each other throughout the fermentation rather than forming mycelial mats as in packed bed fermenters, would be facilitated by SBB-SSF. Control of moisture content, as has been shown in a FBR-SSF (Matsuno, et al., 1993), and also of pH by misting the bed surface with water or an acid or alkali solution would be possible, compared to tray and packed bed reactors where control of such variables is impossible.

Addition of supplemental nutrients or precursors in a controlled manner is likewise possible by misting the bed surface with nutrient solution (Barrios-Gonzalez et al., 1993;

Bauer, 1986), in a manner similar to spouted bed coating of pharmaceutical tablets

(Mathur and Epstein, 1974). Temperature control would be facilitated by the increased heat transfer and oxygen transport should also improve because of the increased mass transfer seen in fluidized and spouted beds (Kunii and Levenspiel, 1991).

As shown by Auria et al. (1992), the growth of biomass in the void space of packed beds is responsible for much of the decrease in effective diffusion coefficients observed in packed beds and is therefore much of the cause of the formation of gaseous concentration gradients. Furthermore, Laukevics et al. (1985) suggested that steric hindrance in packed beds prevents continued growth of biomass. A spouted bed reactor would solve both of these problems. Because the particles in a spouted bed are well mixed and are not packed together, the effective diffusion coefficients will be much higher and steric hindrance to growth will not be a factor. Of course, diffusion through

98 the bulk gas will no longer be a concern at all because of the overwhelming effect from

convective mass transfer in the bulk.

Feasibilitv

It is difficult to predict what effect spouting would have on the growth of the

organism. Drum reactors shear mycelia, which is a disadvantage to coenocytic genera,

esp. Rhizopus spp. (Ramana Murthy et al., 1993), so one might expect that the agitation

present in a spouted bed would damage mycelia, also; however, because the pattern of

agitation in a spouted bed is much closer to one of constant motion than in a slowly

rotated drum, the mycelia are unlikely to form matted networks between individual

particles. It is possible that much of the damage observed in drum reactors arises when these mats are tom apart. The pattern of mycelial growth may be dramatically different in a spouted bed, perhaps with more of the mycelia directed into a substrate particle rather than through the void space and into a neighboring particle. The agitation may cause short mycelial nets, thereby reducing the age distribution of the population and decreasing the diversity in physiological stages (Barrios-Gonzalez et al., 1993). This would be an advantage in modeling. That mycelial organisms have been successfully been grown in fluidized bed reactors (Kunii and Levenspiel, 1991) leads one to believe that culture in spouted beds may also be successful. Yeasts have also been successfully grown in fluidized beds (Rottenbacher et al., 1987; Moebus and Teuber, 1982).

Agitation sensitivity has been observed in SmF; perhaps the presence of a solid substrate would provide some protection from shear. Agitation and shear can reduce productivity in solid substrate SmF (Moo-Young et al., 1983), but it is not obvious what

99 the effects of spouting will be in SSF. At any rate, susceptibility to shear varies with

species, so various fungi would have to be investigated. Shear sensitivity has been shown

in antibody production by CEL-2 hybridoma cells, plant cell biomass production, and

production of fiingi for SCP (Moo-Young and Chisti, 1988). Yeasts and bacteria are

unlikely to be affected by shear so they may be most suitable to taking advantage of the

potential improvements in heat and mass transfer in a spouted bed SSF reactor. Agitation

was not found to affect penicillin production in SSC when moisture loss was

compensated (Barrios-Gonzalez et al., 1993). In a packed bed SSF study of Rhizopus

oligosporus on sago starch where low substrate packings combined with high air flow

resulted in some particle vibration, productivity was decreased, though this was not

clearly linked to the agitation (Gumbira-Sald et al., 1992). This could determine whether or not a given organism is suited to agitated SSF, whether in a spouted bed or otherwise.

That spouted bed fermentation is feasible is hinted at by successful SSC of rice in which the kernels were dry enough to swirl vigorously past each other and production of aflatoxin was greatly increased (Hesseltine, 1972). This appeared to be related to mass transfer improvements. Until SSF is actually attempted in a spouted bed reactor, however, the question of its feasibility is not totally answered.

100 Chromatography Substrate Inoculum

Solid State Extraction Centrifuge Fermenter Vessel

1 - 4 days

Purified Enzyme

Figure 2.1. Potential enzyme production process.

101 Oxygen Carbon dioxide

Biomass ' Enzyme

Eimmatic hydrolysis-*' ose Substrate

Figure 2.2. Processes occurring in SSF.

102 CO; Bioreaction zone of thickness dr ^

oxygen

Figure 2.3. Schematic of diffusion in mycelial pellet.

103 Air Flow Out Air Flow Out

•Bed Height

Air Flow In Air Flow In

Figure 2.4. Circulation patterns in a spouted bed; a) gas; b) particles.

104 Included Cone Angle

Measured Cone Angle

Figure 2.5. Schematic diagram of a spouted bed reactor.

105 CHAPTERS

PRODUCTION OF AMYLASE BY ASPERGILLUS ORYZAE IN STATIC SOLID

STATE FERMENTATION

ABSTRACT

Advantages of solid state fermentation (SSF) over submerged fermentation (SmF), including the increase in product concentration (because of the absence of large volumes of diluting water), the simplicity of media, and the similarity of the culture system to the natural environment of the organism, have encouraged a recent surge in research activity in the field. Much of the research focuses on overcoming the few, but important, disadvantages associated with SSF, such as the inherent difficulty in measuring process variables, the mass and heat transfer difficulties found in existing SSF processes, and the lack kinetic and design data for SSF. The study described here adds to the kinetic data available for an industrially significant SSF process, the production of a-, P-, and glucoamylases by fungi using rice as a substrate. Optimal process conditions, alternative grain substrates, and nutrient supplementation of substrate were studied.

Of the fungal species studied, Aspergillus oryzae was found to produce the highest enzyme levels with the best productivity. Within the range studied, inoculum size did not

106 affect the results. Decreasing the pH from 6.2 (that of untreated rice) to 3.3 had a

progressively deleterious effect on the fermentation. As initial moisture content increased

from 29% to 49% and enzyme production decreased, as did substrate consumption rates.

Maximum productivity was achieved at 30°C, but the highest concentration was achieved at

a fermentation temperature of 37°C. With the addition of 0.1% yeast extract, extracellular

protein production was increased by over 50%; with 0.5% yeast extract, protein production

was increased over 100%. Studies on the effects of substrate bed depth on protein

productivity indicated severer negative effects of mass transfer limitations; for substrate

over a certain depth, little or no protein was produced. It was postulated that if a portion of

the substrate bed became anaerobic, resulting in production of various organic acids and

alcohols, the presence of these fermentation endproducts might have caused a cessation in

protein production. In a study that used lentils, wheat, oats, and barley as substrate,

cultivation on lentils resulted in a maximum productivity of almost twice that of the next

best substrate, wheat.

INTRODUCTION

The interest in solid state fermentation (SSF) that developed in the early part of this century in the West (Takamine, 1914) was eclipsed by the enthusiasm engendered for submerged fermentation (SmF) with the advent of successful penicillin production in stirred tank reactors around the time of World War II. Recent years, however, have seen a return of interest as the advantages of SSF over SmF become evident. Chief among these advantages are the increase in product concentration (because of the absence of large

107 volumes of diluting water), resulting in lower product recovery and liquid waste treatment

costs; the simplicity of media, allowing the use of plant biomass, particularly waste streams

from agri-industry; and the similarity of the culture system to the natural environment of the

organism, which often allows production of metabolites that do not form or form more

slowly in SmF than in SSF (Ramana Murthy, 1993; Ramesh and Lonsane, 1990; Mudgett,

1986; Moo-Young et al., 1983; Hesseltine, 1972). The disadvantages associated with SSF

are generally associated with the inherent difficulty in measuring process variables (biomass

growth, pH, temperature, substrate and product concentrations) in a heterogeneous mass of

moist solids, with the mass and heat transfer difficulties found in existing SSF processes,

and with the lack of research on the processes, including kinetic and design research, which is a direct result of these difficulties. Overcoming these difficulties may allow SSF to become the basis for a renewable source of many valuable products.

Various classes of products can be produced in SSF, including enzymes, organic acids, and antibiotics. Of these, hydrolytic enzymes hold particular promise for SSF production because they can be produced on plant biomass with little or no supplementation; an amylase-producing organism can simultaneously provide its own substrate from starchy materials while producing the amylase product. Amylases are particularly relevant enzymes to study because of their importance in the food industry, being used for production of high fructose com syrup, as dough conditioners, and for dehazing fruit juices, among other applications (Bigelis, R., 1991). Recent years have seen only a few studies of fermentation kinetics of amylase production in solid state fermentation; these have investigated production of a- and |3-amylase hy Aspergillus oryzae

108 on rice koji in a packed bed type system with forced airflow through the bed (Narahara et

al., 1982) and production of a-amylase \>y Aspergillus niger in static culture (Ghildyal et al.,

1993) and in a packed bed column with forced aeration (Gowthaman et al., 1993). These

studies demonstrate the critical importance of temperature, moisture content, and mass

transfer in the fermentation.

A lack of applications research and design data has prevented Western industry from

taking advantage of the potential of SSF. By virtue of its use of plant biomass as a

substrate, SSF can become the basis of a sustainable system of chemical production from

natural resources, but more fundamental knowledge and practical demonstrations of its

usefulness are needed. In this work detailed fermentation kinetics of a-, P-, and glucoamylase production in static solid state fermentation of rice by A. oryzae were investigated. The effects of initial pH, moisture content, nutrient supplementation, inoculum quantity, substrate bed depth, and temperature were studied. It is hoped that the new data provided in this report will assist in extending the applications of SSF to a wider industrial audience.

MATERIALS AND METHODS

Organisms

Several cultures were selected for use in the SSF studies based on literature examples. Aspergillus oryzae was selected based on its use in packed bed studies of SSF

(Narahara et al., 1982), Aspergillus niger based on its use in packed bed culture (Saucedo-

Castaneda et al., 1996), and Eupenicillium javanicum based on its use in a fluidized bed

109 reactor (Tanaka et al., 1986). The particular strains were chosen based on reports of enzyme

production in the American Type Culture Collection catalog. A. oryzae NRRL 697, A.

niger NRRL 1278, and E. javanicum NRRL 707 were ordered from the United States

Department of Agriculture Midwest Area National Center for Agricultural Utilization

Research (1815 North University Street, Peoria, Illinois 61604). The lyophilized cultures

received were sent to Bill Swoagar, manager of the Ohio State University Culture

Collection in the Department of Microbiology for resuscitation.

A. oryzae grows as a typical filamentous fungus with white mycelia. Aerial mycelia

are cottony, and conidiophores are readily visible. Conidiophores are unpigmented when they first form, turning bright avocado green with maturity. A. niger grows in a similar fashion, except that the conidiophores are black. E. javanicum has white to tan mycelia, and the spore-bearing structures are khaki brown and close to the surface; aerial mycelia are not noted. The spore-bearing structures are large and do not appear to release individual spores as do the conidiophores of the Aspergilli. All three fungi are reported to produce amylases and proteases.

The cultures were maintained by transferring to fresh potato agar slants every several weeks. The transfer was made from a stock culture held in the refrigerator; the resulting slant was the working culture for production of inoculum. Each working culture could be used several times to inoculate slants for inoculum production before it was necessary to generate a new working cultiu-e.

110 Substrate

For culture maintenance, either potato dextrose agar or malt extract agar was used as slants. The potato dextrose agar was a commercial product (Difco Lab, Detroit, Michigan) composed of potato infusion (200 g/L), Bacto brand dextrose (20 g/L), and Bacto brand agar (15 g/L) and was prepared as directed on the package (39 g powdered PDA per liter of distilled water). Malt extract agar was prepared as in the medium formulation given by the

American Type Culture Collection. Malt extract (20.0 g, Difco Lab, Detroit, Michigan), glucose (20.0 g, added prior to sterilization), Bacto brand peptone (1.0 g, Difco Lab,

Detroit, Michigan), and Bacto brand agar (20.0 g, Difco Lab, Detroit, Michigan) were combined with sufficient distilled water to make one liter liquid.

The agars were autoclaved for 15 minutes at 121°C, then approximately 10 to 15 ml of molten agar was poured into sterile culture tubes, filling the tubes slightly more than halfway. The tubes were tilted to one side while the agar cooled, forming an agar slant for culture purposes. Slants were stored at room temperature until use. Shelf life was indefinite, but with time the agar would dry and pull away from the sides of the tube. This could be delayed or prevented by wrapping a strip of laboratory paraffin film around the joint between the culture tube cap and the tube. At the time of inoculation, any slants that exhibited growth of contaminants were discarded.

Brown rice. Le Gourmet brand (Rice Mill, Inc., Stuttgart, Arkansas) was purchased at Crestview Oriental Market, N. High Street, Columbus, Ohio. It was not enriched or fortified with any nutrients. The package composition was reported per 42 g or raw rice as

1 g (2.3% w/w) fat, 32 g (76.1% w/w) total carbohydrate, including 1 g (2.3% w/w) fiber

111 (cellulose) and the remainder starch, 100 mg (0.2% w/w) potassium, and 3 g (7.1% w/w)

protein. This rice was purchased in 20 pound bags on three separate dates, as needed (June

20,1995; June 6,1996; February 2,1997). Moisture content for the three separate lots were

determined by standard procedures to be 10.9%, 9.0%, and 7.45% (w/w), respectively.

Most static fermentations were run using this substrate plus distilled water, with no

supplementation of any kind.

Supplementation experiments used Amberex 1003 AG yeast extract (Red Star

Bioproducts, Juneau, Wisconsin, Lot #C5121). Other grain substrates used used in this

study were Scotch brand Quick Pearled Quaker Barley (Quaker Oats Co., Chicago, Illinois),

Food Club brand Lentils (Topco Associates, Inc., Skokie, Illinois), Bob’s Red Mill brand

Bulgur (a cracked, cooked wheat product. Natural Foods, Inc., Milwaukie, Oregon), and

Bob’s Red Mill brand Steel Cut Oats (Natural Foods, Inc., Milwaukie, Oregon). These were

used unsupplemented. Nutritional analyses as provided on the packages are given in Table

3.1.

Inoculum preparation

Two methods were used to prepare inocula for SSF. Either slant culture or rice culture was used to produce the spores. Initially, slant culture was used and was mostly satisfactory, except that for A. oryzae and E. javanicum it could be difficult to obtain a dense enough growth of spores for easy collection of sufficient spores. For this reason, a switch was made to rice culture late in the studies. Source of culture is indicated in the individual experimental methods.

112 Barley Lentils Bulgur Oats Rice

Sample size (g) 48 32 47 20 42

Fat (g) I 0 1 13 1 (2.1% w/w) (0% w/w) (2.1% w/w) (73% w/w) (2.3% w/w)

Total 37 19 35 13 32 Carbohydrates (g) (77.1% w/w) (59.4% w/w) (74.5% w/w) (65.0% w/w) (76.1% w/w)

Fiber (g) 5 9 11 2 2

(10.4% w/w) (28.1% w/w) (23.4% w/w) (10% w/w) (10% w/w)

Protein (g) 5 8 6 3 3

(10.4% w/w) (25% w/w) (12.8% w/w) (15.0% w/w) (7.1% w/w)

Table 3.1. Nutritional analysis of various grain substrates used in this study. Percentages may add to more than 100% because the fiber content is included in the total carbohydrate as well as being expressed separately.

For slant culture of inoculum, a potato dextrose agar (PDA) slant of the desired organism was grown for 2 to 6 days at 37C, until spomlation was visibly heavy. PDA was always used for Aspergillus spp. and after the first few maintenance transfers, was also used for E. javanicum, though initially malt extract agar was used to maintain E. javanicum. The surface of the slant was washed with 2 ml of sterile Tween 80 solution(TM) (Polysorbate

80 , Lot 22013l g , Emulsion Engineering, Sanford, Florida, 6 drops per 25 ml distilled water). Washing was enhanced by vortexing the tube vigorously. The resulting spore suspension was collected in a sterile flask, and the slant was again washed with 2 ml sterile

Tween 80 solution. The second washing was pooled with the first.

113 A drop of the resulting spore suspension was placed in a Petroff-Hausser counting

chamber (Hausser Scientific Company, Horsham, PA, Catalog number 39(X)). Spores were

counted within the central five-by-five grid, counting five of the large squares, starting in

the upper left and proceeding diagonally to the lower right, as indicated in Figure 3.1a. A

photo micrograph of the spore counting chamber in use is shown in Figure 3.1b. The sum

of these five squares, multiplied by 250,(XX), equaled the spores per milliliter of suspension.

The suspension was then diluted aseptically to give a spore count of 1x10^ spores/ml. One

ml of the spore suspension was used as inoculum per 25 g dry rice prepared as substrate.

For preparation of inoculum from sporulated rice, a small sample (approximately 1 g) of heavily sporulated rice was placed in a sterile flask. Four to ten ml of sterile Tween 80 solution were added to the flask and the contents swirled. The spore suspension was decanted to a clean sterile flask, and spore density was determined and inoculation performed as described for the preparation of inoculum from slant cultures. The sporulated rice was koji collected from previous experiments.

During the time period that no microscope was available for counting spore density, a nonstandard inoculum preparation procedure was followed. As before, a potato dextrose agar slant of the sporulated organism was washed twice with 2 ml of sterile Tween 80 solution. The washings were pooled in a sterile flask and 25 ml of sterile distilled water were added. This spore suspension was used without further treatment.

Fermentation

The basic procedure for static fermentation began with preparation of the substrate.

Twenty-five grams (±0.1 g) of raw rice were measured into a 250 ml Erlenmeyer flask. The

114 appropriate amount of distilled water was added to produce the desired moisture content in the rice, a silicone sponge closure (Catalog number C0921, Sigma-Aldrich, Milwaukee,

Wisconsin) was placed on top, and the flask was autoclaved for 20 minutes at 12rC.

Because autoclaving changed the moisture content of the rice from that calculated based on dry rice and water, moisture content had to be measured after autoclaving. The post- autoclaving result is reported in the results section. After autoclaving, the rice mass was loosened by rapping the flask sharply against a padded surface (a thick cellulose sponge made an appropriate cushion) several times. The rice was then allowed to cool completely, and when cool, inoculation was performed by aseptically pipetting 1 ml of spore suspension previously prepared over the surface of the rice. The rice was again loosened and mixed by rapping the flask sharply against a padded surface, then the rice was settled in the flask with one sharp jolt to the bottom of the flask, after which the flask was placed in the appropriate temperature control device. Sufficient flasks were prepared to allow harvest of an entire flask per sample desired. Unless otherwise noted, all fermentations were conducted with rice plus 10 ml water at 37°C without pH adjustment.

For studies of the effect of initial moisture content, the amount of water added to the rice at first was varied. Table 3.2 gives the experimental treatments for the studies of moisture content.

115 Nominal Moisture Water added per 25 g Post-Autoclaving Content Desired raw rice Moisture Content 32% 8 29% 39% 12 39% 48% 18 46% 50% 20 49%

Table 3.2. Water and rice quantities used to prepare substrates of varying initial moisture content.

For studies of the effect of inoculum size, inoculum was prepared at roughly 10 and

1/10 times the density of the standard inoculum. The standard inoculum was used, as well.

Because of an error in preparation, the actual density used was not lxlO \ 1x10^, and 1x10^

spores/ml, but rather 1.45xlO\ 1.45x10^, and 1x10^ spores/ml. Otherwise, fermentation

proceeded as usual, with initial moisture content of approximately 41%, temperature of

37°C, and no pH adjustment.

For the study of the effect of initial pH on growth, various amounts of 1 N

hydrochloric acid (prepared from Fisher brand Certified A.C.S. reagent grade hydrochloric

acid, Fisher Scientific, Fair Lawn, New Jersey) were added to the distilled water used to

prepare the substrate, such that the total volume of liquid added to the rice was 15 ml per 25

g raw rice. Acid volumes used for the various treatments were 0,1,5,10, and 15 ml. The resulting pH for the autoclaved rice was 6.2,4.7,4.0, and 3.3. Initial moisture content was approximately 40% (w/w), and incubation temperature was 37°C.

116 Temperature was studied using rice prepared with 10 ml distilled water added per

25 g rice and with nonstandard inoculum preparation, resulting in an initial moisture content

of 32.4% . No pH adjustment was made. For one run, the flasks were placed in the static

incubator at 37°C, in an oven set at 45-50°C, and at room temperature, which ranged from

20-25“C. For the second temperature run, the static incubator at 35°C, the incubator-shaker

set at 30°C (flasks were affixed to the sides of the chamber with duct tape; they were not

shaken), and a water bath set at 40°C were used for incubating the flasks.

Nutrient supplementation was studied by adding 0,0.1%, or 0.5% (w/w based on

total initial koji weight of 35 g) yeast extract to flasks prepared with 25 g raw rice plus 10

ml distilled water, resulting in an intial moisture content of 33% (w/w). Nonstandard

inoculum was used. Incubation temperature was 37°C and no pH adjustment was made.

Substrate bed depth studies were performed by varying the amount of rice substrate

charged to the vessels. The amount of water and inoculum were varied proportionately.

The studies were performed at first using straight-sided beakers (600 ml) instead of

Erlenmeyer flasks. The last two bed depth studies returned to 250 ml Erlenmeyer flasks for

fermentation vessels. The treatments are indicated in Table 3.3 below. All inocula were

standard 1x10® spores/ml suspensions prepared from sporulated rice from a previous experiment. Incubation temperature was 37°C and no pH adjustment was made. Initial moisture content was approximately 40% (w/w).

Alternate substrate studies were performed using 25 g of the various substrates plus

10 ml distilled water, autoclaved at 121°C for 20 minutes. Nonstandard inoculum prepared

117 from sporulated rice was used. Incubation temperature was 37°C and no pH adjustment was

made.

Experiment Mass of Raw Rice Water added Inoculum size Depth (g) (ml) (ml) (cm) 1 25 14 1.0 1.5 75 42 3.0 3.5 125 70 5.0 5.5 175 98 7.0 7.5 2 25 14 1.0 2.25 50 28 2.0 3.75 75 42 3.0 5.25 3 20 11.2 0.8 2.0 25 14 1.0 2.25 30 16.8 1.2 2.5 35 19.6 1.4 2.75

Table 3.3. Experimental treatments for study of the effect of substrate bed depth on amylase fermentation.

Analytical techniques

Numerous analyses were run on fermentation samples. Appendix A provides a general flow chart of the analytical process to which each sample was subjected. Details of procedures are given here.

118 Dry weights

Dry weights were determined by weighing a wet sample in a tared aluminum pan, drying it in an oven at 105°C overnight (at least 18 hours), and reweighing it. Moisture content was calculated as the difference in wet and dry sample masses divided by the weight of the wet sample.

Glucose analysis

Glucose was analyzed using a YSI Model 2000 glucose and L-lactic acid analyzer

(Yellow Springs Instrument Co., Inc., Yellow Springs, Ohio). This is an analytical instrument that is not affected by turbidity or pH, among other parameters, is accurate to within 0.04 g/L for a glucose range of 0 to 20.0 g/L.

To measure the glucose, the centrifuged, room temperature sample (minimum 0.6 ml) was presented to the sample tube of the instrument, the sample cycle was run, and the readout of glucose concentration obtained either from the LCD readout or from the printout that accompanied each sample. Details of running the machine, including buffer and standard preparation, can be found in the operations manual for the instrument.

Starch analysis

Starch determination was performed by hydrolyzing all residual starch with hydrochloric acid then analyzing the resulting glucose concentration. Approximately one gram of koji was weighed into a 250 ml Erlenmeyer flask; the flask was then washed into a blender jar with 80 ml of distilled water. The sample was blended in an Osterizer 14 speed blender at the highest available speed for one minute, then was returned to the flask. An additional 20 ml of distilled water was used to rinse the blender jar, and the washings were

119 added to the flask. A one ml sample was pulled from the flask for determination of the

initial glucose content.

Ten ml of 37% Fisherbrand hydrochloric acid (Fisher Scientific Co., Fair Lawn,

New Jersey) was added to each blended sample. The flask was capped loosely with

aluminum foil; a dimple was formed in the foil so that condensate forming on the foil would

flow back into the flask. The flasks were autoclaved for 15 minutes at 121°C. After cooling, the glucose in the hydrolyzed sample was measured. Starch was determined as the final glucose minus the initial glucose, accounting for volume changes. This analysis assumes that the resulting glucose concentration is equivalent to the starting starch concentration; it does not take into account the mass gain during hydrolysis. For the purposes of the current study, this approach is sufficiently accurate.

Enzvme Analvsis

Enzyme extraction

Two methods, shaking and blending, were used to extract the extracellular enzymes from the koji. For extraction by blending, 10.0 ±0.1 g koji was measured into a small blender jar. The weight of the koji used was recorded. One himdred ml of distilled water

(house utility) was added, and the mixture was blended at high speed (Osterizer 14 speed blender) for exactly one minute. The resulting suspension was decanted from the jar into a graduated cylinder to determine the volume of extract; coarse particles were left behind in the blender jar. A sample of the suspension was centrifuged at 5000 rpm for 10 minutes.

The volumes of pellet and supernatant were estimated from the markings on the centrifuge tubes in order to determine the volume of clarified extract that was harvestable from the

120 initial amount of koji. Samples of the extract supernatant were considered to be cmde

extract and were frozen until further analysis. The choice of extraction method is discussed

further in the section on sample handling.

Shake extraction also used a 1:10 ratio of koji to distilled water. Either 1.0 ±0.1 or

10.0 ±0.1 g koji were placed in a 50 ml centrifuge tube or 250 mi flask, respectively, along

with 10.0 or 100.0 ml distilled water, respectively. The weight of the koji used for

extraction was recorded. The tube/flask was placed in an incubator shaker at 30°C (warmth

was not required for the extraction; it was the only shaker easily available) for 2.5 to 3.0

hours. The supernatant was decanted, and samples were stored frozen in glass vials until

further analysis. Extractant volume was assumed equal to the volume of distilled water

used for extraction. This was not quite accurate, but suffices for the comparative purposes

in this study.

Cf-Amylase determination

a-Amylase was determined using a commercial kit for colorimetric determination of

amylase (Kit No. 700, no longer available. Sigma Diagnostics, St. Louis, Missouri). The kit

is based on the Somogyi method of amylase determination (Somogyi, 1960), in which the time required to effect a color change of an enzyme-digested starch solution combined with iodine indicates the amylase activity in the enzyme solution. Starch combined with iodine produces an intense blue color; oligosaccharides combined with iodine produce a red color.

As a starch solution is digested by amylase and samples of it are introduced over time to fresh aliquots of iodine solution, this color change can be followed and the point at which

121 the color changes from blue to reddish-brown can be determined visually. The time to

reach this point is proportional to the enzyme concentration.

Starch substrate (Catalog No. 7(X)-1, no longer available. Sigma Diagnostics, St.

Louis, Missouri, 0.75 g/L in Tris-phosphate buffer with sodium chloride, sodium fluoride,

and preservative) was dispensed aseptically into test tubes in 2.0 ml aliquots. The tubes

were placed in a water bath at 37°C for at least 5 minutes before proceeding to allow

thermal equilibration. Ten small culture tubes were placed in a rack, and 0.25 ml of iodine solution (0.002 N in potassium iodide, 2%, and buffer. Catalog No. 700-2, no longer available. Sigma Diagnostics, Stl. Louis, Missouri) was pipetted into each tube. A 0.5 ml sample of crude extract was pipetted into the starch reagent, the tube mixed thoroughly by vortexing, and the tube replaced in the water bath. At 2 minutes after mixing the enzyme and starch, a 0.25 ml portion of the starch/extract digest was removed and mixed with one tube of iodine (removal several seconds before 2 minutes and mixing at exactly 2 minutes).

This was repeated every 1 to 2 minutes until the endpoint approached, as indicated by significant reduction in the purple color, at which time samples were taken every 15 to 30 seconds. Experience was necessary to become skilled at estimating time to endpoint based on current color.

Enzyme samples were diluted as necessary to obtain in most cases an endpoint between 10 to 20 minutes, which was necessary for the most accurate results. Once an endpoint was reached, amylase activity was determined by the following equation:

18,000 * {dilution factor) Activity {SU IL ) = — ------,

122 where t is the endpoint time in minutes and 18,000 is the Somogyi factor given in the diagnostic kit instructions for 37°C incubation conditions. No standard curve was necessary. Repeated measurements on the same sample indicate that an error of approximately 10% is typical. The Somogyi activity corresponds to enzymatic activity. If there are X SU/L in an enzyme extract, addition of 1 ml of this extract to 2 ml of 1.5% starch solution for a 30 minute incubation at 40°C will produce starch cleavage products with reducing power equivalent to X mg glucose (Somogyi, 1938; Somogyi, 1960). fi-Amylase analysis

P-Amylase activity was defined based on a modification of the official method of the Japanese National Research Institute of Brewing (Tax Administration Agency, Japan)

(Narahara et al., 1984) as:

Activity Xu I L) = ^ ^ 7 ^ T ^ *500, (3.1) {s-n)

for which I is the reducing sugar content in the reaction mixture, m is the reducing sugar content in the enzyme extract, n is the reducing sugar content of the starch substrate, and s is the total glucose in the starch substrate, all in consistent mass units.

This definition of p-amylase activity is based on measuring the increase in reducing ends of a digested starch solution (see below) at a specific time using a variation of the dinitrosalylic acid method (Southgate, 1991; Miller, 1959). A digest of crude enzyme extract and starch solution was prepared by combining 2.0 ml of 1% soluble starch solution,

1.6 ml distilled water, and 0.4 ml of crude enzyme extract in test tubes, and mixing

123 thoroughly. The mixture was incubated at 37°C for 10 minutes. The reaction was

terminated by placing the tubes containing the mixture into an ice bath and immediately

adding 2.0 ml of 1 N sodium hydroxide, and then the reducing sugar content of the mixture

was measured. For use in the glucoamylase assay, a blank digest (distilled water used instead of enzyme extract) was simultaneously run.

The starch substrate solution for the enzyme digestion was prepared by making a slurry of 10.0 g soluble starch (Catalog No. S-9765, Lot 55H1065, Sigma Chemical

Company, St. Louis, Missouri) in approximately 100 ml of distilled water. The slurry was washed into 500 ml of boiling distilled water, making sure to thoroughly rinse the beaker that contained the slurry with additional distilled water, adding the rinsings to the boiling starch solution. The solution was boiled for Î minute, then allowed to cool. After cooling the total volume was brought to one liter.

The dinitrosalycylic acid reagent was prepared by combining 500 ml distilled water

(house utility) and 8.0 g sodium hydroxide (Fisher Scientific Co. Fair Lawn, New Jersey) and mixing until dissolved. To this 5.0 g of 3,5-dinitrosalicylic acid (Catalog No. D-0550,

Lot 66H5023) Sigma Chemical Company, St. Louis, Missouri) and 100.0 g sodium potassium tartrate tetrahydrate (Rochelle salts, catalog number S-6170, Lot 46H5023,

Sigma Chemical Company, St. Louis, Missouri) were added. The solution was stored in a glass bottle wrapped in foil for at least two days to allow solids to dissolve completely.

Long term storage was in foil-wrapped glass at room temperature.

Glucose standard solution was prepared by preparing a 4 mg/L glucose solution (0.4 mg of dried glucose dissolved in 100 ml of solvent in a 100 ml volumetric flask) in 50%

124 saturated benzoic acid solution (approximately 0.5 g/L benzoic acid). This standard

solution was prepared by dissolving 0.4 mg of oven-dried glucose (85°C overnight) in 100

ml of benzic acid solution in a 100 ml volumetric flask. The glucose standard was stored at

room temperature. Dilutions with distilled water were prepared to allow testing a series of

0.0,0.5,1.0,1.5 and 2.0 mg/ml glucose along with test samples. It was important to run the

glucose standards and test samples simultaneously, because the results of the test were

sensitive to specific time and temperature of reaction.

Samples of the enzyme digest were analyzed for reducing sugar content by mixing

1.0 ml of sample, 0.5 ml of 0.5 mg/ml glucose standard (to replace glucose consumed in

reaction (Miller, 1959)), 0.5 ml distilled water, and 2.0 ml of dinitrosalicylic acid reagent.

The mixed samples were placed immediately in a boiling water bath for 10 minutes, then

were cooled to room temperature. After cooling, 20.0 ml of distilled water were added to

each mixture and the absorbance of light was read in a Bausch and Lomb Spectronic 70

spectrophotometer at wavelength of 530 nm. The reducing sugar content was then

determined by comparing the absorbance to a sample curve prepared from the standards that were run simultaneously. A sample standard curve for this procedure is shown in Figure

3.2. Using the values determined here, reducing sugar in the enzyme digest (/ in the activity equation) was determined by multiplying the measured reducing sugar concentration in mg/ml by the volume of the digest (2.0 ml), resulting in mg of reducing sugar in the digest.

In addition to the reducing sugar analysis of the enzyme digests, reducing sugar was measured for each enzyme extract and the value of total reducing sugar in the extract, m, was found. Simultaneously, a sample of the starch substrate was analyzed for reducing

125 sugar content, and from this measurement, n, the total reducing sugar in the starch substrate

was found. Total glucose in the starch solution, s, was measured by adding 1.0 ml of 37%

hydrochloric acid (Fisher Scientific Company, Fair Lawn, New Jersey) to 10.0 ml of the

starch substrate, autoclaving for 20 minutes, then measuring the glucose content with the

YSI glucose analyzer.

Glucoamylase activity

Glucoamylase activity was determined using the enzyme digests prepared for the

determination of P-amylase activity. Glucose concentration for each enzyme extract and

digest sample, including the blank digest, was determined using the YSI glucose analyzer.

One unit of glucoamylase activity was defined as the amount of glucoamylase that will catalyze the formation of IX) g of glucose per 10.0 minutes under the assay conditions. It was calculated as:

{p — p-Q5*q) Activity {UlD = ^ -----— , (3.2) where o is the glucose concentration in the enzyme digest, p is the glucose concentration in the blank digest, and q is the glucose concentration in the enzyme extract in mg/ml.

Protein analvsis

Extracellular protein was determined using the Bradford method of protein analysis

(Bradford, 1976). Several different Bradford reagents were used. Initially, Bradford reagent as prepared by the Ohio State University Reagent Lab was used. For this reagent, protein was assayed by combining 0.1 ml of sample with 5.0 ml of Bradford reagent and measuring the absorption at 595 nm with a Bausch and Lomb Spectronic 70

126 spectrophotometer. Comparison to a standard curve prepared from bovine serum albumin

(Serva Fine Biochemicals, New York) sample readings allowed calculation of protein

concentration. Later, a Coomassie(TM) Plus Protein Assay Reagent (No. 23236, Pierce

Chemistry, Rockford, IL) was used as per package instructions (0.1 ml sample per 3.0 ml reagent, absorbance readings at 595 tun), again using standards prepared from bovine serum albumin. The final protein assay used a reformulated version of Coomassie(TM) Plus

Protein Assay Reagent, G-250 based with expanded linear range (No. 23236, Pierce

Chemistry, Rockford, EL) was used as per package instructions (0.1 ml sample per 3.0 ml reagent, absorbance readings at 595 nm), but with prepared standard bovine serum albumin,

2.0 mg/ml in 0.9% sodium chloride with sodium azide (No. 23209, Pierce Chemistry,

Rockford, IL). Several of the last analyses were nm using a Spectramax 250 microplate reader (Molecular Devices, Sunnyvale, California) to measure the absorbance. For these runs, 150 microliters of reagent and 20 microliters of sample were used. An examples of a standard curve for protein determination is shown in Figures 3.3 for data obtained using the first protein reagent used.

During preliminary investigations it was noted that enzyme activity correlated fairly well with extracellular protein concentration. Figure 3.4 shows this correlation for a- amylase activity. Figures 3.5 and 3.6 show the correlation between a-amylase activity and that of P-amylase and glucoamylase, respectively. The a-amylase activity correlates well with protein at all levels; the activities of P-amylase and glucoamylase correlate with a- amylase activity for a-amylase activity below approximately 300 SU/g dwt rice. Because

127 of this reasonably close correlation, enzyme analysis was not undertaken for some of the

fermentation studies. Instead, extracellular protein production was used as a basis for

comparison.

Comparison of results between experiments must be done with care in this group of

experiments. The use of different protein reagents and standards resulted in some variance

in the levels of protein measured, as indicated by different slopes in the standard curves.

Only for those experiments analyzed simultaneously (the pair consisting of the first

temperature study and the nutrient supplementation study and pair consisting of the first

substrate bed depth study and the alternative substrate study) can be directly compared. The

variance in protein results is outlined in Appendix B.

Measurement of pH in solid media

A flat surface pH probe (Catalog No. 34105-026, VWR Scientific, West Chester,

Pennsylvania) for solid media was used to monitor the final pH in the koji. The probe was pressed firmly against the moist koji until a stable readout was obtained.

Sample handling study

As this was the first study of solid state fermentation in general and of production of amylase by fungi in SSF in particular in Dr. S.-T. Yang’s research group, it was necessary to determine proper sample handling procedures. Questions to be answered included whether or not there was a difference between enzyme activity for crude extracts produced by the two extraction methods possible, how the extracts should be stored for maximum stability, and how long the storage life of a given sample was.

128 To answer these questions, a series of studies were performed. Six flasks of a two day SSF culture of A. oryzae on brown rice (25.0 g rice plus 14 ml distilled water) were combined and mixed. Portions weighing 10.0 g each were measured out into 50 ml centrifuge tubes which were tightly sealed, and ten were placed in the freezer and eleven were placed in the refrigerator. Three additional flasks of a similar two day SSF culture of

A. oryzae on brown rice were subdivided with 10.0 g placed in blender jar with 100 ml distilled water, 10.0 g placed in 250 ml flask with 100 ml distilled water, and the remainder used for dry weight determination.

Samples were extracted either by blending (blender jars) or shaking (250 ml flasks).

The a-amylase activity was determined for each flask for both extraction methods. This allowed a comparison of extraction methods for amylase yield. The blender samples also provided a time zero sample for comparison with the samples previously stored, and samples of the extracts from both methods were also stored in either the freezer or refrigerator for later comparison with storage in koji form.

Figure 3.7 shows the comparison of two methods of extraction of enzyme, shaking and blending, for a number samples of koji. Most of the sample pairs had no significant difference (within one standard deviation from average) between the two methods; sample pairs 4, 8, and 11 showed higher amylase activity in the extract when shaking was used to extract the enzyme. Either extraction method was used in subsequent studies, depending on what was convenient; the same extraction method was used for all samples within a given study.

129 For long term storage studies, koji samples previously stored in either the refrigerator or the freezer were extracted by blending and analyzed for a-amylase activity.

Samples were pulled at 1,2,34,56, and 94 days. These studies allowed comparison of storage as extract or koji and by frreezing or refrigeration.

For the first 5 weeks of storage, no effect of storage was discernable; however, by 8 weeks activity had dropped by over 50% in most cases, whether the extract was stored frozen or refrigerated or was prepared by shaking or blending. These results are also shown in Figure 3.8, and indicate the importance of testing for enzyme activities within the first few weeks of enzyme harvest. Storage methods, whether by freezing or refrigerating had little or no effect on results.

Fermentation Studies

Organism studv

In a study of differences in amylase production between organisms, 20 flasks of

40% nominal initial moisture content rice were prepared for each organism. Flasks were inoculated with 1.0 ml of spore suspension with density IxlO’ spores/ml of either A. oryzae or A. niger and were incubated statically at 37°C. Samples were collected as follows:

Day 0 -1 flask of each organism

Day 1-3 flasks of each organism

Day 2 -3 flasks of each organism

Day 3 -3 flasks of each organism

Day 4 -3 flasks of each organism

Day 5 -3 flasks of each organism

130 Day 6 -2 flasks of each organism

Day 7 -2 flasks of each organism

The collected koji was divided into three parts for analysis, with 10.0 g used for enzyme

extraction, 10.0 g used for substrate analysis, and the remainder used for dry weight

determination. Analysis included dry weight determination, pH determination, protein

measurement, a-, (3-, and glucoamylase activities, and substrate consumption.

Inoculum quantity studv

Eighteen flasks of 40% target moisture content substrate (25 g rice plus 14 ml

distilled water) were prepared. Six each were inoculated with 1.0 ml per flask of an A.

oryzae spore suspension of density 1.45xlO\ 1.45x10^, or 1.45x10^ spores/ml. The cultures were statically incubated at 37°C. One flask each was pulled at time zero and day 1, and two flasks each were pulled at day 4 and day 8. Standard analytical procedures were run on each sample.

Initial pH studv

Substrates were prepared as described previously. Eight flasks were prepared for each of the initial pH levels, and each flask was inoculated with 1.0 ml of a 1x10^ spores/ml suspension of A. oryzae spores. For each pH treatment, one flask was harvested at time zero and day 1, and 2 flasks were harvested at each of days 2,3,4, and 6. Standard analysis was done on all samples. All treatments were incubated at 37°C.

Initial moisture content

Substrates of various initial moisture content were prepared as described previously, with a total of 14 flasks for each treatment. An A. oryzae spore suspension of density 1x10®

131 spores/ml was used to inoculate each flask with 1.0 ml of suspension. The flasks were

placed in a 37°C static incubator. Single flasks were harvested at time zero and 20 hours,

and two flasks from each treatment were harvested at 24,50.5,72,95,144 and 196 hours.

Standard analytical procedures were followed for each flask.

Temperature effect

The treatments for the temperature study are described above in the section on

fermentation methods. Samples were harvested at time zero and days 1,2,4, and 6.

Standard analyses were performed on the harvested samples, except that enzyme assays

were not performed. Instead, enzyme production was considered to be indicated by

extracellular protein content.

Nutrient supplementation

Treatments were prepared as described in the fermentation section above. Samples

were harvested at day 0,1,2,4, and 6. Standard analyses were performed, except that

substrate consumption and enzyme analyses were not performed. Enzyme production was

assumed to be similar to extracellular protein production.

Substrate bed depth

Three substrate depth studies were performed using increasingly narrow ranges of depths. The treatments are described in the fermentation section above. Samples were harvested at 0, 1,2,4, and 6 days, except for the final study, for which samples were harvested at 0, 38, and 60 hours. Standard analyses, excluding substrate consumption and enzyme activity, were performed on each sample. Enzyme production was considered to be correlated with extracellular protein production.

132 Alternative substrate studv

The substrates and preparation are described in the fermentation section above.

Samples were harvested on days 1,2,4, and 6. Standard analyses, excluding substrate

consumption and enzyme activity, were performed, a-amylase activity was measured for

the day 6 samples.

RESULTS AND DISCUSSION

Organism comparison

Preliminary cultivation of the various species indicated that E. javanicum grew

much more slowly than did the Aspergillus spp. A summary of these results are given in

Table 3.4. Because of the difficulty in growing E. javanicum, it was dropped from further study.

PARAMETER CONSIDERED COMPARATIVE RESULTS Growth on PDA A. niger and A. oryzae » E. javanicum Spomlation on PDA A. niger » A. oryzae » E. javanicum Growth on rice A. niger and A. oryzae »E. javanicum Likelihood of contamination A. niger < A. oryzae « E. javanicum PDA = potato dextrose agar

Table 3.4. Comparative cultivation characteristics of several filamentous fungi.

The two species of Aspergilli used for the organism comparison study, A. oryzae and A. niger, have both previously been studied for production of various amylases in SSF

133 (Ghildyal et al., 1993; Ghildyal et al., 1992; Narahara et al., 1982; Mudgett et al., 1982). A

comparison of the a-amylase production by both using the brown rice substrate under

investigation resulted in much higher enzyme assays for the A. oryzae. Results for a- amylase and protein production for both species are shown in Figure 3.9. Initial a-amylase production was similar for both species, until at arotmd 24 hours production by A. niger halted while that of A. oryzae continued, resulting in final activities over four times greater for the latter organism. Based on these results, which confirmed preliminary studies not reported, it was decided to continue work using only the A. oryzae culture.

Inoculum size

The effect of inoculum size on enzyme production kinetics was investigated using three different inoculum densities, 1.45x10^, 1.45x10^, and 1.45x10^ spores per milliliter of inoculum. Little difference was seen between treatments. Figure 3.10 shows the results for extracellular protein production as an example. In a study of ethanol production by SSF of sweet sorghum by Saccharomyces cerevisiae, little difference was seen in production profiles for inocula sizes of 10® and 10^ cells per gram sorghum and for inocula sizes 10* and 7x10* cells per gram raw sorghum, but there was difference between these two groups of inocula sizes (Kargi et al., 1985). It is likely that there is some point at which a change in inocula density would have an effect for the A. oryzae fermentation considered here, but within the range investigated, no difference was observed.

Complete time course data for each of the treatments is given in Figures 3.11,3.12, and 3.13. These results were typical of koji fermentations. The total starch decreased with time, slowly at first, then at a greater rate after about I day of cultivation. Protein increased

134 in somewhat of an inverse fashion to the substrate decrease. Moisture content decreased

slightly initially, probably as a result of drying in the incubator, then increased over 10%

from day 1 to day 8 as a result of the release of water during the fungal metabolic processes.

There was also a slight decrease initially in pH, followed by a gradual rise, resulting in a

final change of less than half a pH unit from the original value. These results are typical for

SSF production of amylases by A. oryzae grown on rice. Additional information

concerning the concentration of free glucose in the koji is discussed in the section

describing the effects of initial moisture content.

An inspection of the enzyme kinetics shows that all three enzymes were produced

concurrently. This makes sense when one considers the use the organism makes of these extracellular enzymes - they are excreted from the cell with the goal of hydrolyzing the starchy substrate into glucose molecules that can freely diffuse into the cell. Though only P- and glucoamylase are actually necessary to produce glucose from starch, the action of a- amylase produces many more free ends of the starch molecule upon which the former enzymes can work, thereby greatly increasing the substrate available to the organism. The increase in extracellular protein that accompanied the increase in enzyme activity also makes sense, because the enzymes, of course, are protein.

Initial pH

The effect of initial pH has been investigated for several other SSF processes (Ortiz-

Vazquez et al., 1993; Ramesh and Lonsane, 1987) and was found to have a large effect.

Similar results were found here. Decreasing the pH from 6.2 (that of untreated rice) to 3.3 had a progressively deleterious effect on the fermentation. Production of extracellular

135 protein and enzymes and consumption of substrate were all markedly reduced in the lower

pH systems. Results for these fermentations are depicted in Figures 3.14, 3.15, and 3.16.

The pH of the initially reduced pH substrates gradually increased during the fermentation,

perhaps as the result of deamination of proteins in the rice, but the enzyme production did

not improve correspondingly. It is possible that a less severe reduction in initial pH or an

increase in initial pH could have a positive effect on enzyme production.

Initial moisture content

Detailed fermentation kinetics are displayed in Figures 3.17 through 3.20 for the various initial moisture contents. The general patterns were similar to those observed in the inoculum study kinetics described in detail previously. The correspondence of the sudden increase in enzyme activity with the marked change in rate of substrate consumption after day one was more clearly seen in these studies, which included more frequent sampling.

The delay in substrate consumption and appearance of fermentation products is consistent with the lag time necessary for spore germination and initiation of mycelial elongation and rapid growth.

Figure 3.21 shows the concentration of glucose in the crude extract for the various moisture content fermentations. This represents the trends for free glucose in the koji, as well. Beginning at 0 g/L, the glucose concentration rises rapidly. This reflects the production of glucose from starch at a rate higher than the rate at which the organism consumed it. For the lower moisture fermentations, 29% and 39% initial moisture content, for which the greatest growth was observed in this period, the glucose concentration peaked around 2 days into the fermentation, after which a steady decline was observed. The

136 consumption rate had surpassed the production rate, probably as the result of increased

biomass. In the higher moisture content fermentations, 46% and 49% initial moisture

content, the peak in glucose concentration is much higher and comes much later in the

fermentation, a reflection of the slower glucose-consuming growth in these fermentations.

The importance of water content in substrate has been well documented for several

SSF processes (Ramesh and Lonsane, 1990; Ramesh and Lonsane, 1987; Narahara et al.,

1982). The current study confirmed this. Initial moisture contents of 29%, 39%, 46%, and

49% showed dramatic differences in fermentation kinetics, as seen in Figures 3.22 through

3.24. As initial moisture content increased, protein (Figure 3.22a) and enzyme production

(Figures 3.24) decreased, as did substrate consumption rates (Figure 3.22b). The

differences are not as noticeable in the first 24 hours, but by 48 hours the differences

became marked. The production trends for glucoamylase, seen in Figure 3.24c, did not

follow the same trends as for the other amylases. This is likely a result of inaccurate

experimental data caused by the high levels of free glucose already present in the enzyme

extract for the 46% and 49% initial moisture content fermentations, as was shown in Figure

3.21. As is commonly observed in these koji fermentations, moisture content tended to

increase during the fermentation as a result of metabolic water released, though for the

higher initial moisture contents (46% and 49%), the increase is not noticed; this may be the

result of lower growth rates leading to lower rates of water released throught metabolism.

Typical pH kinetics were noted for 29% and 39% initial moisture content

fermentations, as shown in Figure 3.23, with a slight increase in pH noted in the course of the fermentation; however, the pH of the 46% and 49% initial moisture content

137 fermentations was seen to fall during the fermentation. It is possible that the high moisture

content caused sufficient inhibition of gas exchange (see below) so that anaerobic

metabolism was initiated in A. oryzae, resulting in the fermentative production of organic

acids.

Total koji dry weight decreased during the fermentations, with a drop in total dry

weight of 40,52,31, and 33%, based on the initial koji dry weight, for the 29,39,46, and

49% initial moisture content fermentations, respectively. The drop in dry weight is the

result of carbon dioxide evolution during the fermentations.

Moisture content is critical to growth and enzyme production in several ways. A

certain water activity is required for growth; too low of an initial moisture content will

result in no enzyme production. Furthermore, moistening is necessary to soften the grain so that mycelia can penetrate the interior, increasing nutrient availability, and for dissolution of the substrate as the enzyme acts upon it. That the mycelia of A. oryzae penetrate deeply into moist rice has been clearly shown through SEM studies (Mudgett et al., 1982). Increased moisture content may, however, have the effect of decreasing porosity in the rice, thereby reducing mass transfer, in particular oxygen and carbon dioxide exchange. Increased stickiness and the concurrent reduction in gas volume in the substrate mass would have a similar effect. It is likely that in the static fermentations presented in the current study, such effects caused the reduction in enzyme production with increased moisture content.

In a study of A. oryzae growth and enzyme production in a solid state fermentor with provisions for forced air flow through the bed of rice, it was shown that with initial moisture contents in the range of 25 to 40%, total a-amylase and saccharifying (P-amylase)

138 activity increased as initial moisture content increased, though specific activity decreased. It

is difficult to compare these results to those in the current study because of the difference in

fermentation conditions: forced air convection vs. static incubation. The conclusion that moisture content is critical to enzyme production results and must be investigated for any given system under study remains valid.

The data from this experiment was used to investigate protein yields based on total starch. Figure 3.25 shows the starch remaining (excludes other carbohydrates that may have been present) plotted versus protein. The relationship is only roughly linear, but the slope of a straight line fitted to the data gives some measure of the yield. For this experiment, yield was calculated to be -6 mg protein per g starch. Figure 3.26 shows the same exercise performed for data fi'om the experiment in which initial pH was varied. In the second case, the yield was calculated to be -7 mg protein per g starch, showing good reproducibility.

Temperature effects

Temperature is known to have a significant effect on all forms of microbial growth; fungal growth in SSF is no exception. The data for two separate temperature studies are combined in Figure 3.27. For the first two days of static fermentation, the most protein resulted when koji was incubated at 30 to 35°C, but by the end of the experiment, incubation at 37°C yielded the greatest amount of protein. This is in agreement with

Narahara et al. (1982), who found that 38“C gave the highest a-amylase production.

The data for the experiments are summarized in Table 3.5. Maximum productivity was achieved at 30°C, but the highest concentration was achieved at a fermentation temperature

139 of 37°C. This indicates that spore germination and extended growth are favored at different

conditions.

Fermentation Maximum Maximum Protein Temperature Productivity Concentration (°C) (mg/g dwt rice/d) (mg/g dwt rice) 25 0.27 1.94 30 0.72 1.85 35 0.70 2.10 37 0.51 2.94 40 0.23 1.20 45-50 0.00 0.31

Table 3.5. Effects of fermentation temperature on maximum protein productivity and maximum protein concentration.

Nutrient supplementation effects

Research on supplementation of rice for koji fermentation is sparse. It has been

investigated in the solid state fermentation process for biopulping, in which case nutrient

supplementation of wood chips with glucose and glutamic acid did not improve

fermentation results (Wall et al., 1993). In this study, supplementation with yeast extract, a

source of growth factors and nitrogen, was briefly investigated. The results of this investigation are shown in Figure 3.28. With the addition of 0.1% (w/w, based on total initial koji) yeast extract, extracellular protein production was increased by over 50%; with

0.5% yeast extract, protein production was increased over 100%. This dramatic increase in

140 protein production should be further investigated, particularly with low cost supplements such as com steep liquor being considered. These results are also shown in Table 3.6, which quantifies the maximum protein productivity and maximum protein concentration achieved in these ferementations. There is likely an optimum level of nutrient supplementation occurring somewhat higher than 0.5% yeast extract addition.

Yeast Extract Maximum Productivity Maximum Protein Concentration Supplementation (mg/g dwt rice/d) (mg/g dwt rice) no supplement 0.52 2.94 0.1% w/w 0.92 4.44 0.5% w/w 1.42 6.47

Table 3.6. Effects of nutrient supplementation on maximum protein productivity and maximum protein concentration.

Effect of substrate bed depth

Several studies of the effect of substrate bed depth were conducted to investigate the possible impact of mass and heat transfer limitations in a small scale static fermentation.

The first two studies looked at a wide range of depths, from 1.5 to 7.5 cm. Only the two most shallow depths, 1.5 and 2.25 cm, showed significant protein production. These results are shown in Figure 3.29. The final study looked at a narrower range, 2.0 to 2.75 cm. Results for this experiment are shown in Figure 3.30. Protein was produced at all of these depths, but a dramatic difference in protein production was still seen with only small increases in substrate depth.

141 Table 3.7 gives the results in terms of maximum productivity and maximum protein

concentration achieved. A clear relationship between depth, productivity, and protein

production achievable was seen. The table combines data from all three studies; for the

final study, the fermentation was not allowed to proceed to completion, so the maximum

protein content achieved data must be compared with care.

Depth Maximum productivity Maximum Protein (cm) (mg/mg dwt rice/d) (mg/mg dwt rice) 1.5 0.640 2.559 2 0.278 0.696* 2.25 0.249 1.222 2.5 0.177 0.281* 2.75 0.157 0.313* 3.5 0.156 0.179 3.75 0.041 0.083 5.5 0.128 0.128 7.5 0.174 0.174 ♦fermentation stopped before completion

Table 3.7. Effects of substrate bed depth on maximum protein productivity and maximum protein concentration.

The negative effects of mass transfer limitations were obvious and expected in this

study. What was surprising was the extent of the effects; for substrate over a certain depth, little or no protein was produced. This was particularly noticeable in the first two depth studies. The substrate from the deeper treatments had a readily noted fruity, alcoholic odor to them. It might be that when the depth was sufficient to allow a region of anaerobiosis.

142 the fungus switched metabolic pathways from the TCA cycle to fermentative balancing of

redox levels within the cell.

Qualitative HPLC analysis (methods in Yang et al., 1992) of one of the extracts

from a deep substrate fermentation indicate the presence of organic acids and ethanol, both

indicative of fermentative metabolism, possibly as a result of low oxygen levels. The

chromatogram for this test is shown in Figure 3.31.

It was surprising that so little protein was formed in the deeper treatments, if oxygen

limitation was the only problem. At least the upper portions of the substrate should have had enough oxygen to prevent anaerobic conditions. Perhaps the presence of ethanol or other inhibitory substances resulting from the fermentative metabolism shut down enzyme production even in the upper layers. Further investigation of this phenomenon is suggested.

Alternative substrates

Several unusual substrates were investigated briefly for amylase production by A. oryzae. Of these, wheat, oats, and barley produced good results, similar to rice, but the unexpected superior substrate was lentils, with a production level approximately twice that of the other substrates. Kinetics of the fermentations are shown in Figure 3.32; corresponding a-amylase activities for the crude extracts resulting from the final sample are also noted on the figure. Maximum productivity and maximum achievable protein concentration results are summarized in Table 3.8 for this study. Cultivation on lentils resulted in a maximum productivity of almost twice that of the next best substrate, wheat.

The growth pattern of the fungi on the lentils was different from that on the other grains used in this experiment and from that on rice, which was used in all other

143 experiments. Instead of covering the surface of the grain with a dense, cottony mycelia, the

fungus grew mostly under the seed coats of the lentils; as a result, the individual seeds

remained loose and did not bind together as the experiment progressed. Such a growth

pattern could be advantageous in any fermentation process, such as a spouted bed

bioreactor, where mixing of the substrate would result in abrasions to surface mycelia. If

the fungus remained below the seed coat, it would be predicted from damage caused by

surface shear.

As a high protein, but lower starch (25% and 31.25%, respectively) substrate,

relative to rice (7.1% and 74%, respectively), one might not have expected the superior

production of a starch hydrolyzing enzyme. It is likely that the increased availability of

protein promoted growth and increased enzyme production in this manner. The relative expense of the two substrates must be considered in choosing the optimal substrate; a combination of the two may turn out to be best, considering cost and results.

Substrate Maximum Maximum Protein Productivity Concentration (mg/g dwt (mg/g dwt substrate) substrate/d) Lentils 1.03 4.82 Wheat 0.66 2.67 Oats 0.52 2.58 Barley 0.69 1.78

Table 3.8. Effects of use of altemative substrates on maximum productivity and maximum protein concentration.

144 Effects of environmental parameters on sporulation and enzyme production

Besides examining amylase production during the above fermentations, it is of interest to observe the effect of the various environmental parameters on fungal growth and spomlation. Spomlation is generally assumed to be associated with nutrient-limitation or other environmental conditions, such as temperature, that can influence metabolism and trigger differentiation. Temperature has a dramatic effect on spomlation as well, inducing or inhibiting the transition or affecting the mophology of spomlation (Anderson, 1978).

Though the two states, vegetative growth and differentiation to spore producing stmctures, are not totally incompatible, it is reasonable to see them as competing for resources present as metabolic intermediates (Smith, 1978). Because of this, spomlation may be undesirable for a given solid state fermentation. For example, if the desired product is produced only during vegetative growth, yield will be reduced by any resources shifted to spore formation.

Observations made during the course of the various fermentations are summarized in Tables

3.9 through 3.11. Visually observed growth and spomlation were heaviest and earliest in the lower moisture content fermentations, as shown in Table 3.9. Growth was apparent after one day for both the 29% and 39% initial moisture content (IMC), but was not apparent until two days post-inoculation for the 46% and 49% IMC. Spomlation appeared at 72 hours for the 29% initial moisture content, but was delayed until 144 hours for 49%

IMC. Extracellular protein production was substantially slowed or complete at the point at which moderate spomlation was observed.

145 Time (h) 29% IMC 39% IMC 46%IMC 49% IMC

24 + - - -

50.5 + + + + + +

72 + + + + + + + + *

95 + + + + + + + + + + + + **

144 r ! t r 't 1 ri" T T T T *** * *

192 t r r 1 *** **** *

Table 3.9. Effect of initial moisture content (IMC) on spomlation and growth. + = mycelial patches evident, ++ = light confluent growth, +++ = medium mycelial growth, +-H-+ = heavy (cottony) mycelial growth, * = first evidence of spomlation (unpigmented conidiophores), ** = pigmented spores readily visible, *** = dense spomlation, **** = spomlation to the point that rice is not visible.

In contrast to generally held beliefs that spomlation is often induced by nutrient limitations, the study on the effects of supplementation indicated that additional nutrient actually stimulated spomlation. The results are presented in Table 3.10. Growth was heaviest at any given time in the most highly supplemented treatments (0.5% yeast extract). Spomlation occurred first, at 46 hours, in both supplemented treatments (0.1% and

0.5% yeast extract), but was not noted at that time in unsupplemented fermentations. At the end of the fermentation, 136 hours, the heaviest spomlation was noted on the 0.1% nutrient supplementation sample, followed by the 0.5% yeast extract supplemented treatments, and then by the unsupplemented fermentation. Appearance of significant spomlation again

146 correlated with the completion of extracellular protein production, which also corresponds

to a substantial depletion of available starch.

The effect of fermentation temperature on spomlation was even more dramatic,

tabulated in Table 3.11. Spomlation occurred earliest, at 46 hours, in the lowest

temperature treatment, room temperature of about 21 °C. All of the remaining treatments

showed spomlation at the next sample time, 93 hours, with the heaviest spomlation being

evident on the 30°C samples; however, by 136 hours, the room temperature sample had a

dramatic increase in spomlation and was by far the most densely spomlated treatment (it

also exceeded the spomlation in the concurrently mn nutrient supplementation study).

Growth followed a different pattern in this experiment, being evident first in the 30°C and

35°C treatments, at 20 hours, becoming discernible in the other treatments at 46 hours, and remaining the heaviest in the 35°C treatment at the completion of the fermentation, 136 hours. Protein production appeared to level off before significant spomlation occurred in the 30°C, 35°C, and perhaps the 37°C treatments, but the data is insufficient to determine the relationship between protein production and spomlation in the room temperature treatment. There was, however, an increase in protein production after the first signs of spomlation in the room temperature treatment.

From the first two sets of spomlation/protein results, one might conclude that the same trigger that initiates spomlation also turns off extracellular protein production; however, in the latter set of results, it appears that there must be more than one trigger.

Spomlation is not incompatible with protein production in all simations.

147 Time (h) 0% yeast extract 0.1% yeast extract 0.5% yeast extract

20 --- 46 + + + **

93 ++ +++ ++-H- * *** ***

136 1 I++ **** **** ***

Table 3.10. Effect of nutrient supplementation with yeast extract on sporulation and growth. + = mycelial patches evident, ++ = light confluent growth, +++ = medium mycelial growth, ++++ = heavy (cottony) mycelial growth, * = first evidence of sporulation (unpigmented conidiophores), ** = pigmented spores readily visible, *** = dense sporulation, **** = sporulation to the point that rice is not visible.

Time (h) 20°C 30°C 35°C 37°C 40°C 45°C 20 - + + - -- 46 + ++ ++ + + - *

93 -H- ++ +++ ++ ++ - * *** ** * *

136 ++ ++ +++ ++ ++ - **** *** ** ** *

Table 3.11. Effect of temperature on sporulation and growth. + = mycelial patches evident, ++ = light confluent growth, +++ = medium mycelial growth, ++++ = heavy (cottony) mycelial growth, * = first evidence of spomlation (unpigmented conidiophores), ** = pigmented spores readily visible, *** = dense spomlation, **** = spomlation to the point that rice is not visible.

148 CONCLUSIONS

It is clear from these results that process conditions affect SSF results to as significant a

degree as for SmF, but the parameters that must be investigated in optimizing a process can

include factors not accounted for in SmF. In particular, initial moisture content must be

investigated for its effect on enzyme production for a given system. This study

demonstrated a strong decline in productivity in a static fermentation system with increasing

initial moisture content from 29 to 49%, whereas Narahara et al (1982) showed little

difference in total activity for a range of moisture contents from 25 to 40%; the latter experiments were performed using a packed bed reactor with forced air flow through the koji bed. This undoubtedly prevented the mass transfer problems observed in the current studies, as confirmed by the substrate bed depth experiments. Though the packed bed configuration appears to resolve mass transport difficulties to some extent, other difficulties, such as with solids handling and lack of uniformity remain. The development of novel bioreactors that solve these problems as well as the mass transport difficulties is clearly needed.

REFERENCES

Anderson, J. G. 1978. Temperature-induced fungal development. Chapter 18 in The Filamentous Fungi, v. 3: Developmental Mycology. J. E. Smith and D. R. Berry, eds. Edward Arnold, London, pp. 358-375.

Bigelis, R. 1991. Fungal enzymes in food processing. Chapter 14 in Handbook of Applied Mycology, v. 3, Foods and Feeds. D. K. Arora, K. G. Mukerji, and E. H. Marth, eds. Marcel Dekker, Inc., New York. pp. 445-492.

149 Bradford, M. 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein dye-binding. Anal. Biochem. 72:248- 254.

Ghildyal, N.P.; M. Ramakrishna, B. K. Lonsane, N. G. Karanth. 1992. Gaseous concentration gradients in tray type solid state fermentors - effects on yields and productivities. Bioproc. Eng. 8:67-72.

Hesseltine, C. W. 1972. Solid state fermentation. Biotechnol. Bioeng. 14:517-532. Kargi, F.; J. A. Curme; J. J. Sheehan. 1985. Solid-state fermentation of sweet sorghum to ethanol. Biotechnol. Bioeng. 27:34-40.

Miller, G. L. 1959. Use of dinitrosalicylic acid reagent for determination of reducing sugar. Analytical Chem. 31:426-428.

Moo-Young, M.; A. R. Moreira; R. P. Tengerdy. 1983. Principles of solid-substrate fermentation. Chapter 5 in The Filamentaous Fungi, v. 4 Fungal Technology. J. E. Smith, D. R. Berry, and B. Kristiansen, eds. Edward Arnold, London, pp. 117-144.

Mudgett, R. E. 1986. Solid-state fermentations. Chapter 7 in Manual of Industrial Microbiology and Biotechnology. A. L. Demain and N. A. Solomon, eds. American Society for Microbiology, Washington, D C., pp. 66-83.

Mudgett, R. E.; J. Nash; R. Rufher. 1982. Controlled gas environments in solid state fermentations. Dev. Ind. Microbiol. 23:397-405.

Narahara, H., Y. Koyama, T. Yoshida, S. Pichangkura, R. Ueda, H. Taguchi. 1982. Growth and enzyme production in solid-state culture of Aspergillus oryzae. J. Ferment. Technol. 60(4):311-319.

Narahara, H.; Y. Koyama; T. Yoshida; Atthasampunna, P; H. Taguchi. 1984. Control of water content in a solid-state culture of Aspergillus oryzae. J. Ferment. Technol. 62(5):453- 459.

Ramana Murthy, M. V.; N. G. Karanth; K. S. M. S. Raghava Rao. 1993. Biochemical engineering aspects of solid-state fermentation. Adv. Appl. Microbiol. 38:99-147.

Ramesh, M. V. and B. K. Lonsane. 1990. Critical importance of moisture contnent of the medium in a-amylase production by Bacillus licheniformis M27 in a solid-state fermentation system. Appl. Microbiol. Bitoechnol. 33:501-505.

Ramesh, M. V.; B. K. Lonsane. 1987. Solid state fermentation for production of a-amylase by Bacillus megaterium 16M. Biotechnol. Lett. 9(5):323-328.

150 Ramesh, M. V.; B. K. Lonsane. 1990. Critical importance of moisture content of the medium in a-amylase production by Bacillus licheniformis M27 in a solid-state fermentation system. Appl. Microbiol. Biotechnol. 33:501-505.

Saucedo-Castaneda, G., M. Gutierrez-Rojas, G. Bacquêt, M. Raimbault, G. Viniegra- Gonzalez. 1990. Heat transfer simulation in solid substrate fermentation. Biotechnol. Bioeng. 35:802-808.

Smith, J. E. 1978. Asexual sporulation in filamentous fimgi. Chapter 11 in The Filamentous Fungi, V. 3: Developmental Mycology. J. E. Smith and D. R. Berry, eds. Edward Arnold, London, pp. 214-237.

Somogyi, M. 1960. Modification of two methods for the assay of amylase. Clin. Chem. 6:23-35.

Somogyi, M. 1938. Micromethods for the estimation of diastase. J. Biol. Chem. 125:399- 414.

Southgate, D. A. T. 1991. Determination of food carbohydrates, 2d edition. Elsevier Science Publishers Ltd., Essex, p. 136.

Takamine, J. 1914. Enzymes of Aspergillus oryzae and the application of its amyloclastic enzyme to the fermentation industry. J. Ind. Eng. Chem. 6(10):824-828.

Tanaka, M., A. Kawaide, R. Matsuno. 1986. Cultivation of microorganisms in an air-solid fluidized bed fermentor with agitators. Biotechnol. Bioeng. 28:1294-1301.

Yang, S.-T.; I. C. Tang; H. Zhu. 1992. A novel fermentation process for calcium magnesium acetate (CMA) production from cheese whey. Appl. biochem. Biotechnol. 34/35:569-583.

151 a)

b)

Figure 3.1. Spore density determination; a) counting pattern; b) photomicrograph showing counting chamber in use.

152 2.5

y = 2.092X R* = 0.9937

8, (03 SI c u 3

0.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Absorbance at 530 nm

Figure 3.2. Standard curve for analysis of reducing sugar by the dinitrosalicylic acid method, glucose as standard.

153 1.2

y=1.3762x = 0.9523

0.8

r 0.6

0.4

0.2

00.1 0.2 0.3 0.4 0.5 0.6 0.7 Absorbance at 595 nm

Figure 3.3. Standard curve for analysis of protein by the Bradford method, reagent prepared by OSU laboratory stores.

154 3 0 0

y = 33.014% 250 R* = 0.8086 8 I 200 I ^ 150

< 1 .0 0 !

o o

0 21 3 4 5 6 7 Protein (mg/g dwt rice)

Figure 3.4. Correlation of protein with a-amylase acitivity.

155 0—^-0-‘0 0 ...... o 150 200 250 400 a-Amylase (SU/g dwt rice)

Figure 3.5. Correlation of a- and P-amylase activity.

156 5 0

45

40

S 35

•o 30 I o 25

E 20

15

10

5

0 0 100 150 200 250 300 35050 400 a-Amylase (SU/g dwt rIce)

Figure 3.6. Corrélation of a- and glucoamylase activity.

157 8000 □ Shaken Blended

& 5000 -

^ 4000

c 3000

5 6 7 Pair Number

Figure 3.7. Effect of extraction method on the determination of a-amylase activity, fermentation time = 2 d.

158 7000

6000

5000

tn 4000

E 3000

2000 Frozen, shaken Frozen, blended Refrigerated, shaken 1000 Refrigerated, blended

0 10 20 30 40 50 60 Storage time (d)

Figure 3.8. Effect of storage method and time on a-amylase activity, fermentation time = 2d.

159 100 A. oryzae - amylase 90 A niger - amylase

80 A oryzae - protein A niger - p r o te iir'' s ' 70

50

40

30

20 10 0 0 50 100 150 200 Time (h)

Figure 3.9. Comparison of koji fermentations of A. oryzae and A. niger.

160 —o — 1.45E7 spores —O— 1.45E6 spores & r 1.45E5 spores

0 20 40 60 80 100 120 140 160 180 200 Fermentation Time (h)

Figure 3.10. Effect of inoculum size on extracellular protein production in an A. oryzae fermentation, 37°C, initial moisture content of -41%, (w/w), no adjustment of initial pH.

161 60

c 40 I e s s £ — % Moisture Content I I ■■A' Starch, g O —D—Koji pH I —O—Protein, mg/g dw t rice

0 50 100 150 200 Fermentation Time (h)

300 30 —D—alpha SU/g dwt rice A beta 1000 U/g dwt rice 250 —A—protein mg/g dwt rice —O—gluco U/g dwt rice 200 20 ô

I 150 15 S

100

50

0 0 20 40 60 80 100 120 140 160 160 200 Fermentation Time (h)

Figure 3.11. A. oryzae fermentation of brown rice at 37°C inoculated with 1.45x 10^ spores per flask (22.5 g dwt rice); a) moisture content, starch, pH, and protein kinetics; b) enzyme and protein kinetics.

162 Is 30 Io I S z —O—% Moisture Content Q. 20 - A—Starch, g I —O—Koji pH —O—Protein, mg/g dwt. rice

0 50 150 200100 Fermentation Time (h)

300

alpha, SU/g dw t rice 250 beta, 1000 U/g dwt rice protein, mg/g dwt rice giuco, U/g dwt rice 200 20 ô I i 150 15 « 10O 10 ^

50

0 0 20 40 60 80 100 120 140 160 180 200 Fermentation Time (h)

Figure 3.12. A. oryzae fermentation of brown rice at 37°C inoculated with 1.45x10^ spores per flask (22.5 g dwt rice); a) moisture content, starch, pH, and protein kinetics; b) enzyme and protein kinetics.

163 6 0

50

S § 40 u s e t»3 I % Moisture Content o X Starch,g I Koji pH a Protein, mg/g dwt rice

0 0 50 100 150 200 Fermentation Time (h)

300 alpha, SU/g dwt rice beta, 1000 U/g dwt rice 250 - 25 protein, mg/g dwt rice gluco, U/g dwt rice / 200 m8 ># I 150 15 s a . a 100

50 - 5

20 40 60 80 1000 120 140 160 180 200 Fermentation Time (h)

Figure 3.13. A. oryzae fermentation of brown rice at 37°C inoculated with 1.45x10^ spores per flask (22.5 g dwt rice); a) moisture content, starch, pH, and protein kinetics; b) enzyme and protein kinetics.

164 6 0

50

40

î o 30

0~ initial pH 6^4 I 20 —D—initial pH 4.73 —A— initial pH 3.98 —O—initial pH 3.32 10

0 0 20 40 60 80 100 120 140 160 Fermentation Time (h)

7

6

5

4

3 —A— initial pH 6.24 - O —initial pH 4.73 2 ■A - initial pH 3.98 - O —initial pH 3.32 1

0 0 20 40 60 80 100 120 140 160 Fermentation Time (h)

Figure 3.14. Effect of initial pH on fermentation kinetics in an A. oryzae brown rice fermentation (initial moisture content -40%, 37°C); a) moisture content; b) pH.

165 20

18 initial pH 6.24 16 initial pH 4.73 initial pH 3.98 14 initial pH 3.32 Oi 12 I 10 3 (0 8 i2 6

4 2 0 0 20 40 60 80 100 120 140 160 Fermentation Time (h)

-^-initial pH 6.24 -4 3 —initial pH 4.73 —A—initial pH 3.98 - O —initial pH 3.32

0 20 40 60 80 100 120 140 160 Fermentation Time (h)

Figure 3.15. Effects of initial pH on fermentation kinetics in an A. oryzae brown rice fermentation (initial moisture content -40%, 37°C); a) substrate consumption; b) protein production.

166 initial pH 6.24 “ too Initial pH 4.73 Initial pH 3.98 initiai pH 3.32 M 60

40

60 80 too Fermentation Time (h)

350 Initiai pH 6.24 s ' 300 Initial pH 4.73 % 250 Initial pH 3.98 Initial pH 3.32 5 * 200

o 150 >* 100

0& 0 20 40 60 80 100 120 140 160 Fermentation Time (h)

80 70 initial pH 6.24 60 Initial pH 4.73 I initial pH 3.98 50 I initial pH 3.32 S 40 (0 30 ! 20 8 3 10 3 0 20 40 60 80 100 120 140 160 Fermentation Time (h)

Figure 3.16. Effect of initial pH on fermentation kinetics in an A. oryzae brown rice fermentation (initial moisture content -40%, 37°C); a) a-amylase production; b) P-amylase production; c) glucoamylase production.

167 20 koji pH 18 protein, mg/g dwt rice 16 % moisture content starcti, g 14

c 12 Î "5 i2 10 X I «• a 6 I

4 2 0 0 50 100 150 200 Fermentation Time (ti)

20 350

18 300 16

14 250 —O—Starcti, g 12 —O—alptia, SU/g dwt rice 200 —A-b e ta , 1000 U/g dwt rice I 10 —O—gluco, U/g dwt rice ! I 150 8 I 6 100

4 SO 2 0 0 20 40 60 80 100 120 140 160 180 200 Fermentation Time (li)

Figure 3.17. Fermentation kinetics for A oryzae grown on brown rice with an initial moisture content of 29%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production.

168 20

40 o —❖ —koji pH e —O —starch, g (0 "5 2 —A—protein, mg/g dwt rice CL —O —% moisture content I u Xa i

0 50 100 150 200 Fermentation Time (h)

20 350

18 300 16 S tarch , g 14 alpha, SU/g dwt rice 250 t)eta, 1000 U/g dwt rice 12 gluco, U/g dwt rice 200 2 10 f ws 8 150 I 6 100

4 50 2 0 0 50 100 150 200 Fermentation Time (h)

Figure 3.18. Fermentation kinetics for A. oryzae grown on brown rice with an initial moisture content of 39%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production.

169 -o

40 koji pH i starch, g ------f 10 protein, mg/g dwt rice % moisture content S s 20 I z

0 50 100 150 200 Fermentation Time (h)

20 350

18 —O—Starch, g 300 16 —O—alpha, SU/g dwt rice —ûr—beta, 1000 U/g dwt rice 14 —O—gluco, U/g dwt rice 250 12 200 t 10 < I 8 150 I I 6 100 4 50 2 0 0 50 100 150 200 Fermentation time (h)

Figure 3.19. Fermentation kinetics for A. oryzae grown on brown rice with an initial moisture content of 46%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production.

170 40

e —❖—Koji pH V —“ —O—Total starch, g &I —ùr— Protein, mg/g dwt rice t —D—% Moisture Content a I

0 50 too 150 200 Fermentation Time (h)

350

starch, g beta, 1000 U/g dwt rice 300 alpha, SU/g dwt rice gluco, U/g dwt rice 14 ■ 250 200 I 150 i 100

50

0 50 100 150 200 Fermentation Time (h)

Figure 3.20. Fermentation kinetics for A. oryzae on brown rice with an initial moisture content of 49%; a) substrate consumption, pH, protein production, and moisture content; b) starch consumption and enzyme production.

171 10 29% IMG 9 39% IMG 8 46% IMG 49% IMG 7

6 ï 5 su 4 s g 3 3 (3 2 1 0 0 50 100 150 200 Fermentation Time (h)

Figure 3.21. Kinetics of free glucose present in crude extract from cultures of A. oryzae grown on brown rice at varying initial moisture contents.

172 7

- 0 —29% IMC 6 - 0 —39% IMC - A - 46% IMC - 0 —49% IMC s5 I.I 4 I

1

0 0 20 40 60 80 100 120 140160 180 200 Fermentation Time (h)

20

14

(0 55

- 0 —29% IMC - 0 —39% IMC —^ —46% IMC - 0 —49% IMC

0 20 40 60 80 100 120 140 160 180 200 Fermentation Time (h)

Figure 3.22. Effect of initial rice moisture content on A. oryzae fermentation kinetics (no pH adjustment, 37°C); a) protein production; b) substrate consumption.

173 6.5

5.5

4.5 —» -2 9 % IMC -0-39% IMG - A —46% IMC —0 —49% IMC 3.5

0 20 4060 80 100 120 140 160 180 200 Fermentation Time (h)

60

50

S 40

o 30

—0 — 29% IMC S 20 —0 —39% IMC —A—46% IMC - 0 - 4 9 % IMC 10

0 0 20 40 60 80 100 120 140 160 180 200 Fermentation Time (h)

Figure 3.23. Effects of initial rice moisture content on A. oryzae fermentation kinetics; a) pH; b) moisture content.

174 350 r —0 — 29% IMC s ' 300 f - 0 —39% IMC - A —46% IMC I 250 : - 0 —49% IMC f 200 f % 150 ; 31s 100 :

50

0 20 40 60 80 100 120 140 160 180 200 Fermentation Time (h)

25 29% IMC 5 20 39% IMC 46% IMC i* 15 49% IMQ

0 20 40 60 80 100 120 140160 180 200 Fermentation Time (h)

70 29% IMC 60 39% IMC 46% IMC "o 50 ■EP 49% IMC 3 40

S 30 I 3 10

0 20 40 60 80 100 120 140 160 180 200 Fermentation Time (h)

Figure 3.24. Effects of initial rice moisture content on enzyme production in A. oryzae fermentation (no pH adjustment, 37°C); a) a-amylase production; b) P-amylase production; c) glucoamylase production.

175 160

140

120

100

40 •6.2874X + 142.37 R* = 0.5456

0 2 4 6 8 10 12 14 16 18 20 Starch (g)

Figure 3.25. Correlation of protein production to substrate (starch) consumption; data from initial moisture content experiments.

176 2 50

y = -7.2178X4-154.25 200 R* = 0.5097

150

0- 100

0 2 4 6 a 10 12 14 16 18 20 Starch (g)

Figure 3.26. Correlation of protein production to substrate (starch) consumption; data from initial pH adjustment experiments.

177 2.5 25C

30C

- 0 - 3 5 C

37C

40C

45-50C

0.5

0 1 2 3 4 5 6 Fermentation Time (d)

Figure 3.27. Effect of temperature on protein production in A. oryzae koji fermentation on brown rice (initial rice moisture content, -32.4%).

178 7

6 0.5% yeast extract

5 S

0.1% yeast extract

S a. no supplement 2

1

0 0 20 40 6 0 80 100 120 140 Fermentation Time (h)

Figure 3.28. Effect of nutrient supplementation on protein production in a koji fermentation of A. oryzae on brown rice (initial rice moisture content -33%, 37°C).

179 1.5 cm 2.25 cm —0 —3.5 cm 3.75 cm 5.25 cm 5.5 cm 7.5 cm

3 4 Fermentation Time (d)

Figure 3.29. Effect of substrate bed depth on protein production in a koji fermentation of A. oryzae on brown rice — study number 1 and 2 combined (initial rice moisture content -40%, 37°C).

180 0 .7

0.6 2.0 cm 2.25 cm -S' 0.5 2.5 cm 2.75 cm

0.4

0.3

0.2

0.1

0 10 20 30 40 50 60 Fermentation Time (h)

Figure 3.30. Effect of substrate bed depth on protein production in a koji fermentation of A. oryzae on brown rice - study number 3 (initial rice moisture content -38%, 37°C); abbreviated fermentation time.

181 s I (X]

"KLjJ

Figure 3.31. HPLC chromatogram indicating presence of fermentative metabolism products at the end of a SSF of A. oryzae on brown rice.

182 5 Substrate Alpha Amylase Activity 4.5 ______SU/q dwt substrate Lentils 1,112 4 Wheat 292 Oats 105 Barley 3.5 time = 6 days

3

« 2.5

2

1.5 —4 —Lentils —Q—Wheat 1 —A—Oats —O—Barley 0.5

0 0 1 2 3 4 5 6 7 Fermentation Time (d)

Figure 3.32. A. oryzae culture on various grain substrates.

183 CHAPTER 4

SOLID STATE FERMENTATIONS IN A GAS-SOLID SPOUTED BED BIOREACTOR

ABSTRACT

Solid state fermentation (SSF) is undergoing a renewed surge of research interest, but several problems are faced in the development of SSF on an industrial scale, including mass and heat transfer limitations inherent in existing reactors and the lack of kinetic and design data on various fermentations. A spouted bed bioreactor was designed and built with the intent of overcoming these problems, and several fermentations of Aspergillus oryzae grown on rice to produce amylases were mn in the reactor to demonstrate its usefulness.

Several advantages for the reactor compared to alternative reactors were identified. The spouted bed bioreactor for SSF can achieve equivalent a-, P-, and glucoamylase and protein productivity without the nonuniformity or solids handling problems seen in the best alternative, packed bed fermentation, from a protein productivity standpoint. A relationship between spouting frequency and protein productivity was observed, with increased spouting frequency associated with decreased protein production, possibly because of shear or impact damage to fungal mycelia. Operating conditions were found to be critical to the reactors

184 success; in particular, proper humidification was important to prevent drying of the

substrate, and control of reactor wall temperature was necessary to prevent excessive

condensation, which interfered with proper spouting. Results comparing the various

operational modes and fermentations in two separate reactors are included here. Kinetics of

static SSF fermentations were studied for comparison, and these results are also described in

the current chapter.

INTRODUCTION

Solid state fermentation (SSF), as mentioned previously, is undergoing a renewed surge of research interest, primarily because of the opportunities SSF affords for increased productivity and product concentration compared to submerged fermentation, new product possibilities, and the prospect of using a wide range of agri-industry waste streams as substrates (Ramana Murthy et al., 1993). It is widely agreed that the major problems faced in the development of SSF on an industrial scale are the mass and heat transfer limitations inherent in the process as run in existing reactors and the lack of kinetic and design data on various fermentations (Ramana Murthy et al., 1993; Mudgett, 1986).

The mass transfer limitations in existing SSF reactors are especially relevant given the investigations of some workers into the effects of oxygen and carbon dioxide concentrations on fungal growth. In controlled gas environment experiments, it has been shown that not only is a sufficient supply of oxygen critical for amylase production, but carbon dioxide must be removed as well (Bajracharya and Mudgett, 1980; Mudgett, 1980).

185 Increased carbon dioxide concentrations decreased enzyme productivity, probably by

shifting metabolism towards biomass production.

Several workers have suggested forced air convection in packed bed reactors as at

least a partial solution to the mass and heat transfer difficulties (Durand et al., 1988; Durand

and Chereau, 1993; Gumbira-Saldetal., 1993; Saucedo-Castaneda et al., 1990; Saucedo-

Castaneda et al., 1992) noted in even shallow tray fermentors (Rathbun and Shuler, 1983).

Though packed bed reactors successfully increase protein productivity, they are difficult to scale up and pose problems in solids handling (Gumbira-Sald et al., 1992). They are also difficult to operate as continuous reactors. Gas-solid fluidized beds have also previously been used to improve mass and heat transfer in SSF (Mishra et al., 1982; Moebus and

Teuber, 1986; Tanaka et al., 1986); however, fluidization requires the use of fine particles and the minimum air velocity for fluidization is high (Kunii and Levenspiel, 1991), resulting in high substrate preparation costs and power requirements.

Spouted bed reactors, initially developed in the early 1950's for grain drying

(Mathur and Gishler, 1951), are suitable for handling the large, coarse particles often used as SSF substrates. Heat and mass transfer rates are high, solids within the reactor are well- mixed, and even sticky materials can be easily spouted (Kunii and Levenspiel, 1991;

Viswanathan et al., 1986; Epstein, 1983; Bridgewater, 1985). The minimum air velocity required for spouting is also lower than that required for fluidization. As has been demonstrated for packed bed fermentation (Gumbira-Sa’id et al., 1992), a spouted bed reactor would allow drying of product in situ, but would have the advantage of preventing mycelial caking. It would also prevent caking during the fermentation. Mixing of the bed

186 of coarse particles, whether for even distribution of added nutrients or pH control agents,

would be straightforward.

It has been shown that the growth of biomass in the void space of packed beds is

responsible for much of the decrease in effective diffusion coefficients observed in these

reactors and is therefore much of the cause of the formation of gaseous concentration

gradients (Auria et al., 1992). Furthermore, it has been suggested that steric hindrance in

packed beds prevents continued growth of biomass (Laukevics et al., 1985). Because the

particles in a spouted bed do not become packed together, neither of these problems will

arise in a spouted bed bioreactor.

It is difficult to predict what effect spouting would have on the growth of the

organism. Drum reactors shear mycelia, which is a disadvantage to coenocytic genera,

esp. Rhizopus spp. (Ramana Murthy et al., 1993), so one might expect that the agitation

present in a spouted bed would damage mycelia, also; however, because the pattern of agitation in a spouted bed is much closer to one of constant motion than in a slowly rotated drum, the mycelia are unlikely to form matted networks between individual particles. It is possible that much of the damage observed in drum reactors arises when these mats are tom apart. The pattem of mycelial growth may be dramatically different in a spouted bed, perhaps with more of the mycelia directed into a substrate particle rather than through the void space and into a neighboring particle. That mycelial organisms have been successfully been grown in fluidized bed reactors (Kunii and Levenspiel, 1991) leads one to believe that culture in spouted beds may also be successful. Yeasts have also

187 been successfully grown in fluidized beds (Rottenbacher et al., 1987; Moebus and

Teuber, 1982).

Agitation sensitivity has been observed in SmF; perhaps the presence of a solid

substrate would provide some protection from shear. Agitation and shear can reduce

productivity in solid substrate SmF (Moo-Young et al., 1993), but it is not obvious what the effects of spouting will be in SSF. At any rate, susceptibility to shear varies with species, so various fungi would have to be investigated. Agitation was not found to affect penicillin production in SSF when moisture loss was compensated (Barrios-Gonzalez et al., 1993). In a packed bed SSF study of Rhizopus oligosporus on sago starch, however, where low substrate packings combined with high air flow resulted in some particle vibration, productivity was decreased, though this was not clearly linked to the agitation

(Gumbira-Sald et al., 1992).

That spouted bed fermentation is feasible is hinted at by successful SSF of rice in which the kernels were dry enough to swirl vigorously past each other and production of aflatoxin was greatly increased (Hesseltine, 1972). Sporulation was eliminated. This appeared to be related to mass transfer improvements. Until SSF is actually attempted in a spouted bed reactor, however, the question of its feasibility is not totally answered.

For these reasons, it was proposed by the author that a gas-solid spouted bed bioreactor might solve many of the problems currently faced in SSF and, in so doing, allow greater industrial exploitation of the SSF technique. A spouted bed was reported for the production of cellulase using immobilized Trichoderma viride cells (Webb et al., 1986); however, this was gas-solid-liquid reactor used for a submerged fermentation. The reactor

188 upon which the current report is based is a gas-solid reactor for SSF, and is believed to be

the first time that gas-solid spouting has been applied to SSF.

In this work, the production of the starch hydrolyzing enzymes, amylases, by

Aspergillus oryzae cultivated on brown rice was investigated in the gas-solid spouted bed

bioreactor under various operational strategies, including continual and intermittent

spouting. For comparison, the fermentation was also studied in a packed bed reactor and in

static fermentation.

MATERIALS AND METHODS

Organism

Aspergillus oryzae NRRL 697 was obtained from the USDA Midwest Area

National Center for Agricultural Utilization Research (1815 North University Street, Peoria,

Illinois, 61604). The culture was revived in the Ohio State University Department of

Microbiology culture collection facility. Working cultures were maintained on potato dextrose agar slants as described in a previous chapter.

Analysis

Analysis of samples was described in a previous chapter, with the exception of an additional assay for a-amylase. The additional procedure used was that given in the instructions for the Sigma Diagnostics a-amylase kit (Catalog Number 577, Sigma

Diagnostics, St. Louis, Missouri), modified for microplate reader techniques. For this assay, 225 pi of reagent as supplied in the kit was pipetted into a microplate well. The plate was inserted in the drawer of the microplate reader (SpectraMax 250, Molecular Devices),

189 the drawer was closed, and the reagent allowed to come to 30°C, the chamber temperature.

After this, 5 pJ of crude extract, centrifuged to remove turbidity, was added, and the

microplate was replaced in the microplate reader. Readings were taken at exactly 0,2, and

4 minutes. The purpose of the zero time reading was simply to activate the mixing cycle

that automatically precedes every reading, so that the assay reagents would be thoroughly

mixed. Between readings, the samples were left in the microplate reader to allow

incubation at the proper temperature.

The a-amylase activity was calculated as:

. , 125*1000V^M Activity {UIQ = ------—------(4.1) iV ,£

where V = total volume of reaction mixture (0.130 ml), AA = change in absorbance per

minute, Vj = sample volume (0.005 ml), e = millimolar absorptivity of p-nitrophenol

(9.1008 NT* cm '), I = lightpath (0.338 cm for this sample volume), 1000 = conversion of units per ml to units per L, and 1.25 = 5 moles substrate yields 4 moles p-nitrophenol.

Water was used as the blank.

One other difference in analysis is that for several mns the microplate reader was used to read the protein results. In this case, 20 ^il of sample and 150 |il of Coomassie(TM)

Plus Protein Assay Reagent, G-250 (No. 23236, Pierce Chemistry, Rockford, IL) were combined in the microplate reader and readings were taken at 595 nm as usual. Figure 4.1 shows a typical standard curve for this assay.

190 Inoculation and spore germination

Inoculation procedures previously described were followed for spouted bed

fermentation inoculation. All of the inocula were standardized for a spore density of 1x10^

spores/ml, starting with a spore suspension composed of the washings from sporulated rice,

except for the first run, continual spouting, which was inoculated with a nonstandardized

inoculum prepared from washings from a sporulated potato dextrose agar slant.

Before charging the reactor with the inoculated substrate, a 24 hour pregermination

period was provided. The substrate was prepared by mixing 25 g of brown rice (Le

Gourmet brand. Rice Mill, Inc., Stuttgart, Arkansas) with 14 ml of house utility distilled

water in a 250 ml Erlenmeyer flask, covering it with a silicone rubber sponge closure

(Catalog number C0921, Sigma-Aldrich, Milwaukee, Wisconsin), and autoclaving it for 15

minutes at 121°C, preparing as many such flasks as needed to give the desired total

substrate quantity. Initial rice moisture content was -40% (w/w); initial pH was -6.2. After

removal from the autoclave, the flasks were shaken to break up the rice clumps, cooled to

room temperature, inoculated with 1 ml of inoculum, shaken again to disperse the

inoculum, and placed in a static incubator at 37°C. After 24 ± 0.5 h, the flasks were

emptied into a sterile beaker, aseptically mixed, and then used to charge the reactor and

additional static fermentors, which were simply sterile 600 ml beakers, covered with foil.

For the first static run, the pregermination flasks were left intact, without disturbing the

nascent mycelia, and entire flasks were sacrificed at each sample period, a sufficient number of flasks having been prepared for this purpose.

191 Reactors

Two spouted bed bioreactors were used in this study. The major difference between

the two was size and cone angle. The smaller reactor. Reactor A, had a column diameter of

7.62 cm and an included cone angle of 90”. Detailed dimensions are given in Figure 4.2 for

Reactor A. The larger reactor had a diameter of 10.16 cm and had a steeper cone with an

included cone angle of 60°; a detailed drawing of Reactor B is given in Figure 4.3.

Appendix B contains the detailed construction drawings for Reactor B; Reactor A was

scavenged and as such did not have construction drawings available. Reactor A was used

for all but the last two spouted bed fermentations.

Reactor operation

Humidification

For spouted bed fermentation to be successful, the spouting air had to be warmed and humidified, the former to encourage growth and the latter to prevent overdrying of the rice. This was accomplished by first sparging the air through a tank of heated water.

Heating of the water was accomplished by a steam coil, into which steam was allowed to pass under the control of a steam solenoid valve controlled by a temperature controller that measured the temperature of the water in the tank. The preheated, humidified air then entered a cyclone trap to remove water that was blown over out of the tank, then flowed through an inlet line to the reactor, through the reactor, and to the outlet. A diagram of this set up is shown in Figure 4.4.

A system of heating tapes was necessary to prevent excessive condensate from forming in the inlet line of the reactor and on the reactor walls when the warm, moist air

192 entered. One tape was wrapped around the inlet line and three were wrapped around the otherwise unjacketed reactor, one at the bottom, in closely spaced spirals, one in the middle with slightly wider spaced spirals, and one at the top that was even more widely spaced, as depicted in Figure 4.5. Each tape was powered through a rheostat. Through a series of trial and error attempts, proper settings were determined for each of the operating conditions, continual spouting, intermittent spouting, or no spouting (packed bed) operation. Reactor temperature was controlled at -35°C. Earlier attempts to control condensate with a post- humidification heater were unsuccessful, causing the humidity of the air to decrease too much and dry out the rice koji.

Reactor startup

Before running a fermentation, the reactor was washed well with detergent, rinsed well, bleached with sodium hypochlorite solution (500 ml of bleach to approximately 10 L water), rinsed again, and allowed to air dry. Approximately 30 minutes before starting operation, the heating tapes and humidity tank thermostat were turned on to the predetermined settings to allow the reactor to warm up. The target fermentation temperature in the reactor, as measured with a thermometer placed in the center of the fountain region of the reactor was 30°C; temperature typically varied from 30 to 35°C.

Outlet temperature and humidity were measured with a combined hygrometer/thermometer

(Catalog Number 11-661-7B, Fisher Scientific, Pittsburgh, Feimsylvania); outlet temperature ran 2 to 3°C below that in the reactor, and outlet relative humidity was almost always at 100%.

193 The pooled pregeraiinated substrate was subdivided into substrate for static

fermentation, spouted bed fermentation, and time zero sample. One hundred grams of

substrate were placed into each of two sterile 600-ml beakers (bed height of ~4 - 5 cm),

which were loosely covered with sterile foil and placed in the static incubator at 37°C.

Approximately 200 g was placed in the spouted bed reactor (bed height of -9 cm from

inlet), except in the case of packed bed operation, which used approximately 300 g substrate

(bed height o f-12 cm from inlet), and in the operation of the new reactor, which required

approximately 350 g substrate to achieve a reasonable bed height (-12 cm from inlet).

Though aseptic techniques were not followed rigorously from this point on, every attempt was made to prevent unneccesary contamination. The air used to spout the rice was not filtered, nor was the water used for humidification sterilized. Instead, the natural tendency of solid state fermentation to resist contamination by virtue of the low moisture content and the overwhelming population of A. oryzae provided by the pregermination period were depended on to prevent deleterious levels of contaminants.

Startup was accomplished by pouring the appropriate amount of substrate into the reactor, bolting the headplate in position, and turning on an appropriate airflow. For continual spouting, spouting was begun immediately. For intermittent spouting, an initial spouting period of approximately one to two minutes was provided to ensure even settling of the substrate in the reactor. Further operational considerations are given below.

Continual spouting

For the continual spouting run in the smaller reactor (Reactor A), 166.7 g of substrate was added to the reactor, and spouting was initiated with an air flow of 9.0

194 standard cubic feet per minute (scftn). Spouting was continued at this air flow, with

occasional interruptions to replenish the water level in the humidifying tank (every 6 hours

or so) and to take samples at the inlet to the reactor (at 6.5,18.5,26, and 41.5 h). At

approximately 19 hours into the run, it was noted that the outer region of the annulus had a

tendency to freeze; at this point, the air flow was increased slightly to 9.5 scftn to reduce

this tendency, though at the end of the mn there was a distinct area of rice that was not

mixing. Samples were taken of the fteely spouting rice, the rice that was just barely packed

into the annular cone (it broke up easily), and the outer region of the annulus, in which the

rice had been knitted rather more strongly together by mycelial growth. Temperature within

the reactor varied between 33 and 37°C.

Intermittent spouting

For intermittent spouting runs, the substrate was initially poured into the reactor,

spouted briefly to uniformly distribute the rice, and then the air was turned to a low flow

rate, 1.9 scftn, one that was expected to provide oxygen to the substrate and heat removal

from the substrate. Subsequent to startup, the reactor was spouted for a given length of time at a predetermined time interval. A high air flow was used initially to break up the substrate mass and initiate spouting, followed with an air flow close to the minimum required for spouting. Operating parameters are given in Table 4.1 for the various intermittent fermentations.

Packed bed fermentation (no spouting)

After charging the substrate to the reactor, a brief spouting was performed in order to uniformly distribute the substrate within the reactor. This was the only spouting

195 performed in the run. The air flow was then set to a low flow rate, 1.9 scfin, the same as

was used between spouting episodes in the intermittent spouting experiments. A

thermocouple was inserted into the center of the bed in order to monitor changes in bed temperature as the fermentation progressed.

Run Charge Period between spouting Temperature Spouting Velocity Spouting Time (°C) (scftn) (g) (h) (s)

1 166.7 0 32-35 9.0 -

2 187.8 2 32-35 8.0-11.25 120

3 217.2 2 30.5-32.5 14-16 15-150

4 226.7 1 29-31.5 8.5-10.5 20-90

5 214.7 4 33 8.0-10.5 60

6 313.6 - 30-36 0 0

Table 4.1. Operating parameters for intermittent spouted bed fermentations.

Sampling of spouted bed and packed bed fermentations

The spouted bed bioreactors were sampled at least every eight hours, either by opening the air inlet and allowing a sample to fall from the reactor, using a scoop to assist as necessary, or by opening a sample port in the side of the reactor and scooping out a sample.

For the packed bed study, samples were taken at four different locations, through side sample ports, all at the same level in the reactor but in the four different quadrants, to follow the kinetics of amylase formation in the packed bed. Heterogeneity of amylase production was determined by taking additional samples through side ports at the time of shut down, two at each of three different levels in the bed. The first sample at each level was within the

196 first 2.0 cm from the outer edge of the reactor; the second at each level was between 2.0 and

4.0 cm from the outer edge. Figure 4.6 shows the location of the sample ports for this run.

Static fermentations

For every spouted or packed bed fermentation run, a concurrent static fermentation

was run using the same inoculated substrate source. The beakers, prepared for the static

fermentations as described in the reactor startup section, were placed in a static incubator at

37°C and were sampled at the same intervals as the spouted bed. The only unusual run was

the first one, corresponding to the continual spouting run. For this static fermentation, the

flasks for the static fermentation were not combined into a larger mass; instead, the small

amounts of substrate initially inoculated in each 250 ml Erlenmeyer were kept separate, one

flask for every sampling period. This was changed for the other mns because it was not

considered to be representative of traditional koji fermentations in which the rice would be

several cm deep.

The static fermentations, except in the first run (see below), were sampled by using

two scoopulas turned with their concave surfaces towards each other as a coring device.

The scoopulas were inserted into the substrate until the bottom of the beaker was touched,

then they were squeezed together and lifted, thereby removing a sample that extended from

the surface of the reactor to the bottom. Sampling in this maimer allowed retrieval of a sample representative of the entire mass, rather than of just the surface layers.

Runs in reactor B

A second reactor was designed and built during the course of the experiments. It has been described in detail previously. The main difference in the reactors was in column

197 diameter, with the first reactor. Reactor A, having a diameter of 7.6 cm (3 in), and the

second reactor. Reactor B, having a diameter of 10.16 cm (4 in). The reactor inlet diameter

could be modified in Reactor B. For the spouted bed fermentation experiments, an inlet

diameter of 1.27 cm (0.5 in) was used.

One continual spouting run and one 4-hr period intermittent spouting run were performed in Reactor B as a demonstration of fermentations at a different scale. For the continual run, a spouting air velocity of 12.0-13.5 scfm was used; reactor temperature varied from 31 - 32°C. For the intermittent spouting run, a spouting velocity of 14 scfm was used to spout the bed for 15 s every 4 hours; reactor temperature varied from 32 -

35.5°C. The sampling period for the former was 6 hours, for the latter, 2 hours for the first

8 hours and 4 hours for the remainder of the fermentation time. Approximately 350 g of substrate was charged to each reactor.

RESULTS AND DISCUSSION

Static Fermentation

Two types of static fermentation were studied for comparison with the spouted bed bioreactor fermentations. The first, referred to as shallow static fermentation, used only approximately 40 g of substrate placed in a 250 ml Erlenmeyer flask, resulting in a substrate bed depth of approximately 2.0 cm. This set up was identical to the flasks and substrate quantity used to generate pregerminated medium for charging the spouted bed reactor; the fermentation was simply allowed to continue in the pregermination flasks and entire flasks were sampled at each time period. The second, referred to as deep static fermentation, used

198 pregerminated substrate aquired by pooling the contents of inoculum flasks after a

pregermination period of 24 hours. One hundred grams of the pregerminated substrate was

aseptically placed in 600 ml beakers, with the resulting bed depth of 4.25 cm. Small

samples were removed from the beaker as described above at appropriate sampling times.

In both the shallow and deep static fermentations, an immediate increase in

extracellular protein content was noted, as graphically displayed in Figure 4.7. This

indicates that stirring did not disturb the production of protein; in fact, if one compares the

average increase in protein for the deep static fermentations (average of 4 runs) to that for

the shallow static fermentation, it appears that stirring the koji was beneficial, resulting in

an initial rapid rise in protein production. This reflects current manual practices in koji-

making, in which a stirring is required at about 20 hours, the time at which white patches of

mycelia become readily visible, indicating an increase in heat generation and oxygen

requirements (Lotong, 1985). The time indicated on the x-axis of Figure 4.7 (and all

subsequent figures) represents the time past the 24 hour initial germination period allowed

for the rice. Accounting for this, it is reasonable to see an increase in protein production

rate in the deep static fermentations, which were stirred, compared to the shallow static

fermentation, which remained undisturbed.

As the static fermentations progress for another 8 to 10 hours, however, the shallow

static fermentation displays higher protein production rates, resulting in over twice the

amount of protein produced per g of initial substrate in the shallow static fermentation at 32

hours than for the deep static fermentation. It appears, in fact, that after the initial increase in protein content seen in the first 8 hour period (24-32 hours post-inoculation), the protein

199 productivity in the deep static fermentations approaches zero, or may even become negative.

A negative productivity is explained by considering that the fungi may also be producing

proteases. It has been shown that different conditions favor amylase production relative to

protease production for A oryzae cultivation on rice, with protease production favored at

lower moisture contents than was amylase production (Narahara et al., 1982). Perhaps

conditions in the deep static fermentations begin to favor protease production, and these

proteases degraded the amylase previously produced.

Another difference between the shallow and deep static fermentations is that the

shallow fermentation generally had little or no smell; the deep static fermenations, on the

other hand, smelled strongly of a fruity, alcoholic odor, starting at about 16 hours (40 hours

post-inoculation). This likely indicates that the fementation has gone anaerobic, resulting in

a shift from respiration to fermentative metabolism and the production of alcohol and organic acids. The smell may be what is considered to be a typical koji smell produced by the production of leucinic acid (Lotong, 1985). Production of fermentative metabolites may be desirable in certain food fermentations, such as the production of sake (Yokotsuka,

1991a), but would be undesirable in a fermentation to produce industrial enzymes.

Typical detailed fermentation kinetics for a deep static fermentation are shown in

Figure 4.8. There are insufficient data to perfectly correlate the production times of the three enzymes assayed, a-, P- and glucoamylase, but it appears that they all rise fairly in synchrony with each other, and with extracellular protein. Shallow fermentation kinetics are shown in Chapter 3, Figures 3.17 to 3.20 for various moisture content substrates.

Similar observations can be made about these fermentations, particularly concerning the

200 relationship between protein and enzyme production. Though it was not measured in this

fermentation, similar decreases in total dry weight (40-50%) as those seen in the static

fermentations discussed in Chapter 3 were expected here.

From the results shown for static fermentations, it is clear that for prolonged enzyme

production, either unreasonably shallow substrate layers or frequent high-labor stirring must

be used so that mass and heat transfer difficulties are either avoided or alleviated. The

spouted bed reactor was proposed to overcome these transport difficulties without requiring

excessive manual labor and allowing for the possibility of reasonable scale up.

Continuous spouting

Several SSF runs with continuous spouting were made in the spouted bed bioreactor. Most of the initial runs were unsatisfactory (5 of the total of 10 runs with spouting) because of difficulties in adjusting equipment operating conditions. This will be discussed further later in this chapter. After several months work, however, a successful spouted bed bioreactor fermentation of rice by A. oryzae to produce amylases was conducted. The results of this run, believed to be the first demonstration of solid state fermentation run in a gas-solid spouted bed reactor, are shown in Figure 4.9.

Several significant points can be made about these fermentation results. It appears that, unlike in the static fermentations, there is a significant lag time of approximately 16 hours from when the pregerminated substrate was charged to the reactor. It is possible that this is caused by shear to the mycelia growing on the outside of the rice grains. At 16 hours protein production begins to be evident, with the final protein content falling approximately midway between that of the deep static and shallow static fermentations.

201 During this fermentation it was noted that some substrate stuck within the cone of the reactor. Over time, this gradually built up, resulting in a significant portion of the substrate not being spouted continuously. At the end of the fermentation, samples were taken not only of the freely spouting material that had been sampled all along, but also of the armular material that was loosely packed in the cone and of the material that was tightly packed. The latter material was bound or knit together by the mycelia of the growing fungi, the former was not freely spouting but was not knit together by fungal myceha and could be easily broken up into individual rice grains.

Figure 4.10 shows the protein content of koji sampled from these various regions of the reactor at the end of the run (42 hours) and compares it to protein content of koji from shallow and deep static fermentations. The shallow static fermentation remains the best for protein production, followed by the material that was packed in the annulus. The freely spouting material and the substrate that was only loosely packed in the annulus had more protein than the material from the deep static fermentation. On another comparitive note, none of the spouted samples had the strong alcoholic smell found in the deep static fermentation at this point.

These results indicated that though spouting the substrate provided conditions for better protein production than did deep static fermentation, conditions must be improved further to outperform shallow static fermentation. Clues to how this may be achieved are given by the improvement in performance noted in the packed section of the bed. This section developed during the fermentation, first being noted at around 26 hours — over halfway through the fermentation, and yet it achieved over half of the protein production

202 noted in the shallow static fermentation, so it is unlikely that brief periods of spouting

disrupt protein production. Packed bed SSF reactors have been investigated by others and

found to have superior performance to tray type (shallow or deep static) fermentations

(Durand et al., 1988; Durand et al., 1993; Gumbira-Sald et al., 1993; Saucedo-Castaneda et

al., 1990; Saucedo-Castaneda et al., 1992), yet were also found to have regions of

nonuniform protein production (Ghildyal et al., 1993). The combination of packed bed

operation to achieve high protein productivity with intermittent spouted bed operation to

uniformly mix the substrate appeared, therefore, to have potential, and several runs were

performed under various spouting intervals.

Intermittent spouting

The detailed enzyme and protein kinetics for intermittent spouting mns with

spouting intervals of every 1 hour and every 4 hours are shown in Figures 4.11 and 4.12.

Total carbohydrate remaining in the koji is plotted for the 4 hour interval intermittent

spouted bed operation; there was insufficient sample available to assay carbohydrate

concentration for the 1 hour run. In general, protein concentration and enzyme titers rose

simultaneously. It was seen that a steady decrease in total carbohydrate accompanied the

increase in protein and enzyme amounts; however, the amount of carbohyddrate used was

less than 20% of that available initially as starch. It is possible that future fermentations

may be run for longer time periods, increasing the conversion of sugar/starch to product. If

not, it is possible that the remaining sugar/starch could be converted to a protein-enriched

(by virtue of the biomass contained within) animal feed simply by drying the residue remaining after extraction of the enzyme.

203 Packed bed fermentation

The results of the packed bed run , shown in Figure 4.13, provided further insight

into the nature of koji fermentations. Analysis of the protein content at various points

throughout the bed showed a lack of uniform protein productivity, as seen in Figure 4.14

The inner samples showed little variance, but the outer samples showed a decrease in

protein concentration the further from the air inlet the sample was taken. It is likely that

because no provisions were made in this packed bed for uniform air distribution, that most

of the air flowed through the center of the bed, hence there was sufficient oxygen

concentration and/or heat removal even as one moved further from the inlet for the central

region. In the outer region, however, it is likely that air flow was much lower and that mass

and heat transfer difficulties could arise, resulting in the lower protein concentrations as one

moved further from the inlet. Though temperature was measured occasionally in the bed,

readings were not frequent enough to make out any trends with fermentation; however, it

was noted that at one point about 8 h into the fermentation the temperature spiked several

degrees above the usual (~35°C compared to 30°C), indicating the possibility of at least a

transient heat accumulation in packed bed operation.

In addition to the measured nonuniformities of protein production, other

nonhomogeneities were noted in the packed bed reactor at time of shut down. Regions of sporulation were noted in a band several cm below the surface of the bed and in the cone region. Upon dismantling the reactor, it was found that near the outer surface of the cone, there was a layer of strongly knit together rice grains. Immediately internal to that layer was an area of dense spomlation, also strongly knit together, and inside of that region was a

204 lightly sporulated and loosely knit but easily broken apart mass of grains. The substrate was

sufficiently strongly knit together in some areas that it interfered with ease of removal of the

material from the reactor.

Samples were taken from each area described above. The protein content for koji

from each region is shown in Figure 4.15. The highest protein content was found in the

region just to the inside of the surface layer, in the region of greatest spomlation.

Comparison of productivity

When the various operational strategies are compared, the packed bed operation and

the 4 hour spouting interval stand out as superior to all other operations, including deep and

shallow static fermentation, in terms of protein production. Figure 4.16 shows the protein

kinetics curves for the various spouted fermentations. A pattern is readily apparent. The

best protein production, as mentioned, occurs for either the packed bed (infinite time

interval of spouting) or the 4 hour interval intermittent spouting, followed by the 2 hour, the

I hour, and then the continual spouting operation. It appears that spouting causes some

form of damage to the ability of the fungi to produce protein, probably by physical shear to

the mycelia external to the rice grain. Mycelial damage has been suggested as a cause of

reduced productivity in rotary dmm reactors (Saucedo-Castaneda et al., 1990), and it would

not be unreasonable to expect similar damage in a spouted bed bioreactor.

As the spouting interval is increased, the balance between damage caused by

spouting and benefits of improved mixing and mass and heat transfer brought about by spouting shifts, until a spouting interval of 4 hours shows no damage, performing as well as the non-spouted, packed bed operation. Similar results are obtained when one investigates

205 the a-, P-, and glucoamylase activity obtained for samples from the various fermentations,

as shown in Figures 4.17,4.18, and 4.19.

Though it is somewhat difficult to compare productivity for a range of fermentation

runs because of varying times of fermentation, an attempt was made to calculate the overall

hourly production rate for protein and the various enzymes under different SSF operation

strategies. Total time used for the calculations varied from 32 to 42 hours, except for the

shallow static fermentation, for which 72 total hours was used, in order to account for the 24

hour pregermination period not counted in the other fermentations. The shallow static

fermentation data presented is not the same run as that presented earlier in this chapter; in

order to include enzyme analysis data, a run from the moisture content studies described in

Chapter 3 was used. Even so, comparable a-amylase data was not available, because the

method used to determine a-amylase in the spouted bed studies was different.

Even though there are some difficulties with the data, it is still possible to observe some trends in results from the various operating strategies. Production rates of protein were nearly 150% higher for both packed bed and 4 hr interval intermittent spouting operation than for 1 or 2 hour interval intermittent spouting, over twice the rate for shallow static cultivation, and over 10 times the rate for deep static or continual spouting fermentation, as shown in Figure 4.20. Similar results are seen for the various enzymes, shown in Figure 4.21, though the differences are not quite as large. The glucoamylase data for the shallow static bed appears to be extremely low and should be repeated to verify the extreme difference seen in this case.

206 It is possible that results in the spouted bed bioreactor are actually better than those reported. In a gas-solid fluidized fermentation of Saccharomyces cerevisiae on semi-solid substrate, significant amounts of secreted yeast proteins swept away from the fermenting substrate in the fluidizing air stream were trapped by sparging the effluent air through water

(Hong et al., 1989; Kokitkar et al., 1990). A similar step was not undertaken in the current study; perhaps some of the extracellular proteins (amylases) produced in the spouted bed fermentations were removed during the fermentation in a similar maimer. Further work is necessary to clarify this point.

This is not to say that there are no difficulties in operating a spouted bed bioreactor for SSF. As noted earlier, process condition difficulties caused a number of runs to be scrapped. The chief reasons for unsuccessful fermentations was a problem with excessive condensate within the reactor, which caused the solids to stick into a non-spoutable mass, or a problem with drying out the rice in the spouted bed to a point where fungal growth was not possible. In short, control of the humidity in the bed was difficult, and without good control, spouted bed fermentation was impossible. For the purposes of this study, the problem was overcome by trial and error, using heating tapes around the inlet tube and the reactor to control inlet humidity and reduce condensate in the reactor. Better control would be possible with a jacketed reactor or by running in a controlled temperature cabinet.

4.22 shows the results of one of the discarded mns. During the first 8 hours of the mn, the inlet air humidity was too low and the rice dried out, with the moisture content dropping by over half. During this time, protein production was slow. When the problem

207 was corrected and the moisture content began to rise, protein production also rose, though it

never did exceed that of the deep static fermentation run as a comparison.

The final spouted bed SSF runs were performed in the second of the two reactors to

be built. This reactor was larger and required a larger initial charge for acceptable

operation. There was insufficient time to properly solve the recurrent problems with

condensate and excessive drying by determining the best configuration of heating tapes and

the best settings for the humdification tank temperature and heating tape temperatures;

however, two runs were made anyway to begin to explore the effects of running the

fermentation at a different scale. Figure 4.23 shows the results for a continual spouting

fermentation and for a 4 h interval intermittent spouting fermentation. The results from the

first reactor were confirmed, in that the intermittently spouted fermentation produced more protein per 100 g initial charge than did the continually spouted fermentation, but the amount of protein produced on a similar basis was lower than for the first reactor, probably because of the difficulty in controlling the critical operational parameters cited above.

CONCLUSIONS

This study is believed to be the first report of a gas-solid spouted bed used for SSF.

The data reported here clearly demonstrated the feasibility of using a gas-solid spouted bed bioreactor for SSF. Though continual spouting was found to be detrimental to protein production compared to static fermentations, intermittent spouting improved protein production and provided the additional benefits of yielding a uniform, free-flowing final substrate.

208 The spouted bed fermentation has an additional advantage that is not reflected in the

presented data. Because it is well mixed, the entire mass is uniform, unlike the packed bed

substrate. Furthermore, the spouting causes each rice grain to remain individual and free-

flowing; no knitting together of rice grains occurs to complicate reactor shut down and

solids handling. An intermittent spouted bed fermentation can, therefore, provide the same

excellent protein or enzyme production of a packed bed without incurring the solids

handling difficulties inherent in packed bed operation with mycelial growth.

As an example of one application of the new reactor, a previously proposed process

could be immediately improved. In a packed bed solid state fermentation for the production

of animal feed, the use of the air stream for drying was proposed, but the solids handling

problems that result from the mycelia binding the substrate together made this approach

difficult to execute (Gumbira-Sà’id et al., 1992); the use of the spouted bed bioreactor

would enable the proposed process to be used.

REFERENCES

Auria, R.; Palacios, J.; Revah, S. 1992. Determination of the interparticular effective diffusion coefficient for CO 2 and O? in solid state fermentation. Biotechnol. Bioeng. 39:898-902.

Bajracharya, R.; Mudgett, R. E. 1980. Effects of controlled gas environments in solid- substrate fermentations of rice. Biotechnol. Bioeng. 22:2219-2235.

Barrios-Gonzalez, J.; Gonzalez, H.; Mejia, A.. 1993. Effect of particle size, packing density and agitation on penicillin production in solid state fermentation. Biotech. Adv. 11:539-547.

Bridgwater, J. 1985. Spouted beds. Chapter 6 in Fluidization. J.F. Davidson, R. Clift, and D. Harrison (eds). Academic Press, London, pp. 201-224.

209 Durand, A.; Chereau ,D.. 1988. A new pilot reactor for solid state fermentation: application to the protein enrichment of sugar beet pulp. Biotechnol. Bioeng. 31:476-486.

Durand, A.; Renaud, R.; Almanza, S.; Maratray, J. ; Diez, M. ; Desgranges; C.. 1993. Solid state fermentation reactors: from lab scale to pilot plant. Biotech. Adv. 11:591-597.

Epstein, N. 1983. Second international symposium on spouted beds: introductory remarks. Can. J. Chem. Eng. 61:265-266.

Gumbira-Sa’id, E., D A ., P.F. Greenfield, and H.W. Doelle. 1992. A packed bed solid- state cultivation system for the production of animal feed: cultivation, drying and product quality. Biotechnol. Lett. 14(7):623-628.

Hong, K.; Tanner, R. D.; Malaney, G. W.; Danzo, B. J. 1989. Protein entrainment during baker's yeast fermentation on a semi-solid substrate in an air-fluidized bed fermentor. Bioproc. Eng. 4:209-215.

Kunii, D.; Levenspiel; O.. 1991. Fluidization Engineering, 2d edition. Butterworth- Heinemann, Stoneham, Massachusetts, pp. 57-58.

Laukevics, J. J.; Apsite, A. F.; Viesturs, U. E.; Tengerdy, R. P. 1985. Steric hindrance of growth of filamentous fungi in solid substrate fermentation of wheat straw. Biotechnol. Bioeng. 27:1687-1691.

Lotong, N. 1985. Koji. Chapter 9 in Microbiology of Fermented Foods. Wood, B. J. B., ed. Elsevier Applied Science Publishers, Ltd. Essex, England, pp. 237-270.

Mathur, K. B.; Gishler, P. E. 1955. A technique for contacting gases with coarse solid particles. AIChE J. 1:157-164.

Mishra, I. M.; El-Temtamy, S. A.; Schugerl, K. 1982. Growth of Saccharomyces cerevisiae in gaseous fluidized beds. Eur. J. Appl. Microbiol. Biotechnol. 16:197-203.

Moebus, O.; Teuber, M. 1986. Feststoff-Fermentation im pneumatischen Wirbelschicht- Reaktor. Chem. Mikrobiol. Technol. Lebensm. 10:99-102.

Mudgett, R. E. 1980. Controlled gas environments in industrial fermentations. Enzyme Microb. Technol. 2:273-280.

Mudgett, R.E. 1986. Solid-state fermentations. Chapter 7 in Manual o f Industrial Microbiology and Biotechnology, A.L. Demain and N.A. Solomon, eds. American Society for Microbiology, Washington, D.C. pp.66-83.

210 Narahara, H.; Koyama, Y.; Yoshida, T.; Pichangkura, S.; Ueda, R.; Taguchi, H. 1982. Growth and enzyme production in a solid-state culture of Aspergillus oryzae. J. Ferment. Technol. 60(4):311-319.

Ramana Murthy, M.V.; Karanth, N.G.; Raghava Rao, K.S.M.S.. 1993. Biochemical engineering aspects of solid-state fermentation. Adv. Appl. Microbiol. 38:99-147.

Rathbun, B. L.; Shuler, M. L. 1983.Heat and mass transfer effects in static solid-substrate fermentations: design of fermentation chambers. Biotechnol. Bioeng. 38:353-362

Saucedo-Castaneda, G.; Gutiérrez-rojas, M.; Bacquet, G.; Raimbault; M.. 1990. Heat transfer simulation in solid substrate fermentation. Biotechnol. Bioeng 35:802-808.

Saucedo-Castaneda, G.; Lonsane; B.K.; Krishnaiah; M.M.; Navarro, J.M.; Roussos; S.; Raimbault’ M.. 1992. Maintenance of heat and water balances as a scale-up criterion for the production of ethanol by Schwanniomyces castellii in a solid state fermentation system. Proc. Biochem. 27:97-107.

Tanaka, M.; Kawaide, A.; Matsuno, R.. 1986. Cultivation of microorganisms in an air- solid fluidized bed fermentor with agitators. Biotechnol. Bioeng. 28:1924-1301.

Viswanathan, K. 1986. Spouted bed drying of agricultural grains. Can. J. Chem. Eng. 64:223-232.

Webb, C; Fukuda, H.; Atkinson, B. 1986. The production of cellulase in a spouted bed fermentor using cells immobilized in biomass support particles. Biotechnol. Bioeng. 28:41- 50.

Yokotsuka, T. 1991. Nonproteinaceous fermented foods and beveerages produced with koji molds. Chapter 10 in Handbook of Applied Mycology, v. 3, Food and Feeds. D. K. Arora, K. G. Mukeiji, and E. H. Marth, eds. Marcel Dekker, Inc. New York, pp. 293-328.

211 0.8

0.7

0.6

& 0.5 E I 0.4 0.3

aI . 0.2

0.1

0 0 0.1 0.2 0.3 0.4 0.5 0.6

Absorbance at 595 nm

Figure 4.1. Example of protein analysis standard curve.

212 7.6 cm Air Out Thermocouple

Screen to prevent entrainment

73 cm

lnlet___ Z screen 5 cm

NOTTO SCALE Spouting Air In

1.3 cm.

Figure 4.2. Schematic diagram of Reactor A.

213 —» 10.16 cm. Air Out ^ Thermocouple

Screen to prevent entrainment

101 cm.

Inlet screen Inlet diameter adjustment insert

NOTTO Spouting SCAŒI Air in

2.5 cm .

Figure 4.3. Schematic diagram of Reactor B.

214 Hygrometer

Air in Air out

Temperature C ontroller

House To Drain Steam

NOTTO SCALE! Cyclone

Humidification Tank

Figure 4.4. Schematic diagram of reactor system.

215 Figure 4.5. Placement of heating tapes on spouted bed bioreactor.

216 L

B

■ Ao Al

Figure 4.6. Location of sample ports on spouted bed bioreactor.

217 120

Shallow aI

1 "O3 I c s 40 s Deep 0. (average)

20

0 5 10 15 20 25 30 35 40 45 Fermentation Time (h)

Figure 4.7. Comparison of protein production kinetics in deep static and shallow static fermentations.

218 800

20 700

600

500

gluco, U/g dwt rice « 400 protein, mg/100 g charged beta, 1000 U/g dwt rice 300 alpha, SU/g dwt rice

200

100

0 0 5 10 15 20 25 30 35 Fermentation Time (h)

Figure 4.8. Typical static fermentation enzyme production kinetics.

219 6 0

ç 40 O)

E 30 O-

20

10

0 5 10 15 20 25 30 35 40 45 Fermentation Time (h)

Figure 4.9. Production of protein in a continually spouted, spouted bed bioreactor by Aspergillus oryzae grown on rice.

220 spout annulus • loose annulus - packed shallow static deep static

Figure 4.10. Comparison of protein production in various regions of a spouted bed bioreactor.

221 20 0 0 3 5 0

1800 300 1600

1400 250 e

S 1200 200 • 1000

Ü 800 gluco, U/g dwt rice beta, 1000 U/g dwt rice 600 alpha, U/g dwt rice 100 “ protein, mg/100 g charge 400 50 200

0 5 10 15 20 25 30 35 Fermentation Time (h)

Figure 4.11. Fermentation kinetics for production of amylases and protein by Aspergillus oryzae grown on rice in a spouted bed bioreactor - intermittent spouting with a 1 hour interval.

222 2 0 0 0 350

1800 —O—gluco, U/g dwt rice 300 A beta, 1000 U/g dwt rice 1600 ■ alpha, U/g dwt rice IB 1400 —D—Total sugars, g 250 I —A—protein mg/100 g ctiarge C S 1200 200 1 Q. 1000 I 150 5 800 f

600 100 1a

400

200

0 0 5 10 20 253015 35 Fermentation Time (h)

Figure 4.12. Fermentation kinetics for production of amylases and protein by Aspergillus oryzae grown on rice in a spouted bed bioreactor - intermittent spouting with a 4 hour interval.

223 2000 350

1800 300 1600

1400 250

g 1200 200 * 1000 gluco, U/g dwt rice

beta, 1000 U/g dwt rice Œ. 800 •a alpha, U/g dwt rice 600 protein, mg/100 g charge 100 « 400 50 200

5 10 15 200 25 30 35 Fermentation Time (h)

Figure 4.13. Fermentation kinetics for production of amylases and protein by Aspergillus oryzae grown on rice in a packed bed bioreactor.

224 2.5

f E. c i CL Outer sample Inner sample 0.5 Bed Height = -7 cm

0 1 2 3 4 5 6 7 Height Above Cone (cm)

Figure 4.14. Effect of position in reactor column on protein production in a packed bed bioreactor.

225 E 1.5

cone surface near surface friable center of cone Position In cone

Figure 4.15. Effect of position in reactor cone on protein production in a packed bed bioreactor.

226 3 5 0

300

250 ai

200 - O — Packed bed A ■ SBF - 4 hr E 150 — SBF-2hr —A —SBF - continuai - ♦ —SBF - 1 hr 0. 100 " 'A Static -deep

010 20 30 40 Fermentation Time (h)

Figure 4.16. Comparison of operational strategies for a spouted bed bioreactor protein production.

227 18

16 —O— Packed bed 14 ■ ÛT' 4 hr spout —O— 1 hr spout 12 —O—static, average I I 10 I 8 ! 6

4

2

0 0 5 10 15 20 25 30 35 Fermentation Time (h)

Figure 4.17. Comparison of operational strategies for a spouted bed bioreactor; a- amylase production.

228 40 shallow static 1 hr interval 9 35 4 hr interval packed bed

25 ■

S 20

0 10 20 30 40 50 6070 80 90 100 Fermentation Time (h)

Figure 4.18. Comparison of operational strategies for a spouted bed bioreactor; 13- amylase production.

229 2 0 0 0

1800

1600

shallow static 1 hr interval ■a 1200 4 hr interval packed bed o 1000

800

600

400

200

0 10 20 30 40 50 60 70 80 90 100 Fermentation Time (h)

Figure 4.19. Comparison of operational strategies for a spouted bed bioreactor; glucoamylase production.

230 Shallow Deep Continual 4 hr Packed Static Static Spouting Interval Interval Interval Bed SBF Operating Strategy

Figure 4.20. Effect of reactor type and operating strategy on protein productivity.

231 160

140

120 ■ Shallow static ■ 1 hr Interval SBF □ 4 hr Interval SBF 100 I □ Packed Bed = 80 "O o 60

40

20

a-Amylase b-Amylase Glucoamylase (0.01 U/g dwt (10 U/g dwt (U/g dwt rice/h) rice/h) rice/h)

Figure 4.21. Effect of reactor type and operating strategy on enzyme productivity.

232 6

5 0.35

o>0> 4

u «

- 3 0.25 £ Î e

aI . 2 02

A -Spouted Bed Reactor —• “ Static Fermentation 0.15 —A— Moisture content - spout —O— Moisture content - static 0 0.1 0 5 10 15 20 25 30 35 Fermentation Time (h)

Figure 4.22. Effect of operating conditions on protein production in a spouted bed bioreactor - intermittent spouting, 1 hour interval.

233 120

100 ou s O SBF - 4 hr interval 80 ai

u i O) I 40 SBF - continuai

C

â1 20

0 510 15 20 25 30 35 Fermentation Time (h)

Figure 4,23. Démonstration of protein production in Reactor B.

234 CHAPTERS

HYDRODYNAMICS OF GAS-SOLID SPOUTED BEDS FOR SOLID STATE FERMENTATION

ABSTRACT

This chapter discusses the hydrodynamics of gas-solid spouted beds for solid state fermentation (SSF). The physical properties of the SSF substrate that are of relevance to spoutability are discussed. Rice of suitable moisture content for SSF was found to be spoutable with little or no particle attrition. Though density decreased with increasing moisture content of the brown rice substrate, spoutability decreased, contrary to what would be expected for a lower density particle. This led to the conclusion that the concurrent increase in rice grain size and stickiness must predominate over density in affecting the spouting characteristics. Fountain height and annular mixing rates increased with increased air flow rate, and annular mixing rates could be linearly correlated to air flow rate, allowing prediction of mixing times for various operating conditions.

Minimum spouting velocity increased with increased moisture content, bed height or holdup, and reactor inlet diameter. A comparison of minimum reactor superficial spouting velocity and minimum inlet superficial spouting velocity to several existing

235 correlations resulted in Uemaki equation being selected as the most generally valid for the reactors and substrates used in this study. The use of a proper diameter in the correlation for a nonspherical particle was important in achieving the best predictions of minimum spouting velocity; for rice at moisture contents typical of SSF, a modified average diameter provided the best fit.

INTRODUCTION

As the interest in solid state fermentation (SSF) has grown recently (Ramana

Murthy et al., 1993), so has an interest in developing improved bioreactors in which to perform SSF. The main problems of the existing reactors are the mass and heat gradients that build up in the substrate during the fermentation (Rathbun and Shuler, 1983;

Ghildyal et al., 1992). A gas-solid spouted bed bioreactor has been proposed to alleviate these difficulties, as described elsewhere in this document.

Among the properties of a spouted bed reactor that make it suitable for SSF are its abilities to spout coarse, sticky particles (Epstein, 1983; Bridgewater, 1985), such as those used for SSF substrates; that heat transfer and mass transfer are improved over that in a packed bed (Kunii and Levenspiel, 1991); that solids mobility and mixing are good

(Viswanathan et al., 1986); and that the reactor is nearly isothermal (Viswanathan et al.,

1986.

In order for a gas-solid spouted bed to be useful for SSF, a basic understanding of the reactor hydrodynamics is necessary. Minimum spouting velocity, mixing time, and

236 the effect of column diameter and inlet diameter on spout stability must be predicted to allow scaling up the reactor. A critical question that must be answered is whether substrates suitable for SSF can be spouted at all. Attrition of the particles is also of concern, and it must be determined whether or not this will be a problem.

Several correlations for pertinent parameters exist in the literature (Mathur and

Epstein, 1974). Of utmost importance in design is the ability to predict the minimum spouting velocity, and correlations for this parameter have been widely studied. Whether or not these parameters apply to an SSF substrate such as moist rice must be determined, especially in the usual case where nearly spherical particles have been used to establish the correlation. Further discussion of the various correlations and how well they fit the current data follow in the results and discussion section.

MATERIALS AND METHODS

Reactors

A series of experiments were run for each of two reactors; these reactors were described in detail in Chapter 4. Reactor A is shown in Figure 4.2, Reactor B in Figure

4.3, The main differences between the two were size, with Reactor A having a smaller column diameter than Reactor B, at 7.62 cm vs. 10.16 cm; included cone angle, 90° for

Reactor A and 60° for Reactor B; and that Reactor B had a series of interchangeable base plates that allowed inlet diameter to be varied from 1.27 to 1.9 to 2.54 cm.

237 Particles

Brown rice, as described in Chapter 3, was used for the spouting hydrodynamics experiments. Water and rice in sufficient amounts to achieve target moisture contents were combined in beakers, covered with foil, and autoclaved for 15 minutes at 121°C.

Immediately after autoclaving and again after cooling, the rice was stirred in an attempt to achieve uniform moisture content throughout the beaker; however, some heterogeneity was noticed during the experiments. Moisture content of a sample from each moisture content rice prepared was measured. For the experiments with Reactor B, moisture content from samples taken after spouting was also measured.

A small portion of each rice sample was dyed by immersing the rice in iodine or in methylene blue. This rice was added with the first increment of rice during the hydrodynamic measurements to allow tracking of individual particles.

Size and density measurement

Size of the rice grains was measured by using hand calipers to measure the length and the major and minor width axes of the rice grains. For Reactor A experiments, length was measured on 25 separate grains and widths were measured on 10 separate grains for each of the moisture contents prepared. For Reactor B experiments, all three measurements were made on 20 different rice grains. The average size for each moisture content was calculated.

Density was measured by adding a sample of each rice to a measured volume and mass of water in a graduated cylinder and immediately measuring the changes in volume and mass. Density was calculated as change in mass divided by change in volume.

238 Single samples were measured for experiments with Reactor A; three samples for each

target moisture content were measured for Reactor B.

Experimental procedures

The following general procedures were followed for each moisture content rice

tested in a given reactor. An initial weighed quantity of rice was placed in the reactor.

Bed height was measured in cm from the air inlet orifice at the base of the cone.

Minimum spouting velocity was determined by increasing the air flow rate until the bed spouted, then decreasing the air flow until the spout was barely stable. This air flow rate was considered the minimum air flow required to spout the rice and was converted to spouting velocity by dividing the volumetric flow rate by the cross sectional area of the reactor or inlet. When the reactor cross sectional area was used, the resulting velocity was called the superficial reactor velocity. Us; when the inlet cross sectional area was used, the velocity was termed the superficial inlet velocity.

Data collection will be discussed in detail only for Reactor B. Procedures were similar for Reactor A, except that inlet diameter was not varied. For Reactor B, fountain height was measured at the minimum spouting velocity from the top of the bed. After measuring the fountain height, the air velocity was increased slightly and the fountain height was measured again. This was repeated until one of the following conditions occurred: the maximum air flow rate was attained, the fountain height reached 70 cm

(near the top of the reactor), or excessive material was in the fountain, as shown by a sudden decrease in fountain height accompanied by what appeared to be afluidized bed of grain above the spouted bed.

239 For each air velocity, if sufficient bed height was available for accurate measurement, the annular speed was measured. A set distance was marked on the column, and the time for a dyed grain of rice to travel the distance was clocked with a stopwatch. The distance was increased as the bed height increased to allow maximum accuracy in timing. Five separate grains were timed for each air flow rate. The average of the five times was used to calculate the annular speed. If the times were less than 0.5 seconds, the data was considered inaccurate and was not used.

After reaching the maximum fountain height for each holdup value, an additional amount of rice was added to the reactor and the process repeated. When either the rice available for testing was used up or bed height was too deep to spout, that series of experiments was concluded and a new rice sample was tested, beginning with a clean reactor. After all of the rice samples were tested for a given inlet diameter, the diameter was changed, identical rice samples were prepared, and the process was repeated.

RESULTS AND DISCUSSION

Effect of moisture content on grain size and density

Absorption of water affects the size and density of individual rice grains. This will affect the spouting characteristics of the rice and is, thus, an important parameter to consider in design of a spouted bed bioreactor. Earlier applications of spouting to rice or other grain have been either drying or thermal disinfestation procedures, processes for which moisture content is generally 10 - 30% (w/w) and which are not expected to cause

240 as large a change in either size or density. For this reason, the effects were investigated here.

Figure 5.1 shows the effect of changing moisture content on rice size and density.

The effect is more pronounced on the major and minor widths than on the length, with changes of approximately 25%, 10%, and 4%, respectively, over a moisture range of 28% to 45% (w/w) moisture content. As it has been suggested that the smaller diameter be used in design correlation when the spouting particles widely deviate from spherical

(Mathur and Epstein, 1974), the significant change in the width of the rice is important and should be considered when predicting required spouting velocity.

Density, conversely, decreases by almost 20% as moisture content increases from

28% to 45% (w/w). The decrease is linear over this range. The experimental data are shown fitted to a linear equation in Figure 5.2. Again, this is important in predicting spouting velocities and must be included in considerations for design of a spouted bed bioreactor.

Hydrodynamic studies

A number of experiments were run in both Reactor A and Reactor B to investigate the basic hydrodynamics of the spouted bed reactor. As described above, for each moisture content rice tested, after setting the reactor inlet size, various hydrodynamic measurements were taken for each height for every combination of inlet diameter and reactor holdup. In the process of taking these measurements, a given rice sample would be spouted for up to an hour and a half. Though precautions (sparging the air stream through a tank of water) were taken to prevent drying of the rice, there was some concern

241 that drying might be taking place, thereby changing the hydrodynamic properties of the

rice as the test was run. Moisture content of the rice was, therefore, measured both pre-

and post-spouting. Figure 5.3 shows the results of those tests. Though a trend towards a

slight increase in moisture content was observed, the difference was not significant.

Bed height, or reactor holdup, was increased during the studies by adding a given

mass of rice. Figure 5.4 shows how the holdup (measured in grams) correlated with the

bed height for a given moisture content rice. Such a correlation is useful for converting design calculations based on bed height to productivity based on holdup.

Attrition was not observed to be a problem under any of the operating conditions

investigated. Neither noticeable particulate in the exit air stream nor broken rice kernels in post-spouting samples were detected.

Fountain height

As superficial spouting velocity was increased over the minimum spouting velocity, the height of the fountain increased. The correlation between the two parameters was nearly linear in the range of bed heights, moisture contents, and air flow rates studied. Figure 5.5 shows the experimental results for Reactor B, inlet diameter of

1.27 cm. Figure 5.6 shows the results for Reactor B, inlet diameter of 1.9 cm, and Figure

5.7 shows the results for Reactor B, inlet diameter 2.54 cm. In all cases, as the holdup or bed height was increased, the fountain height decreased.

Similar results are shown in Figure 5.8, which compares fountain heights for two different moisture contents and 4 different bed heights. It is seen in this figure that fountain height decreased as moisture content increased.

242 Minimum spouting velocity

Experimental results for minimum spouting velocity required to spout rice of various moisture content at a given bed height are shown in Figures 5.9 and 5.10. As expected, for both reactors at all inlet diameters, the minimum spouting velocity increased as the bed height increased. It also increased as moisture content increased. An increase in moisture content corresponds to increased kernel size and decreased density

(Figure 5.1). Increased size should increase minimum spouting velocity; decreased density should decrease minimum spouting velocity. The increase in minimum spouting velocity with increased moisture content indicates that the effect of increased size predominates, with the strong possibility that this observation may also be the result of factors such as stickiness, which was observed to increase dramatically as moisture content increased from 28 to 45% (w/w).

The inlet diameter appears to affect the degree to which moisture content affected the minimum spouting velocity. In Reactor A, with a fixed 1.27 cm inlet, increased moisture content caused increased Ums- This was also observed for Reactor B when the reactor inlet diameter was 1.27 cm. For this operational configuration, the curves in the figure for the various moisture contents are spaced fairly well apart, with differences in minimum spouting velocity as great as 14 cm/s, over 20%; however, for an inlet diameter of 1.9 cm, the curves overlap for much of their length. The data for inlet diameter of 2.54 cm is difficult to interpret because of its sparcity and because of the poor spouting behavior observed for most experimental conditions for this inlet diameter.

243 Though no actual measurements were possible, visual observations of the spouting action found an increase in spout diameter with both increased inlet diameter and rice moisture content.

Typically, the minimum spouting velocity referred to in discussions of spouted bed hydrodynamics is the superficial spouting velocity based on the reactor cross sectional area. This is the spouting velocity referred to up until this point in this document. The minimum inlet superficial velocity, based on cross sectional area of the inlet, may also be of interest.

Figure 5.11 shows the effect of inlet diameter and bed height on minimum superficial spouting velocity and minimum inlet superficial spouting velocity for various moisture content rice in Reactor B. The minimum superficial spouting velocity, as noted previously, increased with increasing inlet diameter. The minimum inlet superficial spouting velocity, on the other hand, decreased with increasing inlet diameter. Though it is widely accepted that the spouting fluid flows through the annular region as well as through the spout, with values reported ranging from 37% to as high as 68% of the air flowing through the annular region (Mathur and Epstein, 1974), the relationship between inlet and reactor diameter, bed height, and cone angle and the total air flow required to spout the bed is unclear. From these experimental results, it is clear that the critical parameter is neither the reactor superficial velocity nor the inlet superficial velocity; rather, it is likely to be the velocity within the spout in the particle bed.

244 Annular speed and mixing time

The flow rate of solids in the annulus is of interest because it determines the

mixing characteristics of the reactor, characteristics that are of great importance in

operation of the reactor as a bioreactor. Complete mixing can be assumed if the mean

residence time is on the order of six times the cycle time (Mathur and Epstein, 1974); this

value is referred to as the mixing time for the purposes of this work.

In all cases, annular speed increased with increased spouting velocity. For

Reactor A, the effect of bed height on annular speed is obscured by the experimental error. The data for the different moisture content rices that were spouted in reactor A are shown in Figure 5.12. The results for the various holdups are seen to overlap for the most part, and the overall data can be fitted fairly well (R^ > 0.88 in all cases) to a linear equation, but it is difficult to state unequivocally that no relationship between bed height and annular speed exists. A roughly linear relationship exists for all of the data pooled for annular speed and spouting velocity, as seen in Figure 5.13.

For Reactor B, the situation is only somewhat less cloudy. There appears to be an effect of bed height, or holdup, on annular speed, but the pooled data for all holdups and moisture contents at a given reactor inlet can still be roughly fit to a linear equation, as is discussed later. Figure 5.14 shows results from Reactor B with a 1.27 cm inlet spouting

28% (w/w) moisture content rice. As bed height increased, annular speed decreased. For a given holdup, the relationship between annular speed and spouting velocity fit very well to a linear equation (R^ > 0.95). Insufficient data was available to undertake a similar analysis at higher moisture content rices or larger inlet diameters.

245 As was true for Reactor A, no obvious trend for the effect of moisture content on

annular speed is apparent. It is surprising that the moisture content did not exert an

important effect on the annular speed, given the way it affected size, density, and

stickiness. Perhaps the ranges of bed heights and moisture contents studied were not

wide enough to discern any effects. Figure 5.15 shows the results for the various inlet

diameters tested. Again, a roughly linear relationship between annular speed and

spouting velocity was noted, though for Reactor B, the fit was much worse (R^ = 0.51 to

0.75) than for Reactor A.

The annular speed is affected by the inlet diameter; Figure 5.16 shows these

results for Reactor B with various inlet diameters. As the inlet diameter increased,

annular speed for a given superficial velocity decreased. The annular speed is related

directly to the flow rate of solids in the spout; this implies that the mass flow rate of

solids in the spout decreases with inlet diameter, as well.

From the annular speed, we can estimate the mixing time for a given

spouted bed configuration. Mixing times were estimated for several of the experiments;

results are given in Table 5.1 and 5.2. Mixing times in the current studies were found to

be on the order of a minute or less, ranging from 18.6 to 57.5 seconds in the examples cited. These are conservative estimates, given that using the wall velocity and total bed

height to calculate cycle time results in the maximum possible cycle time for an

individual particle. When plotted against bed height, as shown in Figure 5.17, mixing

time was seen to be linearly proportional to bed height for the experimental conditions

studied. This is in disagreement with the earlier results presented, that bed height

246 affected annular speed, with decreasing annular speed for increasing bed height. This

disagreement may be an artifact of experimental error. It is unlikely that mixing time is

truly linear with respect to bed height over a wide range, but within the narrow range sampled, the scatter in the data allows a linear approximation with good fit.

Moisture content (w/w) 26.6% 36.2% 37.2% Cycle time 9.6 s 5.3 s 3.7 s Mixing time 57.5 s 31.5 s 22.4 s

Table 5.1. Cycle and mixing times estimated for Reactor A; Us = 1.2 Ums. bed height 15 cm.

Moisture content Bed height Ums (cm/s) Annular Speed Cycle time Mixing (w/w) (cm/s) (s) time (s) 28& 12.5 65.5 1.56 8.0 48.2 32.5% 14 81.5 3.48 4.0 24.1 39% 14 90.3 4.52 3.1 18.6 39.4% 15.5 90.29 4.18 3.7 22.2

Table 5.2. Cycle and mixing times estimated for Reactor B; Us = Ums. variable bed height.

Summarv of measured hvdrodvnamic parameters

Tables 5.3 and 5.4, while not comprehensive, gather the experimental results of this chapter into a concise form and can be used to obtain a general idea of how various system configurations and conditions affect important spouted bed parameters.

247 Dc = 7.62 cm , D; = 1.27 cm, MC = 26.6% (w/w)

Hb (cm) Urns (cm /s) Annular speed (cm/s) Mixing time (s)

9 77.61668 0.637484 84.708

12.5 79.68646 0.586625 127.85

15 85.8958 0.917993 98.04

Dc = 7.62 cm, Dj = 1.27 cm, Hb = 9 cm

MC %(w/w) Unu (cm /s) Annular speed (cm/s) Mixing time (s)

26.6 77.61668 0.637484 84.708

36.2 79.68646 0.878073 61.49829

37.2 96.24469 1.453488 37.152

Table 5.3. Summary of some experimental parameters for Reactor A.

Spoutabilitv

As moisture content increased, it was noted that it became more and more difficult to spout the rice uniformly. The annular region would not mix well, and often the spout would freeze, building up walls of unmoving rice within which a very dilute fountain would continue to spout. These situations occurred at the two highest moisture contents and appeared to be a greater problem with increasing inlet diameter.

It was also noted that at the 2.54 cm inlet diameter, even the low moisture content rice became unspoutable when excess water blew into the reactor from the humidification tank.

The steeper cone angle in Reactor B appeared to allow moister rice to spout in a more stable manner. The sticky, moist rice slid down the cone wall better in Reactor B, which seemed to reduce problems with dead zones forming in the annular region.

248 Dc = 10.16 cm, Di = 1.27 cm, MC = 28% Dc = 10.16, Di=1.27 cm, Hy = 14±1.5 cm

Hb (cm) Urn, (cm/s) Annular Mixing time %MC Urn, Annular Mixing time speed (cm/s) (s) (w/w) speed (cm/s) (s) (cm/s)

9.6 655 2.6 22.6 28 65.5 1.6 49.5

10.4 66.9 2.3 26.9 325 8 1 j 3.5 24.1

11.2 65.5 1.7 39.2 39 90.2 4.5 18.6

12.0 64.0 1.5 47.6 39.4 90.2 4.2 22.2

12.9 65.5 1.6 495

Di = 1.27 cm, MC = 27+/- 1 %, Hy = 14 +/- 1.5 cm Dc = 10.16, MC = 28%(w/w), Hb = 14+/- 1.5 cm

Dc (cm) Um, (cm/s) AS (cm /s) Mixing time Di (cm) Urns (cm/s) AS (cm/s) Mixing time (s) (s)

7.61 * 85.9* 0.92* 84.7* 1.27 65.5 1.6 49.5

10.16 65.5 1.6 49.5 1.9 103.3 1.2 69.5

2.54** 116.4 1.0 78.084

‘ data from Reactor A; cone angle is also different (90" instead o f 60" ‘•D| 2.54 cm used 34.5% MC because of lack o f data

Table 5.4. Summary of some experimental results for Reactor B.

Correlations for minimum spouting velocity

Numerous correlations have been suggested for the prediction of minimum

spouting velocity, a critical design quantity. Many of these are empirical in nature,

though a few have a dimensional analysis or theoretical basis. One of the earliest

proposed, and still one of the most popular, is that of Mathur and Gishler (Mathur and

Epstein, 1974). Several improved or situation specific correlations have since been proposed, though most of this work was done in the early 1970’s, and few papers have

been published on the subject in recent years. Table 5.5 lists several of these correlations.

249 Reference Correlation and Comments Mathur and Gishler, X 1955 {Pp-Pf) Pf Charlton et al., 1965 COS units Smith and Reddy, 1/2 g[pp-pfj ro,Yl 1964 [ /„ = d. 0 .6 4 + 26.8 pfD , U c j \ V SI units Bmnello et al., 1974 X [Pp-Pf) 2g Pf COS units OJ24 Uemakietal., 1983 f j \ 0.615 0.274 (Pp-Pf) t/„,= 0.977 Rl 2gH, Pf SI units

Table 5.5. Correlations for minimum spouting velocity.

Of the existing correlations, most were developed for roughly spherical particles.

The substrate used in this study is not spherical, having a length to width ratio of 3 or more. Furthermore, though grain in general and rice in particular have been studied in spouting beds before, these studies were all concerned with drying rice from fairly low moisture contents (generally less than 30%) (Viswanathan et al., 1986), not with the somewhat higher moisture contents (30% to 45%) expected to be used in spouted bed solid state fermentation. For this reason, a comparison of experimental results from this study with existing correlations was undertaken, in order to determine the correlations best suited to design of a spouted bed bioreactor for SSF. Several of the more recent

250 correlations include parameters such as bed voidage which were not measured in this study (Kmiec, 1983; Littman and Morgan, 1983; Becker, 1961); these correlations were therefore left out of the analysis.

Proper determination of the particle diameter is important for the best fit of a given correlation (Mathur and Epstein, 1974). The diameter of the particle was at first determined in two ways in this study. The first, called the effective diameter, Deff, was found by calculating the volume of an average rice grain, assuming it to be a rod of elliptical cross-section, with length equal to the measured length of the grain and the major and minor axes of the ellipse equal to the major and minor widths of the grain, respectively. From this volume, the diameter of a sphere of equal volume was calculated.

The surface area ratio of the grain to equivalent sphere, the sphericity, was then calculated. The sphericity multiplied times the equivalent diameter results in the effective diameter (Kunii and Levenspiel, 1991). The second diameter used, called the average diameter, Dave, was simply the average of the major and minor width. The use of this diameter was based on the observation that cylindrical particles tend to align vertically in air streams (Mathur and Epstein, 1974). Though this could not be experimentally confirmed in the system used in this study, it is expected to apply.

All of the data from the experiments in both reactors was plotted along with selected correlations. These charts are included in Appendix C. Tables 5.6 and 5.7 summarize the results in terms of what correlations best fit the data for given conditions and diameter used, based on qualitative visual inspection of the graphs.

251 Experimental conditions Mathur and Uemaki Brunello Smith and Chariton Gishler Reddy 2896IM C 1.27 cm inlet POOR-HIGHPOOR-HIGHPOOR-LOW VERY POOR- VERY POOR - HIGH HIGH 34J96 IM C 1.27 cm POOR - HIGH POOR - HIGH POOR-LOW VERY POOR - VERY POOR - inlet HIGH HIGH 9.8% IMC. 1.27 cm POOR - HIGH POOR - HIGH POOR-LOW VERY POOR - POOR - HIGH HIGH 26.6% IMC, 1.27 cm POOR - HIGH POOR - HIGH POOR-LOW VERY POOR- VERY POOR - inlet HIGH HIGH 36.2% IMC. 1.27 cm POOR - HIGH POOR - HIGH POOR-LOW VERY POOR- VERY POOR- inlet HIGH HIGH 37.4% IMC. 1.27 cm POOR - HIGH POOR - HIGH PtXIR - LOW VERY POOR - VERY POOR - Inlet HIGH HIGH 28% IMC. 1.9 cm inlet GOOD GOOD POOR - LOW VERY POOR - VERY POOR - HIGH HIGH 34 J% IMC, 1.9 cm inlet GOOD GOOD VERY POOR - VERY POOR - VERY POOR - LOW HIGH HIGH 28% IMC. 2 M cm inlet EXCELLENT EXCELLENT VERY POOR - VERY POOR - VERY POOR - LOW HIGH HIGH .34.5% IMC. 2.54 cm GOOD GOOD POOR-LOW EXTREMELY EXTREMELY inlet POOR - HIGH POOR - HIGH

Table 5.6. Fit of various literature correlations to experimental Ums; diameter used = Deff.

In most cases, the correlation of Charlton et al. (1965) provided the worst fit to the experimental data, greatly overestimating the minimum spouting velocity. Data for which this correlation was developed were taken from experiments in which relatively short bed heights (2.5 to 20 cm, which encompasses the range used in the current studies)

were used; however the density of the particles used was much heavier than those in the current work (2.6 to 11.0 g/cm^ compared to 1.27 to 1.29 g/cm^)and the particles were

perfect spheres. This demonstrates that the best correlation to use is not necessarily the

252 one developed in a geometrically similar reactor - the particle characteristics are also

important.

Exptl conditions Mathur and Uemaki et al. Brunello Smith and Reddy Charlton Gishler

25% IMC POOR - LOW EXCELLENT VERY POOR- POOR - HIGH VERY PO O R- 1.27 cm inlet LOW HIGH

30% IMC POOR - LOW GOOD VERY POOR- EXCELLENT VERY POOR - 1.27 cm inlet LOW HIGH

9.8% IMC POOR - LOW POOR - LOW VERY POOR- POOR - HIGH VERY POOR - 1.27 cm inlet LOW HIGH

26.6% IMC GOODEXCELLENT POOR-LOW POOR - HIGH VERY POOR - 1.27 cm inlet HIGH

36.2% IMC GOOD EXCELLENT POOR - LOW POOR - HIGH VERY POOR - 1.27 cm inlet HIGH

37.4% IMC POOR - LOW FAIR VERY POOR - POOR - HIGH VERY POOR - 1.27 cm inlet LOW HIGH

25% IMC POOR - LOW FAIR VERY POOR - FAIR EXTREMELY 1.9 cm inlet LOW POOR - HIGH

30% IMC POOR - LOW FAIR VERY POOR - FAIR EXTREMELY 1.9 cm inlet LOW POOR - HIGH

25% IMC POOR - LOW POOR - LOW EXTREMELY VERY POOR - EXTREMELY 2.54 cm inlet POOR-LOW HIGH POOR - HIGH

30% IMC POOR - LOW FAIR VERYPOOR- POOR - HIGH EXTREMELY 2.54 cm inlet LOW POOR - HIGH

Table 5.7. Fit of various literature correlations to experimental Ums; diameter used Dave*

From inspection of the charts, it appears that the Uemaki and the Mathur and

Gishler correlations are the most generally applicable correlation for the spouted bed reactors under study here. It was noted that often the use of Deff overpredicted the

253 experimental results and use of Dave underpredicted them. An arithmetic average of the

two diameters was therefore explored as a way to further improve the fit.

A statistical analysis of all of the results, including calculations of Uoms based on

predicted Ums, was performed by finding the square of the sums of the residuals (SSR)

for each correlation. The results of the analysis are given in Table 5.7 through 5.10 for the originally calculated correlations using Deff and Dave; Table 5.11 gives results for the newly defined modified average diameter.

By selecting the lowest SSR in each case, the best-fitting correlations were selected for plotting against the experimental data. Figure 5.18 shows the overall results.

The best-fitting correlation was that of Uemaki using the modified average diameter.

Best correlations for Reactor A are shown Figure 5.19 and for Reactor B at three inlet diameters in Figure 5.20. In three out of the four cases, the use of the Uemaki correlation with the modified average diameter provided the best fit, within about 20-25% overall of the experimental value. This correlation and diameter are recommended for future design use at this scale.

The Mathur and Gishler equation, which was the second best-fitting correlation in the current study, was developed using dimensional analysis and was derived from numerous experimental data, covering column sizes of 7.6 to 30.5 cm, various particles, and both air and water as spouting fluid. The Uemaki correlation was developed as an improved Mathur and Gishler type equation better suited to reactors containing a variety of particle sizes. It is not surprising that both are reasonably valid for the conditions in this study.

254 The equation of Brunello et al. was developed for a mixture of sorghum and

soybeans. The Smith and Reddy equation was developed using data from particles with a

wide variation in density and shape, including some particles of cylindrical shape with an

elliptical cross section, and for four different inlet sizes in a 15.24 cm column. Over the

wide range of conditions investigated in the current study, these two equations fit the data

fairly well in several instances, but were not as widely applicable as was the Uemaki or

Mathur and Gishler equation.

Further work is necessary to understand the spouting of substrates for SSF.

Deeper beds should be investigated to better understand scale up issues. Other substrates of interest for fermentation, such as lentils or wheat bran, must also be investigated, as it

is not expected that similar spouting behavior will occur for such dissimilar particles.

CONCLUSIONS

Rice of a moisture content amenable to SSF was easily spoutable in a spouted bed reactor with little or no attrition of the particles. Though no existing correlation was found to predict the minimum spouting velocity with a high degree of accuracy in all cases, the Uemaki equation using the modified average diameter was generally valid for providing an estimate of the minimum spouting velocity. This provides the basis for the design of a spouted bed bioreactor using moist rice as the substrate.

The information on the variation of fountain height with spouting velocity, the effect of moisture content of substrate on minimum spouting velocity, and the control of mixing time in the reactor should prove valuable in future design work as well.

255 a s

D(effective) D(average) Urn* Gom; Urn. tJomi Uemaki 28504.64 Uemaki 55555025 Uemaki 18566.29 Smith & Reddy 32656320 MaUiur & Gishler 47684.58 Mathur & Gishler 74287356 Mathur & Gishler 45531.38 Uemaki 39452318 Brunello 112892.8 Brunello 1.53E+08 Smith & Reddy 54546.99 Mathur & Gishler 78668313 Smith & Reddy 899380.5 Smith & Reddy 7.35E+08 Brunello 196106.9 Brunello 2.7E+08 Charlton 1083710 Charlton 9.39E+08 Charlton (k=10) 1083710 CharUon(lO) 9.39E+08

Table 5.8. Sums of squared residuals for all experimental data. Sums of squared residuals for all experimental data for Dj = 1.27 cm D(effcctive) D(average) Urn U. Uemaki 25174.1 Uemaki 51387415 Uemaki 2736.771 Uemaki 5816060 Brunello 40221.4 M&G 70149606 M&G 9551.979 Smith & Reddy 21897734 M&G 43446.49 Brunello 71762806 Smith & Reddy 16889.95 M&G 24982521 Charlton (k=10) 225516.3 Smith & Reddy 5.57E+08 Brunello 77503.95 Brunello 1.53E+08 Smith & Reddy 334297 Charlton (10) 6.25E+08 Charlton (k= 10) 225516.3 Charlton(lO) 6.25E+08 Sums of squared residuals for all Reactor A data for D; = 1.27 cm. D(effective) D(average) Urn. Uomi Umi Uomi Uemaki 17666.5 Uemaki 20636286 Uemak 1842.194 Uemaki 2151871 Brunello 31757.95 Brunello 37096545 M&G 4830.219 M&G 5642191 M&G 36820.7 M&G 43010348 Smith & Reddy 16149.31 Smith & Reddy 18864044 Charlton (k=10) 101956 Charlton (10) 1.19E+08 Brunello 56246.47 Brunello 65701649 Smith & Reddy 277352 Smith & Reddy 3.24E+08 Charlton (k=10) 101956 Charlton(lO) 1.19E+08 Sums of squared residuals for all Reactor B data for Dj = 1.27 cm. D(effective) D(average) Urn, Uomi Umi Uomi M&G 6625 M&G 27139258 Smith & Reddy 740 Smith & Reddy 3033690 Uemaki 7506 Uemaki 30751129 Uemaki 893 Uemaki 3664188 Brunello 8463 Brunello 34666261 M&G 4721 M&G 19340329 Smith & Reddy 56944 Smith & Reddy 2.33E+08 Brunello 21256 Brunello 87070629 Charlton (k=10) 123559 Charlton (10) 5.06E+08 Charlton (k=10) 123559 Charlton(lO) 5.06E+08

Table 5.9. Sum of squared residuals for data for Dj = 1.27 cm. D(effective) D(average) Urn. Uoms Ums Uoms M&G 2229.808 M&G 3623630 Smith & Reddy 5046.801 Smith & Reddy 2410364 Uemaki 2296.268 Uemaki 3902836 Uemaki 7219.587 Uemaki 31432116 Brunello 34365.93 Brunello 71798832 M&G 17886.97 M&G 49054130 Smith & Reddy 178507.9 Smith & Reddy 78319758 Brunello 58922.11 Brunello 1.02E+08 Charlton (k=10) 344771.3 Charlton (10) 1.83E+08 Charlton (k=IO) 344771.3 Charlton(lO) 1.83E+08

Table 5.10. Sum of squared residuals for data for Dj = 1.9 cm.

Ulw 00

D(effective) D(average) Urn. Uoms Ums Uoms Uemaki 1034.274 Uemaki 264774.1 Uemaki 8609.929 Uemaki 2204142 M&G 2008.283 M&G 514120.5 M&G 18092.43 M&G 4631663 Brunello 38305.43 Brunello 9806191 Smith & Reddy 32610.24 Smith & Reddy 8348222 Smith & Reddy 386575.6 Smith & Reddy 98963363 Brunello 59680.81 Brunello 15278287 Charlton (k=IO) 513422.4 Charlton (10) 1.31E+08 Charlton (k=IO) 513422.4 Charlton(lO) 1.31E+08

Table 5.11. Sum of squared residuals for data for D, = 2.54 cm. Uemaki, modified average diameter Mathur and Gishler, modified average diameter Overall data 11013.85 6810.472 1.27 cm inlet, Reactor A 5936.669 3517.557 1.27 cm inlet Reactor B 217.2 1351.33 VO 1.9 cm inlet, Reactor B 1960.54 605.4367 2.54 cm inlet. Reactor B 2899.436 1336.152

Table 5.12. Sum of squared residuals for correlations using modified average diameter. REFERENCES

Becker, H. A, 1961. An investigation of laws governing the spouting of coarse particles. Chem. Eng. Sci. 13:245-262.

Bridgwater, J. 1985. Spouted beds. Chapter 6 in Fluidization. J.F. Davidson, R. Clift, and D. Harrison (eds). Academic Press, London, pp. 201-224.

Charlton,; Morris, J. B.; Williams,. 1965. An experimental study of spouting beds of spheres. Rep. No. AERE-R4852. U.K. At. Energy Authority, Harwell, cited in Mathur and Epstein, 1974.

Epstein, N. 1983. Second international symposium on spouted beds: introductory remarks. Can. J. Chem. Eng. 61:265-266.

Ghildyal, N.P., M.Ramakrishna, B.K. Lonsane, and N.G. Karanth. 1992. Gaseous concentration gradients in tray type solid state fermentors - effect on yields and productivities. Bioproc. Eng. 9:67-72.

Khoe, G. K.; Van Brake!, J. 1983. Drying characteristics of a draft tube spouted bed. Can. j. Chem. Eng. 61:411-418.

Kmiec, A. 1983. The minimum spouting velocity in conical beds. Can. J. Chem. Eng. 61:274-280.

Kunii, D. and O. Levenspiel. 1991. Fluidization Engineering, 2d edition. Butterworth- Heinemann, Stoneham, Massachusetts, pp. 57-58.

Littman, H; Morgan, M. H. EŒ; Vukovic, D. V.; Zdanski, P. K.; Grbavcic, Z. B. 1977. A theory for predicting the maximum spoutable height in a spouted bed. Can. J. Chem. Eng. 55:497-501.

Mathur, K.B. and Epstein, N. 1974. Spouted beds. Academic Press, NY. Ramana Murthy, M.V., N.G. Karanth, and K.S.M.S. Raghava Rao. 1993. Biochemical engineering aspects of solid-state fermentation. Adv. Appl. Microbiol. 38:99-147.

Rathbun, B. L.; Shuler, M. L. 1983.Heat and mass transfer effects in static solid-substrate fermentations: design of fermentation chambers. Biotechnol. Bioeng. 38:353-362.

Uemaki, O.; Yamada, R; Kugo, M. 1983. Particle segregation in a spouted bed of binary mixtures of particles. Can. J. Chem. Eng. 61:303-307.

260 Viswanathan, K. 1986. Spouted bed drying of agricultural grains. Can. J. Chem. Eng. 64:223-232.

261 8 1.5

Length 7 1.4

6

Density 1.3 V 1.2 I I S 3 I Major width 2 Minor width

1

0 0.9 20 25 30 35 40 45 50 Moisture Content (% w/w)

Figure 5.1. The effect of moisture content on rice size and density.

262 1.3

1.28

1.26

1.24

c 1.22

y = -0.0075x+ 1.5052 1.2 R* = 0.9684

1.18

1.16 20 25 30 35 40 45 50 % Moisture Content (w/w)

Figure 5.2. Relationship between rice density and moisture content.

263 □ prespout '■postspout

32.5 39 Initial % Moisture Content (w/w)

Figure 5.3. Effect of spouting on moisture content.

264 18 28% 16 32.5% 14 ^ 39% •A' A '*39.4% 12 A' , ♦ • ? A m j : : . : * I' o 6 28% y = 0.0283x+3.1607 32.5% y = 0.0196x + 4.5248 4 39% y = 0.0189x + 4.4613

2 39.4% y = 0.0163x + 4.7083

100 150 200 250 300 350 400 450 500 550 600 Holdup (g)

Figure 5.4. Relationship between holdup and bed height.

265 28% MC

g 60

■ISO 40 ai 200 250 30 300 350 400 450 500

0 74 5060 7040 80 90 U, (cm /s)

32.5% MC 70

60

200 40 250 a t 300 30 350 400 20 450 u. 500

0070 8060 90 100 U, (cm /s)

Figure 5.5. Effect of spouting velocity and holdup on fountain height; Reactor B, 1.27 cm inlet; a) 28.8% (w/w) moisture content; b) 32.5%; c) 39%; d) 39.4%. (continued)

266 3 1 Fountain height above bed (cm) Fountain height above bed (cm) LA LA fO CO Ol OD ooooooooo 8 g s CO o $ I ; I

ro 00 3 I ÎM

(O $ (I 6

S g g

o 28% MC

- 0 - 1 5 0 - A - 200 - D - 2 5 0 - 0 —300 - # - 3 5 0 ^A-400 - # - 4 5 0 —♦—500

75 85 95 105 115 Us (cm/s)

39% MC

- 0 - 1 5 0 » 30 —Ù— 200 - 0 - 2 5 0 I 20 —0—300 - # - 3 5 0 - A —400

70 80 90 100 110 Us (cm/s)

39.4% MC S 60

- 0 —200 <0 40 - A - 250 I* 30 - 0 - 3 0 0 - 0 - 3 5 0 —# —400

85 95 105 115 Us (cm/s)

Figure 5.6. Effect of spouting velocity and holdup on fountain height; Reactor B, 1.9 cm inlet; a) 28.8% (w/w) moisture content; b) 32.5%; c) 39%.

268 28% MC

120 U, (cm/s)

60

39% MC

50

E a. 40 1 I « 30 s a 150 200 20 250

10

0 100 110 120 U, (cm/s)

Figure 5.7. Effect of spouting velocity and holdup on fountain height; Reactor B, 2.54 cm inlet; a) 28% (w/w) moisture content; b) 32.5%.

269 80

70

-g- 60

1 50 ni | 4 0 - * — 200 g, 25% IMC S> —# —200 g, 30% IMC £ - A —200 g, 35% IMG c 30 —■ —200 g. 40% IMG - * - 4 0 0 g, 25% IMG 20 —A—400 g. 30% IMG —0 —400 g 35% IMG 10 - 0 - 4 0 0 g, 40% IMG

55 65 75 85 95 U, (cm/s)

Figure 5.8. Effect of moisture content and holdup on fountain height.

270 120

37.2% IMC

100 36.2% IMG

80 26.6% IMC

I J

40

Reactor A

6 7 89 10 11 12 13 14 15 16 Bed Height (cm)

Figure 5.9. Effect of bed height on minimum spouting velocity. Reactor A, 1.27 cm inlet.

271 100

80

J 28% IMC 32.5% IMC 39% IMC 39.4% IMC

6 7 8 95 10 11 12 13 14 15 Bed Height (cm)

120

100

80 I 60 DE 40 28% IMC 20 32.5% IMC 39% IMC 0 5 6 7 8 9 10 1211 13 14 15 Bed Height (cm)

120

100

! =■ 28% IMC 32.5% IMC 39% IMC

5 6 7 8 9 10 11 12 13 14 15 Bed Height (cm)

Figure. 5.10. Effect of bed height on minimum spouting velocity. Reactor B, a) 1.27 cm inlet, b) 1.9 cm inlet, c) 2.54 cm inlet.

272 4500 350 a) 4000 28% IMC 300 3500 -0.5" inlet. Uoms •=. 3000 -0.75" Inlet, Uoms 250 -1.0" inlet, Uoms ? E u 2500 -0.5" inlet. Urns 200 I I 2000 S -0.75" inlet. Urns 150 I = 1500 -1.0" inlet. Urns 100 1000 500 50

10 11 12 13 14 15 16 17 Bed Heigtit (cm)

6000 350 b) 32.5%% IMC 300 5000 5" inlet, Uoms ,75" inlet, Uoms 250 4000 .0" inlet, Uoms ? 1 5” inlet. Urns 200 ^ a 3000 75" inlet, Ums S 150 I 0" inlet, Ums =» 2000 100

1000 50

10 11 12 13 14 15 16 17 Bed Heigtit (cm)

6000 350 c) 5000 39% IMC 300 0.5" inlet, Uoms 0.75" inlet, Uoms 250 4000 1.0" inlet, Uoms «■ E 0.5" inlet, Ums 200 1 ■Ü 3000 u 0.75" inlet, Ums 150 1 1.0" inlet, Ums = 2000 100

1000 50

10 11 12 13 14 15 16 17 Bed Height (cm)

Figure 5.11. Effects of inlet diameter and bed height on minimum superficial spouting velocity and minimum inlet superficial spouting velocity for various moisture content rice in Reactor B.

273 5 4.5 iReactor A. 26.6% MC

L s t : r i 0.5 0 50 60 70 80 90100 110 120 130 140 150 U, (cm/s)

5 4.5 Reactor A, 36.2% MC

l a s 0 9 cm 012.25 cm IJ A lS cm

< 1 0.5 0 50 60 70 80 90 100 110 120 130 140 150 U, (cm/s)

5 4.5 Reactor A, 37.2% MC

I s a ♦ 9 cm □ 12.25 cm U A15 cm I.: < 1 0.5 0 50 60 70 80 90 100 110 120 130 140 150 U, (cm/s)

Figure 5.12. Effect of spouting velocity on annular speed at various bed heights. Reactor A; a) 26.6% moisture content (w/w), b) 36.2% moisture content (w/w), c) 37.2% moisture content (w/w).

274 Reactor A 4.5 •

3.5 026.6% MC □ 36.2% MC A 37.2% MC o. 2.5

1.5

0.5

50 60 70 80 90 100 110 120 140 150130 U, (cm/s)

Figure 5.13. Overall relationship between annular speed and spouting velocity. Reactor A.

275 28% IMC

9.6 cm 11.2 cm

12.9 cm

60 65 70 75 80 85 U, (cm/s)

Figure. 5.14. Linear relationships between annular speed and spouting velocity for particular bed heights, Reactor B, 1.27 cm inlet.

276 4.5 y = 0.0867x-3.5693 R* = 0.7457

028% IMC lô □ 32.5% IMG a c A 39% IMG < 039.4% IMG 0.5

60 65 70 75 80 85 90 95 U, (cm/s)

3.5

y = 0.0617x-4.1869 E 2.5 R* = 0.5407

1.5

028% IMG □ 32.5% IMG 0.5 A 39% IMG

60 80 90 100 110 12070 U, (cm/s)

1.4

1.2 y = 0.0271X - 2.0479 R^ = 0.5134

« 0.6

0.4

0.2 32.5% IMG

90 95 100 105 110 115 120 U, (cm/s)

Figure. 5.15. Effect of spouting velocity on annular speed at various moisture contents, Reactor B; a) 1.27 cm inlet, b) 1.9 cm inlet, c) 2.54 cm inlet.

277 6

1.27 cm inlet 5

O

l a .9 cm Inlet (0 .23 C

□ O 1

2.54 cm inlet 0 60 70 80 90 100 110 120 U, (cm/s)

Figure 5.16. Effect of inlet diameter and spouting velocity on annular speed. Reactor B.

278 60

50

40

HO) 30 y = 9.1589x-65.662 R* = 0.9524 20

10

0 911 1210 13 Bed Height (cm)

Figure. 5.17. Relationship between mixing time and bed height, Reactor B, 1.27 cm inlet, 28% moisture content rice.

279 O modified average diameter. Uemaki A width average diameter, I Mathur & Gishler a « A width average diameter, e Uemaki ^ o o

O E o

Overali best nt correiations, all data

40 60 80 120 Experimental Um« (cm/s)

Figure. 5 .18. Best overall fit of correlations to experimental data.

280 150

A width average diameter, Uemaki O modified average diameter. Uemaki o. 100

50

Reactor A

0 0 20 40 60 80 100 120 Experimental Ums (cm/s)

Figure. 5.19. Best fit of correlations to Reactor A data.

281 100

I O width average g 70 diameter, Smith & RedcV « •modified average diameter, Mathur & S Gishler u A width average diameter, Uemaki I Reactor B. 1.27 cm inlet 3I

0 50 100 Experimental Um, (cm/s)

150

■effective diameter, I Mathur & GIshier O o □effective diameter, 0 Uemaki u O modified average 1 diameter, Uemaki I 3 Reactor B, 1.9 cm Inlet • modified average diameter, Mathur & Gishler 0 30 90 120 15060 Experimental 11^, (c/s)

140 u 120 ■ effective diameter, 5 100 Mathur & GIshier □ effective diameter, Uemaki 60 O modified average diameter, Uemaki Reactor B, 2.54 cm Inlet

0 50 100 150 Experimental (cm/s)

Figure. 5.20. Best fit correlations to Reactor B data; a) 1.27 cm inlet; b) 1.9 cm inlet; c) 2.54 cm inlet.

282 CHAPTER 6

CONCLUSIONS AND RECOMMENDATIONS

CONCLUSIONS

The major conclusion arising from this work is that it is possible and advantageous to conduct certain SSF processes in a spouted bed reactor. Compared to the reactor type currently considered to give the best results, the packed bed reactor with forced aeration, the spouted bed bioreactor yielded equivalent amounts of protein while demonstrating superior operational properties when operated under conditions of intermittent spouting, once every four hours. Unlike in the packed bed reactor, the SSF substrate remained free-flowing and uniform in the spouted bed bioreactor. This will provide a solids-handling advantage and should make the spouted bed bioreactor attractive for industrial use.

Several other conclusions were made during the course of the investigation.

These are described in previous chapters and are summarized here as well.

283 Static Fermentation Conclusions

1. Within the range of 1.45x10^ to 1.45x10^ spores, the quantity of inoculum used for

static SSF does not affect enzyme production or other fermentation kinetics in the

cultivation of A. oryzae on brown rice.

2. Initial pH had a strong effect on protein and enzyme production; for the range studied,

decreasing pH caused dramatic decrease in growth and production.

3. Initial moisture content had a strong effect on fermentation kinetics. Within the range

studied, an initial moisture content of 29% provided superior protein and enzyme

production.

4. Between the temperatures of 25 and 45°C, fastest initial protein and enzyme

production was observed for the range of 30 - 35°C. The highest final protein and

enzyme concentrations were observed for those cultures grown at 37°C.

5. Supplementation of brown rice with yeast extract promoted the production of protein

and enzyme in a SSF of A oryzae on brown rice.

6. Of the five substrates tested — rice, wheat, oats, barley, and lentils - lentils yielded the

highest levels of protein and a-amylase activity.

7. Sporulation was related to enzyme production in a complex manner. Nutrient

supplementation and low temperatures both promoted sporulation but had opposite

effects on enzyme production. Further work is necessary to elucidate this

relationship.

Spouted Bed Bioreactor Fermentations

1. It was possible to conduct a SSF process in a spouted bed bioreactor.

284 2. Operating strategy affected the performance of the spouted bed bioreactor.

Intermittent spouting was preferred to continual spouting, and performance improved

as the spouting interval was increased from one to four hours. Enzyme productivity

increased as the spouting interval increased.

3. A packed bed fermentation, equivalent to an intermittently spouted fermentation with

an interval of infinity, showed similar protein and enzyme kinetics profiles to the 4

hour interval intermittent spouted bed fermentation, but also had nonhomogeneous

enzyme production within the bed and displayed binding together of the substrate by

the fungal mycelia, resulting in difficult product harvesting and reactor cleaning.

4. A gas-solid spouted bed bioreactor had the particular advantage over packed bed

bioreactors in that it provided equivalent enzyme productivity while maintaining ease

of solids handling and uniformity within the reactor.

Reactor Hydrodynamics

1. Rice of moisture contents suitable to SSF can be spouted.

2. Because moisture content affected the size and density of the rice kernels, it also

affected the minimum spouting velocity. As moisture content increased, so did Ums-

The highest moisture content rice (-45%, w/w) was unspoutable.

3. Increased inlet diameter from 1.27 cm to 2.54 cm caused reduced spouting stability

and required increased Ums-

4. Fountain height and linear annulus speed both increased with increased superficial

spouting velocity. Linear correlations could be developed for a given set of reactor

parameters (moisture content, holdup, inlet diameter).

285 5. The Uemaki correlation combined with the modified average diameter method for

determining particle diameter was shown to be the most suited to predicting the Ums

required for the experimental system.

RECOMMENDATIONS

This research provided many answers; it also provided many questions. Rather than list every small query here, general areas that are expected to be fruitful for research are recommended.

1. Only one organism was tested in the spouted bed. It is highly unlikely that this was

the optimal organism for SSF. Screening of various organisms previously shown to

grow well in SSF should be undertaken to find those that especially benefit form the

advantages of SSF.

2. In addition to investigating other organisms, the benefits of other substrates should

also be studied. This includes studying nutrient supplementation.

3. Reactor design must be improved to overcome operational difficulties not related to

the SSF process; in particular, the humidification and temperature control systems

should be redesigned to allow better control of these parameters.

4. Further study of the hydrodynamics of the system will provide a stronger basis for

design of larger reactors. In particular, deeper beds should be studied to determine the

feasibility of scaling up the spouted bed solid state fermentation process.

5. SEM examination of the cell morphology and distribution is recommended to provide

insight into the effects of the various reactor systems.

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300 APPENDIX A

ANALYTICAL FLOWSHEET AND CALCULATIONS

301 Koji Sample

0.4 ml 0.5 ml AI g A2 g 100 ml HgO A3 g YSI glucose Tared Pan, B1 g Extract w/ lOx (v/w) water 2 ml 0 2 . g/L B2 ml crude extract Blend, C l g, total weight starch DNSA, reducing 1 minute substrate sugars, 0 2 g/L 103°Coven, overnight 0.5 ml 0.5 0.5 ml extract, 02 2 ml 0 .1% starch 0 1 g, dry weight dilution factor 1 .5 ml HgO YSI glucose 37°C B3, g/L Determine Dry Weight (Calculation #1) 0.25 ml m (imed Intenrals 10 ml, 37% HQ of digest 1

0.25 ml Iodine sol'n Autoclave, 30 mln Ice Bath YSI glucose 12 minutes to endpoint H2, g& YSI glucose, 03 g/L Determine «Amylase, colorimetric Determine etareh, (Calculation #2) ateimlne glucoamylase r total sugara, (Calculation #4) (Calculation # 6 extract extract, x ml (see methods) DNSA Procedure for reducing sugar determination, 12 (A 5 3 0 ) Microplate Reader (convert to reducing sugar w/ standard, N2 g/L) 37 ®C, read at 405 nm * run starch sol'n L2 g/L * find YSI glucose for starch sol'n, J2 g/L A405 at 2 mln, E2 * find YSI glucose for hydrolyzed starch, K2 g/L A4 0 5 at 4 mln, F2 * find YSI glucose for blank digest, M2 g/L ______

Determine «-Amylase, kinetic Determine Protein Determine p-Amylase (Calculation #3) (Use standard cun/e) Calculation #5)

Figure A. 1. Flowslieet of analytical procedures performed on each koji sample; bolded letters/numbers indicate data points taken. Calculations for Koji Analysis

1 j Diy weight

C l - 01 C l - B1 = d'y weight, gfg

0 a-Amylase - colorimetric

1800 * 02 0 2 ------= activity, U/L

a-Amylase - kinetic

F2 - E2 •0.130 ml • 1000 * 5/4 . = activity, U/L 9.1008 cm'^ * 0.338 cm * 0.05 ml

# 4 Qucoamylase

H2 - M2 - O.S‘G2 0 .5 = activity, U/L

# 5 |J-Amylase

(N2*6 - 02*0.5 - L2)‘2*500 (K 2-L2)*2 = activity. U/L

#6 I Starch

(03 • 1.1 • B3) • 0.1 A3 = starch, g/g koji

Sugars

03*1.1*0.1 A3 = sugars, g/g koji

Figure A.2. Calculations for analysis of koji samples; variables taken from flowsheet in Figure A.I.

303 APPENDIX B

PROTEIN REAGENTS USED FOR ANALYSIS

304 Experiment Inoculum Protein Standard Absorbance Protein Slope of Standard Curve Source (mg/ml) (595 nm) Reagent (mg protein/unit absorbance) Organism slant 0.5 .21 #1 — Inoculum Size slant 0.4 .262 #1 2.0269 Initial pH slant 0.4 .25 #1 1.4879 Moisture Content slant 0.4 .37 #1 1.3762 Temperature #1 slant 0.4 .196 #2 2.129 Temperature #2 rice — — #2 — Bed Depth #1 rice 0.4 0.219 #2 — Bed Depth #2 rice 0.5 .51 #3 1.152 Bed Depth #3 rice 0.5 .552 #3 .71195 slant 0.4 w Alternative Grain .219 #2 1.9771 s Nutrient Supplement slant 0.4 .218 #2 1.8986

Table B. 1. Protein reagent used for various experiments and effect on slope of standard curve. APPENDIX C

CORRELATIONS FOR MINIMUM SPOUTING VELOCITY

306 2 5 0 Deff, 1.38 g/on®, 1.27 cm inlet

Smith & Reddy 200

150 Mathur & Chariton I Uemaki

100 Experimental

50 Bruneiio

0 5 68 94 10 11 127 Bed Height (cm)

160

Chariton 140 D „ , 1.38 g/cm’, 1.27 cm ^ . 120 Smith & Reddy______100 Experimental

80 Uemaki

60 Mathur & Gishier

40 Bruneiio 20

0 4 5 6 7 8 9 10 11 12 Bed Height (cm)

Figure C. 1. Correlations for minimum spouting velocity compared to experimental data; Reactor A, 9.8% moisture content (w/w); a) using Deff in calculation, b) using D, avc in calculation.

307 250 Smith & Reddy

Charlton 200

Drt, 1.28 g/cm®, 1.27 cm inlet Mathur & Gishier 150 I Üêmaki J 100 Experimental

50 Bruneiio

0 6 7 a 9 10 11 12 13 14 15 16 Bed Height (cm)

200 . ' "Charlton 180

160 Dave. 1.28 g/cm', 1.27 cm

140 Smith & Reddy 120

Uemaid 80 iperimentai iatturr & Gishier 60

40 Bruneiio

6 7 8 9 10 11 12 13 14 15 16 Bed Height (cm)

Figure C.2. Correlations for minimum spouting velocity compared to experimental data; Reactor A, 26.6% moisture content (w/w); a) using Deff in calculation, b) using Da in calculation.

308 2 5 0

Smith & Reddy Charlton

200 D^, 1.27 g/cm . 1.27 cm inlet

Mathur & Gishier

150 I

100 Experimental

Bruneiio

6 8 9 10 11 12 13 147 15 16 Bed Height (cm) 250

Dart, 1.27 g/cm . 1.27 cm inlet Smith & Reddy 200

150 Charlton ! 3I 100 Uemi

Mathur & Gishier

Expérimentai

Bruneiio

6 7 8 9 10 11 12 13 15 1614 Bed Height (cm)

Figure C.3. Correlations for minimum spouting velocity compared to experimental data; Reactor A, 36.2% moisture content (w/w) ; a) using Deff in calculation, b) using Da in calculation.

309 3 0 0

D,a. 1-23 g/cm’, 1.27 cm inlet Smith & Reddy 250

Chariton

200

Mathur & Gishier a, 150 'U iem 'ak i

too Experimental

Bruneiio

67 8 9 10 11 12 13 14 15 16 Bed Height (cm)

250

Chariton Da,,, 1.23 g/cm’, 1.27 cm inlet 200

150 Smith & Reddy

I Experimental J 100 Uemaid Mathur & Gishier

50

Bruneiio

6 7 8 9 10 11 12 13 14 15 16 Bed Height (cm)

Figure C.4. Correlations for minimum spouting velocity compared to experimental data; Reactor A, 37.2% moisture content (w/w) ; a) using Deff in calculation, b) using Da in calculation.

310 1 8 0 Charlton 160 Drtt. 1.27 cm inlet, 1,287 g/cm

140 Smith & Reddy

120

I 100 u Uemaki Mathur & Gishier 3I Experimental

. Bruneiio

7 8 9 10 11 12 Bed Height (cm)

180

160 Dave, 1.27 cm inlet. 1.287 g/cm Chariton

140

120

I too Uemaki Smith & Reddy u Experimental 3I

Mathur & Gishier 40 Bruneiio

7 8 9 10 11 12 Bed Height (cm)

Figure C.5. Correlations for minimum spouting velocity compared to experimental data; Reactor B, 28% moisture content (w/w), 1.27 cm inlet; a) using Deff in calculation, b) using Dave in calculation.

311 2 5 0

Charlton Drt, 1ZI cm inlet, 1.27 g/cm 200

150 .Smith & Reddy

I .Mathur & Gishier I ^ 100 Uemaid

Experimental

Bruneiio

8 9 10 11 12 13 14 15 16 Bed Height (cm)

250

Da«. 1.27 cm inlet, 1.27 g/cm 200 Charlton

150 I Experiment 100 Smith & Reddy Uemaki

50 Mathur & Gishier Bruneiio

0 a 9 10 11 12 13 14 15 16 Bed Height (cm)

Figure C.6. Correlations for minimum spouting velocity compared to experimental data; Reactor B, 32.5% moisture content (w/w), 1.27 cm inlet ; a) using Deff in calculation, b) using Dave in calculation.

312 3 0 0 * ' Chariton

D,b, 1.29 g/cm®. 1.9 cm inlet 250

Smith & Reddy

200 -

a ISO Uemaki Mathur & Gishier

100 Experimental

Bruneiio

6 7 8 9 10 11 12 13 14 Bed Height (cm)

300 Chariton

250 Dm . 1.29 g/cm®. 1.9 cm iniet

200

a 150 Smith & Reddy

100 Experiments Uemaid

Mathur & Gishier

Bruneiio

6 7 8 9 10 11 12 13 14 Bed Height (cm)

Figure C.7. Correlations for minimum spouting velocity compared to experimental data; Reactor B, 28% moisture content (w/w), 1.9 cm inlet ; a) using Deff in calculation, b) using Dave in calculation.

313 3 0 0

Chariton 250 D^, 1.27 g/cm’, 1.9 cm inlet Smith & Reddy

200

J 2 , 150 Mathur & Gishier Uemaki

100 Expérimenta

50 Bruneiio

0 6 78 9 10 11 12 13 Bed Height (cm)

300

Djve. 1.27 g/cm’, 1.9 cm inlet Chariton 250

200

& 150 Smith & Reddy

100 Experiment Uemaki

50 lishler Bruneiio

6 7 a 9 10 11 12 13 Bed Height (cm)

Figure C.8. Correlations for minimum spouting velocity compared to experimental data; Reactor B, 32.5% moisture content (w/w), 1.9 cm inlet; a) using Deff in calculation, b) using Dave in calculation.

314 3 0 0 SmflhlBedây

250

200 DaH. 1.29 g/cm®, 2.54 cm Inlet

1 , 5 0 s' Uemaki Expérimentai ^ , 1 100 Matnur & Gishier

50 "Bruneiio

7.5 Bed Height (cm)

300 Chariton

250 ■

b) Daw. 1.29 g/cm’, 2.54 cm iniet

200 -

I &nith_& Reddx, “ ,150 J Experimental

100 - Uemaki

50 Mathur & Gishier

"* "Bruneiio

7.5 Bed Height (cm)

Figure C.9. Correlations for minimum spouting velocity compared to experimental data; Reactor B, 28% moisture content (w/w), 2.54 cm inlet ; a) using Deff in calculation, b) using Dave in calculation.

315 4 5 0 Chartton De*. 1.27 g/cm', 2.54 cm inlet 400

350 Smith & Reddy 300

I 250 O. 200 J Uemaki Mathur 4 Gishier 150

100 Expenmental

Bruneiio

7 8 9 10 11 12 13 14 Bed Height (cm)

450 tjj Dave, 1.27 g/cm'. 2.54 cm inlet Charlton 400

350

300

|2 5 0 a 3^00 Smfti 4 Reddy 150 Uemaki

: . / . ----A------A------100 V — ♦ * ------j ------^ txpenmentai

so —------— Mathur & Gishier

0 10 11 12 13 14 Bed Height (cm)

Figure C. 10. Correlations for minimum spouting velocity compared to experimental data; Reactor B, 32.5% moisture content (w/w), 2.54 cm inlet; a) using Deff in calculation, b) using Dave in calculation.

316