Investigative Study of Microalgal and Electrochemical Wastewater Treatment Systems and Modeling of the Wafer-Enhanced Electrodeionization Using Supervised Learning

University of Arkansas, Fayetteville ScholarWorks@UARK

Graduate Theses and Dissertations

5-2021

Humeyra Betul Ulusoy Erol

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A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering with a concentration in Chemical Engineering

Humeyra Ulusoy Erol Ege University Bachelor of Science in Bioengineering, 2013

May 2021 University of Arkansas

This dissertation is approved for recommendation to the Graduate Council.

Jamie A. Hestekin, Ph.D. Chair of Committee

Christa N. Hestekin, Ph.D. David M. Ford, Ph.D. Co-Chair of Committee Committee Member

Wen Zhang, Ph.D. Julian Fairey, Ph.D. Committee Member Committee Member

Abstract

Wastewater has a serious impact on environment and public health due to its high concentration of nutrients and toxic contaminants. Without proper treatment, excess nutrients discharged in wastewater can cause a damage to the ecosystem such as undesirable pH shifts, cyanotoxin production, and low dissolved oxygen concentrations.

Main objectives of this dissertation work were to investigate i) the biofuel potential of P. cruentum when grown in swine wastewater, ii) the influence of four most commonly used ion exchange resins on the system efficiency and selectivity for the removal of sodium, calcium, and magnesium ions, and iii) the modeling of wafer-enhanced electrodeionization with data science and machine learning techniques.

The growth and lipid production of the microalgae Porphyridium (P.) cruentum grown in swine wastewater (ultra-filtered and raw) were examined as compared with control media (L−1, modified f/2) at two different salt concentrations (seawater and saltwater). The cultivation of P. cruentum in the treated swine wastewater media (seawater = 5.18 ± 2.3 mgl−1day−1, saltwater =

3.32 ± 1.93 mgl−1day−1) resulted in a statistically similar biomass productivity compared to the control medium (seawater = 2.61 ± 2.47 mgl−1day−1, saltwater = 6.53 ± 0.81 mgl−1day−1) at the corresponding salt concentration. Furthermore, no major differences between the fatty acid compositions of microalgae in the treated swine wastewater medium and the control medium were observed. The performance comparison of four commonly used cation exchange resins (Amberlite

IR120 Na+, Amberlite IRP 69, Dowex MAC 3 H+, and Amberlite CG 50) and their influence on

the current efficiency and selectivity for the removal of cations from a highly concentrated salt stream were also reported in this work. The current efficiencies were high for all the resin types studied. Results also revealed that weak cation exchange resins favor the transport of the monovalent ion (Na+) while strong cation exchange resins either had no strong preference or preferred to transport the divalent ions (Ca2+ and Mg2+). Moreover, the strong cation exchange resins in powder form generally performed better in wafers than those in the bead form for the selective removal of divalent ions (selectivity > 1). To further understand the impact of particle size, resins in the bead form were ground into a powder. After grinding the strong cation resins displayed similar behavior (more consistent current efficiency and preference for transporting divalent ions) to the strong cation resins in powder form. This indicates the importance of resin size in the performance of wafers.

Through this research, the modeling of wafer-enhanced electrodeionization with high concentration multi-ion solution has been accomplished. This paper is the first study that uses data science and machine learning techniques for the modeling of wafer-enhanced electrodeionization with high concentration multi-ion solutions. With the use of data science and machine learning, the sodium, calcium, and magnesium ion concentrations were predicted with multioutput regression and neural networks multilayer perceptron (NN-MLP), and the observed effects of different resin wafers were confirmed using both multioutput and single output regression as well as leave-one-out cross validation and NN-MLP.

Acknowledgements

The process of earning a doctorate and writing a dissertation is long and arduous – and it is certainly not done singlehandedly. First and foremost, I would like to thank my husband and family for putting up with me and all my quirks and always being my side when times I needed them most during this process. Recep has been unfailingly supportive on both shouldering the responsibilities of our family and mentoring me in the data science chapters. Without my family’s constant support, encouragement, and understanding, it would not have been possible for me to achieve my educational goals.

I would certainly be remiss to not mention and sincerely thank my advisors and mentors, Dr.

Christa Hestekin and Dr. Jamie Hestekin. Without their help, advice, expertise, and encouragement, this research and dissertation would not have happened. I would also like to thank the other members of my dissertation committee: Dr. David Ford, Dr. Julian Fairey, and

Dr. Wen Zhang. Their insight, feedback, and advice were influential and essential throughout the dissertation writing process.

I would also like to thank my many friend and colleagues in the chemical engineering department, especially Jessica, Raheleh, and Zahra for a cherished time spent together in the office, conferences, and in social settings.

I would like to acknowledge the many undergraduate students who assisted me on the projects, and who I had the pleasure to mentor during their studies – Catey Atchley, Kayvan

Afrasiabi, Benjamin Drewry, and Cody Bossio.

Special thanks would like to be awarded to Membrane, Science, Engineering, and

Technology (MAST) Center and American Association of University Women (AAUW) for their support and funding for this degree.

Last but not least, I would like to thank me for believing in me, for doing all this work, for never quitting, and for just being me at all times.

Dedication

To my loving husband, Recep Erol, for providing unending support through my graduate career and beyond.

Table of Contents

Chapter 1: Introduction ...... 1 Purpose and Significance ...... 6

References ...... 9

Chapter 2: Literature Review ...... 11 2.1. Introduction to Microalgae and Algal Biofuels ...... 11

2.2. Wastewater Treatment with Microalgae Cultivation ...... 15

2.3. Overview of Ultrafiltration ...... 17

2.4. Electromembrane Processes ...... 19

2.5. Theoretical Background: Transport in Ion Exchange Membranes ...... 31

2.6. WE-EDI Performance Indicators ...... 32

2.7. Overview of the Principles of Big Data Analytics and Machine Learning...... 33

References ...... 37

Chapter 3. Porphyridium cruentum Grown in Ultra-Filtered Swine Wastewater and Its Effects on Microalgae Growth Productivity and Fatty Acid Composition...... 43 3.1. Abstract ...... 45

3.2. Introduction ...... 45

3.3. Materials and Methods ...... 49

3.4. Results and Discussion ...... 53

3.5. Conclusion and Future Perspectives ...... 56

References ...... 58

Chapter 4. Effects of Resin Chemistries on the Selective Removal of Industrially Relevant Metal Ions Using Wafer-Enhanced Electrodeionization ...... 61 4.1. Introduction ...... 62

4.2. Materials and Methods ...... 66

4.3. Results and Discussion ...... 70

4.4. Conclusion ...... 82

References ...... 85

Chapter 5. Modeling of Wafer-Enhanced Electrodeionization using Data Science and Machine Learning Techniques ...... 89 5.1. Introduction ...... 89

5.2. Data Collection and Cleansing ...... 92

5.3. Methodology ...... 94

5.4. Experiments ...... 105

5.5. Conclusions...... 113

References ...... 115

Chapter 6. Conclusion ...... 118

Table of Figures

Chapter 1:

Figure 1: Illustration of selective wafer-enhanced electrodeionization process ...... 4

Chapter 2:

Figure 1: Energy conversion processes from microalgae [13]...... 15

Figure 2: The filtration spectrum of various filtration methods relative to sizes of common matters [24] ...... 18

Figure 3: Schematics of how ultrafiltration works ...... 19

Figure 4: Illustration of the structure of a cation-exchange membrane [29] ...... 21

Figure 5: Diagram of a two-compartment electrodialysis stack (C): concentrate, (D): diluate compartments [40]...... 23

Figure 6: Ion transport and electrochemical regeneration in an EDI cell [76] ...... 28

Chapter 3:

Figure 1: Total biomass productivity of each culture in mgl−1 day−1 repeat. C = control media, SW = swine wastewater media, UF = purified by ultrafiltration, RW = raw swine wastewater added after ultrafiltration, SEA = seawater, SALT = saltwater, N = 3...... 54

Figure 2: Fatty acid methyl esters (FAME) composition of P. cruentum grown in control and swine wastewater media. N = 2 for C-SEA and SW-UF-SEA, N = 1 for C-SALT and SW-UF- SALT...... 55

Figure 3: Saturated and unsaturated fatty acid compositions of P. cruentum grown in control and treated swine wastewater media. N = 2 for C-SEA and SW-UF-SEA, N = 1 for C-SALT and SW-UF-SALT...... 56

Chapter 4:

Figure 1: Illustration of typical wafer fabrication and particle size reduction (grinding) of ion exchange resins for wafer fabrication...... 68

Figure 2: Illustration for wafer-enhanced electrodeionization (EDI) setup ...... 69

Figure 3: Overall current efficiencies for strong (IR 120 Na+ and IRP 69) and weak cation exchange wafers (Dowex MAC 3 H+ and CG 50)...... 71

Figure 4: Comparison of selectivity values of calcium and magnesium relative to sodium for different strong cation exchange (IR 120 Na+ and IRP 69) and weak cation exchange (Dowex MAC 3 H+ and CG 50) resin wafers...... 73

Figure 5: The FTIR-ATR spectrum of strong cation exchange resins and wafers including these resins...... 75

Figure 6: The FTIR-ATR spectrum of IR 120 Na resin alone and in two different wafers. ... 77

Figure 7: The FTIR-ATR spectrum of weak cation exchange resins and wafers formed using these resins...... 78

Figure 8: The FTIR-ATR spectrum of Dowex MAC 3 H+ resin alone and in two different wafers...... 79

Figure 9: Optical microscopy images of (a) unground IR 120 Na+ resin and (b) ground IR 120 Na+ resin ...... 80

Figure 10: Current efficiencies for unground bead form IR 120 Na+ and ground IR 120 Na+ 81

Figure 11: Selectivity of the unground IR 120 Na+ and the ground IR 120 Na+...... 82

Chapter 5:

Figure 1: Model selection pipeline for machine learning algorithms ...... 95

Figure 2: Operational model of supervised learning...... 96

Figure 3: Diagram of linear regression overview [19]...... 97

Figure 4: Comparison of the sizes of toy datasets of scikit-learn and WE-EDI...... 100

Figure 5: Illustration of leave-one-out cross validation [24] ...... 101

Figure 6: Neural network structure ...... 103

Figure 7: Comparison of actual and predicted sodium concentrations for the Run01 ...... 106

Figure 8: Comparison of actual and predicted calcium concentrations for the Run01 ...... 107

Figure 9: Comparison of actual and predicted calcium concentrations for the Run01 ...... 107

Figure 10: Mean squared error scores for the Run 01 ...... 108

Figure 11: Comparison of actual and predicted current efficiency values ...... 109

Figure 12: The mean squared error scores for the Run02 ...... 110

Figure 13: Comparison of actual and predicted sodium concentrations for the Run03 ...... 111

Figure 14: Comparison of actual and predicted calcium concentrations for the Run03 ...... 112

Figure 15: Comparison of actual and predicted magnesium concentrations for the Run03 .. 112

Figure 16: Mean squared error scores for the Run03 ...... 113

Chapter 6:

Figure 1: Future outlook of completed research progress. The top squares indicate the work presented in Chapters 3, 4, and 5. Each square below the top briefly describes subsequent research projects that can be developed as a result of the work accomplished...... 120

List of Tables:

Chapter 1:

Table 1: Outline of Dissertation ...... 7

Chapter 2:

Table 1: Comparison of properties of petroleum-derived fuels and biofuels. Derived from [1, 2, 3]...... 12

Table 2: Lipid content of some microalgae. Adapted from [9] ...... 14

Table 3: Wastewater types and nutrients characterizations for microalgae cultivation. Adopted from [18] ...... 17

Chapter 3:

Table 1: Microalgae growth media compositions...... 51

Table 2: Statistical analysis of biomass productivity for different growth conditions...... 54

Chapter 4:

Table 1: Cation exchange resins and their properties...... 66

Chapter 5:

Table 1: Input Variables of Wafer-Enhanced EDI model ...... 93

Table 2: Other parameters of WE-EDI model ...... 93

Table 3: Output variables for WE-EDI modeling ...... 94

Table 4: Configurations for the Run01 ...... 106

Table 5: Configurations for Run02 ...... 109

Table 6: Configurations for Run03 ...... 111

List of Publications

Chapter 3 - Published: “Ulusoy Erol, H.B.; Menegazzo, M.L.; Sandefur, H.; Gottberg, E.; Vaden, J.; Asgharpour, M.; Hestekin, C.N.; Hestekin, J.A. Porphyridium cruentum Grown in Ultra-Filtered Swine Wastewater and Its Effects on Microalgae Growth Productivity and Fatty Acid Composition. Energies 2020, 13, 3194”

Chapter 4 - Published: “Ulusoy Erol, H.B.; Hestekin, C.N.; Hestekin, J.A. Effects of Resin Chemistries on the Selective Removal of Industrially Relevant Metal Ions Using Wafer-Enhanced Electrodeionization. Membranes 2021, 11, 45. https://doi.org/10.3390/membranes11010045”

Chapter 1: Introduction

Wastewater has a serious impact on environment and public health due to its high concentration of nutrients and toxic contaminants [1]. Without proper treatment, excess nutrients discharged in wastewater can cause a damage to the ecosystem such as undesirable pH shifts, cyanotoxin production, low dissolved oxygen concentrations, and fish kills [2]. Two of technologies that can remove excess nutrients, particulates, organic, ionic and gaseous contaminants from the aqueous streams reliably and economically without the application of hazardous chemicals [1] include microalgae and wafer-enhanced electrodeionization.

Some of the wastewater types are livestock, human sewage, swine waste, fracking waste, and other agricultural wastes which are mixtures of organic and inorganic materials as well as synthetic compounds [3]. The major organic carbon in sewage are in the form of carbohydrates, fats, proteins, amino acids and volatile acids [3]. The inorganic materials in wastewater are high concentrations of monovalent and divalent ions such as sodium, calcium, potassium, magnesium, sulfur, phosphate, bicarbonate as well as ammonium salts and heavy metals [3]. Microalgae can be used in wastewater treatment for various different purposes including the removal of nitrogen, phosphorus, heavy metals, and some bacteria [4], and this high concentrations of nitrogen and phosphorus make the wastewater suitable as algae growth media for biofuel production while removing nutrients. Hence, the microalgae that grow on wastewater have a significant potential to be used as feedstock for biofuel production. There have been many reported cases of using microalgae to clean up wastewater, including

Chlorella vulgaris for the clean-up of wastewater from ethanol and citric acid production [5],

Chlorella vulgaris for the biodegradation of hydrocarbons [6], Nannochloris sp. for the treatment of trimethoprim, sulfamethoxazole, and triclosan [7]. While there have been numerous studies on the nitrogen and phosphorous removal abilities of microalgae on wastewater, and numerous studies on the growth of algal biomass on wastewater, there have been limited studies evaluating the fatty acid composition of the microalgae grown in swine wastewater. Since there are many different types of wastewater, here the focus will be on swine wastewater and the products produced, as compared with conventional growth media.

Several previous studies have explored the growth rates of different microalgae species on swine wastewater, such as Scenedesmus intermedius (0.014 mg chlorophyll h−1), Nannochloris sp.

(0.011 mg chlorophyll h−1) [8], and Chlorella vulgaris (40 mgl−1day−1) [9]. However, some studies have also evaluated the fatty acid content of microalgae grown in swine waste. Hu et al. (2012) compared the growth of Chlorella sp. in fresh and anaerobically digested swine wastewater [10].

They found a growth of 75.7 mgl−1day−1 in raw diluted swine wastewater and a growth of 164.3–

224.7 mgl−1day−1 in diluted swine wastewater supplemented with volatile fatty acids (acetic, propionic, and butyric). Depending on the amount of volatile fatty acids added, the fatty acid composition was ~43–57% saturated, ~8–10% monosaturated, and 35–48% polyunsaturated fatty acids. However, the fatty acid composition was not determined for microalgae grown in the raw diluted swine wastewater, which was the medium for the study below. In a study by Mulbry et al.

(2008), the microalgae were grown in raw swine wastewater at different effluent loadings and had a growth of 6.8–10.7 gm−2 day−1 [17]. They found that the dominant algal species was

Rhizoclonium sp. with a fatty acid composition of ~53–58% saturated, 16–20% monosaturated, and 22–26% polyunsaturated fatty acids, depending on the concentration of the swine waste. In this study, the polyunsaturated fatty acids were lower, but the microalgae culture was mixed.

Another study by Wu et al. looked at the growth of Nannochloropsis oculata in anaerobically and aerobically treated diluted swine wastewater [18]. The growth rate was 0.59 gl−1day−1 (50% diluted) and 0.42 gl−1day−1 (25% diluted). Most of the studies used digestion as a way of preparing the nutrients for microalgae growth, but these studies did not look at ultrafiltration for swine wastewater purification. Further, Porphyridium cruentum was not used in any of these swine wastewater studies, and it is felt that this alga is important to characterize because of its ability to make pharmaceuticals, food products, and fuels. To date, there have been no studies looking at P. cruentum grown in swine wastewater media for biofuel production and comparing this with culture media. It is desirable to establish that a biofuel-producing organism can be grown in swine wastewater with parity compared to culture media. The purpose of this study was to determine if the biofuel potential of P. cruentum would have significantly altered the growth or lipid composition when grown in swine wastewater.

Another technology that is investigated in this study for wastewater treatment is the wafer- enhanced electrodeionization (WE-EDI). WE-EDI is a charged based hybrid membrane separation technique that utilizes ion exchange membranes and ion exchange resins for the separation of ions (Figure 1).

Figure 1: Illustration of selective wafer-enhanced electrodeionization process

WE-EDI has a multitude of applications in the chemical industry, but its use has been mainly ion product recovery and water desalination [13, 14, 15, 16, 17]. WE-EDI is especially useful when high purity water or high ion removal is desired, such as deionized water for microelectronics processing, or the recovery of high concentration ionic products [18, 19, 20].

The wafer structure of WE-EDI allows the operation of WE-EDI at low power levels at high ion removal rates [21, 22].

WE-EDI holds a great potential in a multitude of industries; however, several limitations hinder implementation. Specifically, selective ion separation requirement has been a big setback to achieve desired product purity. There are many ion exchange resin types that can be implemented in WE-EDI. However, their product specifics are limited with particle size, ion

exchange capacity and maximum operating temperature as well the type of the resins. WE-EDI can address the ion removal from high concentration wastewater, yet a solution that addresses selectivity of resins has not been produced. In WE-EDI process, not every ion has a priority to be removed. Depending on the application, the user may need a selective removal of an ion relative the remaining ions in the system. Also, because every ion transported that does not need to be transported costs money, there is a need for an energy and cost-effective measure and a process.

Ion selectivity in WE-EDI processes depends on ion exchange resin chemistry [22, 21] as well as other parameters of the process such as membrane and wafer thickness, exchange capacity of resins, membrane surface area, and resin bead size. However, there are no studies that shows the effect of commonly used resins on the ion selectivity and system efficiency to the best of our knowledge.

Furthermore, the optimization of WE-EDI units for maximizing selectivity and efficiency carries a significant importance, especially for reproducibility and scaling up. However, the modeling of WE-EDI is limited due to the complexity of physical models. Several papers have been published on the modeling of EDI, but most models were limited due to the oversimplification of transport processes [23, 24] and the system variables [22], or their models was done on dilute single-ion solutions [25, 26]. This emphasizes the need for directly applicable studies in which insights can be obtained to optimize the design of WE-EDI units and scale it up for maximizing selectivity and efficiency.

Purpose and Significance

The purpose of this work was (1) showing that swine wastewater can be used to cultivate microalgae with little change, as compared with other nitrogen and phosphorous sources, (2) finding the influence of four most commonly used ion exchange resins on the system efficiency and selectivity for the removal of sodium, calcium, and magnesium ions, and (3) modeling of wafer-enhanced electrodeionization with data science and machine learning techniques for a deeper understanding of the impact of ion exchange resins, the prediction of current efficiency and the final concentrations of sodium, calcium, and magnesium ions, as well as for the relevant feature selection. Through this research, several questions were explored and answered. When studying biofuel production from microalgae, we investigated how the swine waste can influence the growth and lipid composition of microalgae compared to controlled media. While the biomass productivity of

P. cruentum varied in the different media, there was no statistical difference between the swine wastewater and the control media. Fatty acid methyl ester (FAME) analysis of P. cruentum grown in the control and swine wastewater media also showed no significant differences in composition.

P. cruentum yielded a higher percentage of saturated fatty acids compared with unsaturated fatty acids, indicating that it has the potential to be used as a biofuel. Therefore, UF-treated swine wastewater has the potential to be used as an alternative growth medium for microalgae in biofuel production, which in turn will help with global issues of eutrophication. In our study in wafer- enhanced electrodeionization, we investigated how ion exchange wafers can affect the system performance and selective removal of ions. We observed that weakly cationic ion exchange resins showed higher overall current efficiencies whereas the strongly cationic ion exchange resins

preferred to transport divalent ions. Furthermore, the smaller bead sized strongly cationic resins favor the removal of divalent ions that are more valuable in industry compared to monovalent ions. Lastly, we investigated the modeling of WE-EDI using data science and machine learning methods. This study is the first study that utilizes data science and machine learning for the modeling of an electromembrane process. We predicted the monovalent and divalent ion concentrations, and current efficiency. Furthermore, we found the most relevant input variables in our dataset that affects the wafer-enhanced electrodeionization system.

Through the following chapters, outlined in Table 1, the scope of this research will be detailed and the impact that this study holds will be discussed.

Table 1: Outline of Dissertation CHAPTER MAIN RESEARCH QUESTION TOPIC OF

INVESTIGATION

Background and literature What is the current state-of-the-art for survey on microalgae wastewater treatment systems with 2 cultivation, biofuel production, microalgae and wafer-enhanced and wafer-enhanced electrodeionization? electrodeionization.

Cultivation of P. cruentum in Can swine wastewater be used instead swine wastewater and of controlled media with the same or 3 controlled media and better performance in microalgae comparison of fatty acid cultivation for the biofuel production? composition.

Table 1 Cont.

TOPIC OF CHAPTER MAIN RESEARCH QUESTION INVESTIGATION

Incorporation of four different

Which ion exchange wafer improve ion exchange wafers in wafer-

4 the divalent and monovalent ion enhanced electrodeionization

separation? for improved selectivity and

efficiency.

Modeling of wafer-enhanced

Can wafer-enhanced electrodeionization with data

5 electrodeionization be modelled with science and machine learning

data science and machine learning? for improved predictions and

feature selection.

How can this research further progress Summary of presented research,

the fields of wastewater treatment and future direction, and 6 improve upon state-of-the-art implications of subject matter,

technologies used in industry? and overall impact to the field.

References [1] A. Dey, "Ultrapure Water," in Advanced Membrane Technology and Applications, John Wiley & Sons Inc., 2008, p. Chapter 13. [2] D. Correll, "Role of phosphorus in the eutrophication of receiving waters: A review," Environ. Qual., vol. 27, pp. 261-266, 1998. [3] S.-L. Lim and W.-L. a. P. S.-M. Chu, "Use of Chlorella vulgaris for bioremediation of textile wastewater.," Bioresource Technology, vol. 101, no. 9, pp. 7314-7322, 2010. [4] N. Abdel-Raouf, A. Al-Homaidan and I. and Ibraheem, "Microalgae and wastewater treatment," Saudi Journal of Biological Sciences, vol. 19, no. 3, pp. 257-275, 2012. [5] L. Valderrama, C. Del Campo, C. Rodriguez, L. de-Bashan and Y. Bashan, "Treatment of recalcitrant wastewater from ethanol and citric acid production using the microalga Chlorella vulgaris and the macrophyte Lemna minuscula," Water Res., vol. 36, pp. 4185-4192, 2002. [6] A. Kalhor, A. Movafeghi, A. Mohammadi-Nassab, E. Abedi and A. Bahrami, "Potential of the green alga Chlorella vulgaris for biodegradation of crude oil hydrocarbons," Mar. Pollut. Bull., vol. 123, pp. 286-290, 2017. [7] X. Bai and K. Acharya, "Removal of trimethoprim, sulfamethoxazole, and triclosan by the green alga Nannochloris sp.," J. Hazard. Matter., vol. 315, pp. 70-75, 2016. [8] M. Jimenez-Perez, P. Sanchez-Castillo, O. Romera, D. Fernandez-Moreno and C. Perez- Martinez, "Growth and nutrient removal in free and immobilized planktonic green algae isolated from pig manure," Enzym. Microb. Technol., vol. 34, pp. 392-398, 2004. [9] E. Barlow, L. Boersma, H. Phinney and J. Miner, "Algal growth in diluted pig waste," Agric. Environ., vol. 2, pp. 339-355, 1975. [10] B. Hu, M. Min, W. Zhou, Z. Du, P. Chen, J. Zhu, Y. Cheng, Y. Liu and R. Ruan, "Enhanced mixotrophic growth of microalga Chlorella sp. on pretreated swine manure for simultaneous biofuel feedstock production and nutrient removal," Bioresour. Technol., vol. 126, pp. 71-79, 2012.

[11] W. Mulbry, S. Kondrad and J. Buyer, "Treatment of dairy and swine manure effluents using freshwater algae: Fatty acid content and composition of algal biomass at different manure loading rates.," J. Appl. Phycol., vol. 20, pp. 1079-1085, 2008. [12] P.-F. Wu, J. Teng, Y.-H. Lin and S.-C. Hwang, "Increasing algal biofuel production using Nannocholropsis oculata cultivated with anaerobically and aerobically treated swine wastewater.," Bioresour. Technol., vol. 133, pp. 102-108, 2013. [13] G. Ganzi, "Electrodeionization for high purity water production," in AIChE Symposium Series No: 261, 1988. [14] V. Palakkal, L. Valentino, Q. Lei, S. Kole, Y. Lin and C. Arges, "Advancing electrodeionization with conductive ionomer binders that immobilize ion-exchange resin particles into porous wafer substrates," npj Clean Water, vol. 3, p. 5, 2020. [15] S.-Y. Pan, S. Snyder, H.-W. Ma, Y. Lin and P.-C. Chiang, "Development of a resin wafer electrodeionization process for impaired water desalination with high energy efficiency and productivity," ACS Sustainable Chem. Eng., vol. 5, p. 2942−2948, 2017.

[16] S. Datta, M. Henry, Y. Lin, A. Fracaro, C. Millard, S. Snyder, R. Stiles, J. Shah, J. Yuan, L. Wesoloski, R. Dorner and W. Carlson, "Electrochemical CO2 capture using resin-wafer electrodeionization," Ind. Eng. Chem. Res., vol. 52, no. 43, p. 15177–15186, 2013. [17] M. B. Arora, J. A. Hestekin, S. W. Snyder, E. J. St. Martin, Y. J. Lin, M. I. Donnelly and C. & Sanville Millard, "The separative bioreactor: A continuous separation process for the simultaneous production and direct capture of organic acids," Separation Science and Technology, vol. 42, no. 11, pp. 2519-2538, 2007. [18] Ö. Arar, Ü. Yüksel, N. Kabay and M. Yüksel, "Various applications of electrodeionization (EDI) method for water treatment—A short review," Desalination, vol. 342, pp. 16-22, 2014. [19] G. Ondrey, "Simplified electrodeionization technology reduces operating costs.," Chemical Engineering, vol. 117, no. 8, p. 9, 2010. [20] W. Su, R. Pan, Y. Xiao and X. Chen, "Membrane-free electrodeionization for high purity water production," Desalination, vol. 329, pp. 86-92, 2013. [21] T. Ho, A. Kurup, T. Davis and J. Hestekin, "Wafer chemistry and properties for ion removal by wafer enhanced electrodeionization," Separation Science and Technology, vol. 45, no. 4, pp. 433-446, 2010. [22] A. Kurup, T. Ho and J. Hestekin, "Simulation and optimal design of electrodeionization process: Separation of multicomponent electrolyte solution," Ind. Eng. Chem. Res., vol. 48, p. 9268–9277, 2009. [23] E. Glueckauf, "Electro-deionization through a packed bed," Br. Chem. Eng, vol. 4, no. 646- 651, 1959. [24] H. M. Verbeek, L. Furst and H. Neumeister, "Digital simulation of an electrodeionization process.," Comput. Chem. Eng, vol. 22, p. S913–S916, 1998. [25] A. Mahmoud, L. Muhr, G. Grévillot and F. & Lapicque, "Experimental Tests and Modelling of an Electrodeionization Cell for the Treatment of Dilute Copper Solutions.," The Canadian Journal of Chemical Engineering, vol. 85, no. 2, p. 171–179, 2008. [26] J. Lu, Y.-X. Wang and J. Zhu, "Numerical simulation of the electrodeionization (EDI) process accounting for water dissociation," Electrochimica Acta, vol. 55, no. 8, p. 2673–2686, 2010. [27] H. Strathmann, Ion exchange membrane separation processes., 1st Edition ed., vol. 9, Elsevier, 2004, p. 360. [28] E. Drioli, E. Curcio, G. Di Profio, F. Macedonio and A. Criscuoli, "Integration membrane contractors technology and pressure-driven membrane operations for seawater desalination: energy, exergy and cost analysis," Chemical Engineering Research and Design, vol. 84, no. 3, pp. 209-220, 2006.

Chapter 2: Literature Review

2.1. Introduction to Microalgae and Algal Biofuels

Biofuels are renewable energy sources that are made from organic matter or wastes such as corn, sugar cane, vegetable oils, or waste feedstocks. Biofuels have an important role in reducing carbon emissions and are one of the largest sources of renewable energy. Other advantages of biofuels include lubricant and better solvent properties, lower emissions of chemicals, storage and transport easiness, their direct use in diesel engines without modifying the engine. Moreover, biofuels are biodegradable and less toxic which are important properties of biofuels from environmental and safety aspect. According to Peterson et al., 2005, biofuels degrade four times faster compared to conventional fuels in aquatic environments. They also showed that biofuels are up to 89 times less toxic than table salt which makes biofuels environmentally friendly and safer fuel. Detailed comparison of petroleum-derived fuels and biofuels are given in Table 1.

Biofuels also have higher cetane numbers (46 - 52) depending on the feedstock used whereas conventional fuels have a typical cetane number range of 42 – 45 [4]. Cetane number is a measure of combustion quality of an engine, and higher cetane number means that engine can run smoothly and quietly. Thus, biofuels improve the performance of engines.

Depending on the origin and production technology, biofuels are categorized as the first, second and third generation biofuels [5]. The first-generation biofuels are sourced from crop plants such as corn, olive, sunflower, soya bean and flax. However, use of these crops for the biofuel production increases food prices and food riots. Due to their negative impact of first-

generation biofuels on food feedstocks, limited biofuel yields and high costs, the second- generation biofuels were developed.

Table 1: Comparison of properties of petroleum-derived fuels and biofuels. Derived from [1, 2, 3]. Petroleum-derived Property Biofuel Pros/Cons of Biofuel Fuel Similarities: Molecule About the same size None size with petroleum Differences: About 95% saturated Chemical FAMEs and Different fuel properties. hydrocarbons and 5% structure unsaturated olefin aromatic compounds Pro: High lubricity Lubricity Lower Higher reduces engine wear Pro: Reduced pollution Sulfur High sulfur No sulfur from engines using content biodiesel Con: Higher oxygen Oxygen Low High (10-12%) slightly reduces peak in content engine power (w4%). Con: A concern, Gel up at low Gel up Does not especially for the cold temperatures winters More likely to Con: A concern, for oxidize to form a extended fuel storage Oxidize Does not semisolid gel-like and while using engines mass occasionally.

Con: More aggressive to Chemically Chemically active as some materials normally Is not active a solvent. considered safe for diesel fuel. Unsafe for Pro: A real benefit for Toxicity Less toxic environment spill cleanups. The second-generation biofuels use the bioenergy crops which are plants specifically grown for bioenergy production, or lignocellulosic non-feedstocks including straws, bagasse fibers, and forest residues like un-merchantable woods. But the second-generation biofuels are also limited

with the need for maximizing the amount of renewable carbon and hydrogen. Due to the problems associated with the first- and second-generation biofuels, the third generation of biofuels are developed from algal biomass [6].

Microalgae are defined as microscopic, photosynthetic organisms that can be eukaryotic or prokaryotic [7]. According to Richmond, 2004, there are more than 50,000 species of microalgae, and they exist in every part of the ecosystem [8]. Microalgae are essential to global carbon, nitrogen and sulfur cycling as 45% of photosynthetic carbon assimilation is achieved by microalgae.

Even though the first use of microalgae by humans was 2000 years ago by Chinese to survive famine, biotechnological use of microalgae started to develop in the middle of 20th century.

Today, microalgae are used for several commercial application including nutrient enhancer for food and animal feed, aquaculture, cosmetics, pharmaceuticals, wastewater treatment and biofuel production.

Microalgae have a high efficacy in converting sunlight into beneficial products like lipids that can be converted to biofuels. Microalgae produce triacylglycerides (TAGs) under certain conditions. TAGs can be converted to fatty acid methyl esters (FAMEs) which are the primary component of biodiesel by the transesterification process. Some of the high oil yielding microalgae species are given in Table 2.

Table 2: Lipid content of some microalgae. Adapted from [9] Lipid Content Species (% dry matter) Scenedesmus obliquus 11-22 Scenedesmus dimorphuus 6-7 Chlorella vulgaris 14-40 Chlorella emersonii 63 Chlorella sorokiana 22 Neochloris oleoabundans 35-65 Spirulina maxima 4-9 Microalgae are expected to be one of the most important renewable fuel crops because of higher photosynthetic efficiency, higher biomass productivity, higher growth rates compared to

feedstock plants, higher CO2 fixation and O2 production and ability to grow in liquid medium

[10]. Microalgae can grow in different climates and non-arable lands [11], and they could produce up to 58,700 L of oil per hectare which is 1-2 magnitudes higher than any other energy crop yield [10]. However, mass production of microalgal biofuel faces a number of technical and economic barriers, and it is important to develop cost-effective technologies that allow the efficient biomass harvesting and oil extraction. Yet, microalgae are considered as a feasible approach to mitigate the global warming, and it is obvious that biofuel and biomass production from microalgae can provide significant benefits [12].

The energy conversion from microalgal biomass can be categorized as biochemical conversion and thermochemical conversion. Biochemical conversion has subcategories of fermentation and transesterification whereas the thermochemical conversion is subdivided into gasification, pyrolysis, liquefaction, and hydrogenation. Figure 1 shows the energy conversion processes and the end products of these processes [13].

Figure 1: Energy conversion processes from microalgae [13].

2.2. Wastewater Treatment with Microalgae Cultivation

Wastewater has a serious impact on environment and public health due to its high concentration of nutrients and toxic contaminants. On the other hand, it also has a big potential due to high amount of nutrients especially nitrogen and phosphorus. However, without proper treatment, excess nutrients discharged in wastewater can cause a damage to the ecosystem such as undesirable pH shifts, cyanotoxin production, low dissolved oxygen concentrations, and fish kills [14]. There are chemical and physical technologies that can remove these excess nutrients, but they are costly processes and often lead to secondary contaminations that can create further problems of safe disposal. The industrial scale wastewater treatment systems suffer from these problems, and algal based wastewater treatment can offer less expensive and environmentally safer nutrient reduction with the benefits of resource recovery and recycling [15].

The different microalgae species such as Chlorella and Dunaliella have been used for mass production and wastewater treatment for about 75 years [16]. Microalgae can treat different types of wastes including livestock, human sewage, swine waste and other agricultural wastes. Hence, microalgae can be used in wastewater treatment for various different purposes including the removal of nitrogen, phosphorus, heavy metals, and some bacteria [16]. The microalgae that grow on wastewater have a significant potential to be used as feedstock for biofuel production.

2.2.1. Composition of wastewater

Wastewater is a mixture of organic and inorganic materials as well as synthetic compounds.

75% of organic carbon in sewage are in the form of carbohydrates, fats, proteins, amino acids and volatile acids [17]. The inorganic materials in wastewater are high concentrations of monovalent and divalent ions such as sodium, calcium, potassium, magnesium, sulfur, phosphate, bicarbonate as well as ammonium salts and heavy metals [17]. The high concentrations of nitrogen and phosphorus make the wastewater suitable as algae growth media for biofuel production while removing nutrients. Table 3 shows the nitrogen and phosphorus contents and their molar ratios in different types of wastewater. According to this table, swine wastewater has the highest concentrations of nitrogen and phosphorus which makes the swine wastewater suitable for algae growth media.

Table 3: Wastewater types and nutrients characterizations for microalgae cultivation. Adopted from [18] Wastewater Type Nitrogen (mg l-1) Phosphorus (mg l-1) References N:P Molar Ratio Medium domestic 40 8 [19] 11 Cattle feedlot 63 14 [20] 10 Poultry feedlot 802 50 [20] 36 Swine feedlot 2430 324 [20] 17 Even though the swine wastewater makes a good growth medium for microalgae, raw swine wastewater includes different microorganism that might contaminate and compete with microalgae culture. Those microorganisms are needed to be removed to get a pure microalgae culture. Ultrafiltration is one of the effective ways to remove those contaminants from swine wastewater.

2.3. Overview of Ultrafiltration

Ultrafiltration is one of the processes that removes particulates from water by forcing water through a porous membrane. In a typical ultrafiltration membrane, the pore size is around 0.01 micron [21] but it can range from 0.05 micron to 1 nm [22]. Ultrafiltration can remove many biological contaminants which would not be possible otherwise with other methods such as heat treatment [23]. The sizes of matters that are removed by ultrafiltration can be seen in the Figure

Figure 2: The filtration spectrum of various filtration methods relative to sizes of common matters [24]

Ultrafiltration membranes are usually made from various materials such as cellulose acetate, polyacrylonitrile, polyether-sulfone, polysulfone, and polyvinyldeneflouride [25] and usually prepared by phase inversion method [26]. Ultrafiltration membranes usually come in three different shapes; sheet, capillary, and tubular [27]. The working mechanism schematic of ultrafiltration is shown in Figure 3. When pressure is applied, the pores within the membrane acts as filter and allow the water, dissolved solids and other organic matters with low molecular weight pass through membrane. The remaining suspended particles are removed from the system with the concentrate stream. Besides the pore size, there is another important factor in ultrafiltration systems. This factor is membrane capacity or flux which is described as the volume of water filtered per unit area per time [27]. Having maximized flux is important because

when the membrane area increases, the costs related to equipment, initial capital, and operation also increases.

Figure 3: Schematics of how ultrafiltration works

Today, there are many common applications for ultrafiltration such as pharmaceutical and beverage sterilizations, liquid clarifications, and wastewater treatment [22, 23, 28]. Ultrafiltration can provide effective disinfection in wastewater treatment because they reduce the levels of various bacteria below the detectable levels.

2.4. Electromembrane Processes

Electromembrane processes are an important part of separation processes and extensively used for removal of ions from solutions. With both existing and developing applications, electromembrane processes are a growing field of research. The separation in electromembrane processes is based on migration of ions across charged membranes with electric field as the main

driving force. This section presents the details of the main electromembrane processes, their common applications, and physical and theoretical bases.

2.4.1. The ion-exchange membranes, their structure and function

The key components of electromembrane processes are the ion-exchange membranes. Ion exchange membranes are synthetic membranes permeable to positive or negative ions in aqueous solutions. This unique semi-permeability property of ion exchange membranes makes them very attractive in chemical industry because it allows for the removal, addition, substitution, depletion or concentration of ions in process solutions. There are two different types of ion-exchange membranes: (1) cation-exchange membranes which contain negatively charged groups fixed to the polymer matrix, (2) anion-exchange membranes which contain positively charged groups fixed to the polymer matrix. The polymer matrix structure contains ion exchange resin which makes membranes permeable to ions. There are two types of ions in the membranes; electrically charged fixed functional groups (ions) in the polymer matrix of membrane and interchangeable mobile ions (counterions) residing within the pore spaces [29].

Figure 4: Illustration of the structure of a cation-exchange membrane [29]

This structure maintains the electrical balance within the matrix so that the electric field can overcome the forces that constrain mobile ions and the ions entering the pores from solutions can replace the loads, thus enables the selective transition of ions [30]. For example, if the matrix is negatively charged, the counterions are positive; hence, the membrane is permeable to cations.

When the electric field is applied, they replace the counterions and the fixed charges of matrix prevent the transitions of co-ions (anions in this case) [31, 32]. This is also valid for vice versa.

Creating a selective barrier with ion exchange membranes for the passage of ions and using the electricity as a driving force are the fundamental principles of electrically driven processes

[33]. The electric field applied across membranes determines the direction of ion movement.

Cations move toward the cathode, and anions move toward the anode. As membranes are selectively permeable to only cations or only anions, the separation process takes place.

In ion-exchange membrane deionization processes such as electrodialysis, electrodeionization and capacitive deionization, low-molecular-weight ions are removed from a feed solution through ion-exchange membranes and concentrated under the driving force of an electrochemical gradient. Accepting and rejecting ions in the dilute and concentrate compartments are done by the ion exchange membranes. Thus, they have important applications in water purification [34, 35, 36] and ion removal [37] for different industries such as juice, dairy, and oil and gas to produce higher quality products.

2.4.2. Electrodialysis

One of the major electrically driven processes for ion removal is electrodialysis (ED).

Electrodialysis has a wide variety of applications but it is mainly used for desalination of brackish water and demineralization of solutions in the food and drug industry [38] as well as in the concentration of salts from seawater [39].

Electrodialysis uses cationic and anionic exchange membranes arranged in an alternating pattern between an anode and a cathode to selectively migrate and remove ions from solutions under the electric field. If an ionic solution is fed through an ED cell and electrical potential is establish between the anode and cathode, the positively charged ions (cations) migrate toward the cathode and the negatively charged anions migrate toward the anode. The cations pass through the negatively charged cation-exchange membrane, but they are retained by the positively charged anion-exchange membrane. The opposite is also valid for anions. At the end of the process, the ion concentration increases in alternate compartments whereas the other

compartments simultaneously become depleted of ions. The depleted solution is commonly referred as the diluate and the concentrated solution as the concentrate or brine. One diluate and one concentrate compartments along with two anion- and cation- exchange membranes make up a cell pair which is called as electrodialysis stack and can have a few hundred cell pairs between two electrodes (Figure 5).

Figure 5: Diagram of a two-compartment electrodialysis stack (C): concentrate, (D): diluate compartments [40].

One of the important phenomena in electromembrane processes and ED specifically is the water splitting or dissociation. Under electric field, the water dissociates into H+ and OH- ions

[41, 42, 43], and these split ions start to compete with other ions in the solution for transport sites through the membrane. This concept results in decreased ion removal, thereby in decreased system efficiency. In process, water splitting under electrical field occurs when the electrical current increases without an increase in the number of ions transported which means the power

usage increases without any benefits. Due to these shortcomings, water splitting is an undesired phenomenon that researchers have long worked to minimize [44, 45, 46, 47]. For example,

Rubinstein et al. attempted to model the transport of ions, optimal concentration, flow rates and current at which water splitting occurs to predict and prevent the water splitting [47]. Korngold et al. and Messalem et al. modified the spacer for prevention of water splitting [45, 46].

Electrodialysis is the most widely commercialized electromembrane technology. It is a chemical-free technology and competes with reverse osmosis with better resistance to fouling and scaling. It also has an economical advantage in desalination of low salinity solutions.

However, there are some major disadvantages of electrodialysis. Firstly, the system performance significantly decreases when solid particles, alcohols and high viscosity solutions fed to the system. This decrease in the performance results in a dramatic increase in power consumption due to the high pressure and low flow rate [48, 49, 43, 50, 51, 52].

Another problem with electrodialysis is the membrane fouling due to the membrane blockage or swelling [53]. There are several studies on overcoming the membrane fouling using anti- scaling substances, anti-fouling membranes and pre-treating the feed streams with ultrafiltration or microfiltration to remove solid components [54, 42, 55, 56]. It was found that these pre- treatment options can limit the fouling at some degree, but they do not eliminate all fouling problems such as those caused by high viscosity or high alcohol concentration solutions [57].

Membrane fouling also results in additional clean-up process which increases the operating costs of electrodialysis up to 30% [58, 48, 49, 59].

Another problem with electrodialysis is high power consumption at low concentration feed streams. It was observed that the power required to transport ions at low concentrations is significantly higher than the power requirement at high ion concentrations [60]. For instance, the electrodialysis consumes twice the power to lower the sodium concentration to the level of 10 ppm compared to ion exchange resins. Additionally, reaching low sodium levels is not possible without removing other ions in the feed streams as the electrodialysis has little to none selectivity for ion removal. This selectivity issue leads to dramatic increase in power consumption [61, 62].

2.4.3. Ion Exchange

Ion exchange (IE) is a specific chemical process in which diffusive ionic redistribution occurs between an insoluble material (resin) and a solution. This insoluble material, that is capable of exchanging anions or cations, is a polymer on which a fixed ion is permanently attached. The solution contains ionic species. Ion exchange process starts with a chemical potential gradient between the solution and ion exchanger.

Like ion exchange membranes, the ion exchange resins are made of synthetic polymers that are made of a cross-linked matrix. This matrix is created with the action of crosslinking agents and a fixed functional group. Depending on these functional groups that are immobilized onto polymer structure, the resins are categorized as:

• Strongly acidic resins: Sulfonic acid groups

• Weakly acidic resins: Carboxylic acid groups

• Strongly basic resins: Quaternary amino groups

• Weakly basic resins: Primary, secondary and/or tertiary amino groups

There are also an additional group of resins called chelating resins that are used to bind cations.

However, anionic and cationic resins are the most commonly used resin types in industry.

The crosslinking structure of resins gives the mechanical stability, strength and solubility to the polymer which determines the swelling capacity. Swelling is an important property that allows the permeability of ions into the matrix and improves the accessibility of ions to the functional group [63]. These properties make the resins (exchangers) show an ionic preference for ions in the solution to selectively exchange their places with the ion in the matrix. The selectivity in an ion exchange process is also linked to the resin dimensions, valence, the pore size within the matrix and electrostatic interactions between the counterions and the matrix [64].

The process efficiency of ion exchange depends on the affinity of ion exchange resins for particular ion, pH of the solution, the concentration of ion in the solution, and temperature.

2.4.5. Electrodeionization (EDI)

Electrodeionization (EDI) is a hybrid technology that is based on the electrodialysis (ED) and ion exchange (IE) [65] with the aim of overcoming the disadvantages of both technologies such as concentration polarization, chemical regeneration [66], and high power consumption at low ion concentrations [67, 68]. EDI was developed to allow the production of deionized water without the use of hazardous acids that are required to regenerate ion exchange resins.

EDI is first introduced by Kollsman in 1953 for the treatment of ionic mixtures [69, 70]. In his patent, the electrodeionization is described as an apparatus for the desalination of brine using

an alternating pattern of anionic and cationic resins. After his invention of EDI, Argonne

National Laboratory researchers used EDI for the removal of radioactive ions from industrial water [71]. In 1959, Glueckauf initiated a theoretical explanation and modeling of an EDI unit for single ion removal [65]. After his work, there are other researchers who proposed both theoretical and experimental explanations to broaden the knowledge of EDI principles by addressing the ion transport mechanism, energy consumption and efficiency, and interrelationships between the components of EDI. Finally, the first commercial continuous EDI

(CEDI) was introduced in 1987 and now sold by U.S. Filter Corporation under the trade name

Ionpure [72].

The EDI is considered as the main technology in ultrapure water production because it consumes less energy (30-40% less) for ion removal at low concentrations compared to ED and other technologies [73, 74]. There are also numerous studies that focused on low concentration ion removal and the performance of EDI with changing flow rates, current and voltage.

The design of EDI is similar to ED with the addition of ion exchange resins in the feed compartment to increase the conductivity and ion transport, and to prevent the concentration polarization. There are two distinct operating regimes for the EDI: enhanced transfer and electro- regeneration [75]. In the enhanced transfer regime, the resins within the EDI remain in the salt forms. In low salinity solutions, ion exchange resins are more conductive than the solution and act as a medium for transport of ions across compartments to the ion exchange membranes. In

this mode, the electroneutrality is maintained with the simultaneous removal of both anions and cations.

In the electro-regeneration regime, resins are continuously regenerated by electrical dissociation of water into hydrogen and hydroxide ions. The optimum location for water dissociation is on the resin filler. The regeneration of resins to their hydrogen and hydroxide forms allow the EDI to remove weakly ionized organic and inorganic compounds. Figure 6 shows the transport of ions and electrochemical regeneration of ion exchange resins in two dilute

(feed) compartments in an EDI cell.

Figure 6: Ion transport and electrochemical regeneration in an EDI cell [76]

2.4.6. Conventional EDI vs. Wafer-Enhanced EDI (WE-EDI)

EDI can be operated in both continuous and batch modes and does not require a regeneration of resins. Furthermore, the EDI can be operated at low concentrations with lower power consumption compared to ED. The EDI is hence considered very important in the ultrapure water production [67, 77]. A standard EDI unit consists of two chambers similar to conventional

ED; the feed/dilute chamber where the ions are to be removed and the concentrate chamber where the ions are collected. There are also two rinse compartments that keep the chemical from building up and corroding the electrodes. The chambers are separated by cation- and anion- selective membranes. But, in the case of EDI, the dilute chamber is filled with ion exchange resins and electrolyte solution is fed through the ion exchange bed consisting both anion- and cation-exchange resins especially for the production of ultrapure water [68, 77, 78].

Even though there are major advantages over ED and ion exchange processes, there are also several disadvantages of EDI. Firstly, the ion exchange resin beads are inserted into a pair of anionic- and cationic- exchange membranes loosely. This loose bead structure prevents perfect sealing between compartments and causes the leakage of ions from one compartment to another due to the convection migration instead of diffusion [79, 80]. Hence, it complicates achieving the target separation. Another disadvantage of loose beads in EDI systems is the uneven flow distribution due to the flow channels which decrease the separation of efficiency [81]. There were studies done to find ways for eliminating these two problems; by using different stack configurations like spiral-wound configurations [82] or by immobilizing the resin using magnetic

fields [83]. With the spiral-wound configurations, the leakage of the solution within the EDI system was eliminated by tightly packing the resin between membranes [82]. In the resin immobilization using magnetic fields method, the cathode, anode and ion exchange resins were charged which immobilized the resins and prevented the packed bed from moving when the solution was fed to the system. This method eliminated the flow channel formation. However, these methods were able to eliminate one of the disadvantages of conventional EDI but not both.

Therefore, there is a need for a new system specifically designed to overcome both disadvantages. As a result, an integrated approach, wafer-enhanced electrodeionization (WE-

EDI), was proposed by Arora et al [84]. In WE-EDI, the loose ion exchange resin bead structure of conventional EDI is replaced by a wafer inserted between two membranes as the spacer. The wafer is a mixture of immobilized cation- and anion-exchange resin beads, a polymer as a binding agent, and sucrose that creates pores within the wafer.

Compared to conventional EDI, WE-EDI can be easily assembled and operated more efficiently as it helps prevent uneven flow distribution and leakage of ion between the compartments. Because of the reduction in leakage, WE-EDI can be used for more selective product separations such as the removal of acidic impurities from corn stove hydrolysate liquor,

CO2 capture, and purification of organic acid.

With the combination of ion exchange resin wafers and electrodialysis, wafer-enhanced electrodeionization shows feasibility in ion selective removal and more potential to be used in wider applications. However, there are no studies that examines the effects of most commonly

used resins on the performance of wafer-enhanced electrodeionization. This study has implemented this novel technology to examine how different resins affect the transport between different ions; especially for sodium, calcium and magnesium.

2.5. Theoretical Background: Transport in Ion Exchange Membranes

The solutes are transported through membranes and this process is dependent on the permeability of the membrane and driving force. The driving force is typically a pressure, electric or concentration gradient between the feed side and product side. If the pressure gradient is present, the transport mechanism is called viscous flow. This is the main form of transport in microfiltration and ultrafiltration. If there is a concentration gradient, this transport is referred to as diffusion which is the dominant form of mass transport in reverse osmosis, gas separation and dialysis. There are also electrically driven processes such as electrodialysis and electrodeionization where the driving force is the electric potential gradient. In these processes, the mass transport is called migration. These driving forces determine the transition of certain constituents through semi-permeable membranes [85].

Membrane processes are different than conventional separation processes of chemical engineering such as distillation, evaporation and crystallization. These conventional processes are equilibrium processes whereas membrane processes are steady-state processes meaning that if the driving force is constant, the flow through the membrane will be constant when the process reaches the steady-state [86]. This transport of mass through semi-permeable membranes has

been explained by various mathematical models including Fick’s, Ohm’s and Hagen-Poiseuille’s

Laws [87, 88]. The most general form of these laws is described by:

푑푋 퐽 = −푃 푖 (1) 푖 푖 푑푧 where J refers to the flux, P is a permeability coefficient, dX/dz gradient is the driving force and i refers to the component [85, 88]. If the driving force is electrical gradients, it can be expressed as:

푑푋푖 = 푑휂푖 = 푑휇푖 + 푧푖퐹푑휑 = 푉̅푖푑푝 + 푅푇푑 푙푛 푎푖 + 푧푖퐹푑휑 (2) where 푑η and 푑μ are the gradients in the electrochemical and chemical potential, 푑p, 푑훼, and 푑휑 are the pressure, the activity and electrical potential gradient across the membrane, respectively,

푉̅ is the partial molar volume, F is the Faraday constant, R is the gas constant, z is the ionic valence, and i is the component i [85] . Depending on the type of the flow and driving forces, additional terms can be added or removed from equations 1 and 2.

2.6. WE-EDI Performance Indicators

The performance of WE-EDI is evaluated by several parameters. The most important factors to consider are the current efficiency and the selectivity.

2.6.1. Current Efficiency

Current efficiency is a measure of how effectively the ions are transported across ion

exchange membranes and resin wafer in the WE-EDI process. It is defined as:

푧 푥 푉푓 푥 (퐶푖− 퐶푓) 푥 퐹 휂푐 = 푥 100 (3) 퐼 푥 푁푐 푥 푀푊 푥 푡

where z is the ionic valence, Vf is the volume of the feed, Ci and Cf are the initial and final

concentration of ionic species in the feed stream, F is the Faraday constant, I is the electric

current, Nc is the number of cells, MW is the molecular weight of the ionic species, and t is

the total operation time.

2.6.2. Selectivity

Separation coefficient is calculated to determine the selectivity of one ion over another ion. It

indicates the removal rate of one ion compared to the removal rate of another ion. The

selectivity is defined as:

푒 푠 (퐶푖 −퐶푖 ) ⁄ 푠 퐶푖 훼 = 푒 푠 (4) (퐶푗 −퐶푗 ) ⁄ 푠 퐶푗

s e where Ci the starting concentration of ion i (i.e. calcium ion) in the feed stream, Ci is the final

s concentration of ion i in the feed stream, , Cj is the starting concentration of ion j (sodium ion) in

e the feed stream, and Cj is the final concentration of ion j in the feed stream.

2.7. Overview of the Principles of Big Data Analytics and Machine Learning

Big data is an emerging topic that turns data into useful insights for more informed decisions in business and operations. All fields of science and engineering has been generating large datasets with high-throughput experimentation, large scale observations, and massive simulations. However, these large datasets are usually collected/synthesized from numerous different sources which results in heterogeneity and complexity. They can also contain noise or other challenging features. As a result, knowledge extraction from these datasets are limited.

Data science which is an intersection of statistics, data management, visualization, machine learning and software engineering addresses this bottleneck that comes with large and complex datasets.

Machine learning is field of computer science in which computers learns from data and makes predictions and/or improves system understanding using the data. There are many ways to sub-divide the machine learning field to relate various problems. One such sub-division is into supervised, unsupervised, and semi-supervised problems. In this thesis, the supervised learning is used for the modeling and will be explained further in this chapter.

Chemical engineering is a very ideal field to deploy data science and machine learning methodologies, and chemical engineers, from the process engineer in operations to the academic researcher, are being asked to manipulate, transform, and analyze complex data sets for years

[89].

2.7.1. Machine Learning in Analyzing the Results of Membrane Separation Experiments and

Reproducibility of the Results

Even though the chemical engineering and membrane separation fields are in the front-end in theory building, they are in late adopting machine learning techniques for analyzing experimental results. In fact, scientific experiments are mostly analyzed by traditional p-values and modeled using statistical methods. However, recent studies [90, 91] showed that there is a reproducibility crisis due to scientific biases such as selection bias and p-hacking. More recent

cases on the reproducibility problem and p-hacking in different fields of science can be found in the literature [92, 93, 94].

One of the cult papers in statistical science by Breiman [95] highlighted the difference between statistical modeling and machine learning. According to the author, data are generated by the statistical model and evaluated based on the how data fits to the model. But this approach has led the scientist to “irrelevant theory” and “questionable conclusions” [95]. On the other hand, the machine learning approach allows scientists to work on both large complex data sets and smaller data sets. This approach treats data as unknowns and focuses on the predictions.

Although statistical models also do predictions, they are limited with strong assumptions that usually lacks the important variables of a system. Additionally, we have to incorporate our knowledge of the system in the statistical modeling to choose a model, but machine learning only needs us to choose an algorithm and does minimal assumptions [95, 96].

Most real systems are governed by nonlinear, coupled differential equations of large number of process variables. Physics based statistical modeling methods are based on an actual understanding of the underlying system dynamics. However, most physics-based statistical models are simplified representations of the system dynamics due to the unfeasibility of creating a model of infinite complexity. Thus, the accuracy of these models depends on the level of simplification and the actual effects of all unincluded variables on the system dynamics.

In data-driven models, only the user-defined objective (task) is concerned. These models try to learn the relation between the relevant input and output variables. This approach could be

useful in cases where modeling the system is difficult due to the lack of understanding of the system dynamics or the models created being inadequate.

Modeling of wafer-enhanced electrodeionization is one of the processes that is governed by non-linear coupled differential equations. If the solutions that are fed to the system are high concentrations and include multiple ions, the number of variables that affects the system performance also increases as well as the number of differential equations that governs the system. This complex system makes the physical modeling challenging unless the critical variables are omitted.

The purpose of this study is to model the wafer-enhanced electrodeionization with multiple ions using data science and machine learning methods, to give a deeper understanding of the impact of all variables on the WE-EDI unit system. The details of algorithms and methods used in this model will be explained in the Chapter 5.

References

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Chapter 3. Porphyridium cruentum Grown in Ultra-Filtered Swine Wastewater and Its

Effects on Microalgae Growth Productivity and Fatty Acid Composition

Humeyra B. Ulusoy Erol, Mariana Lara Menegazzo , Heather Sandefur, Emily Gottberg, Jessica Vaden, Maryam Asgharpour, Christa N. Hestekin and Jamie A. Hestekin “Ulusoy Erol, H.B.; Menegazzo, M.L.; Sandefur, H.; Gottberg, E.; Vaden, J.; Asgharpour, M.; Hestekin, C.N.; Hestekin, J.A. Porphyridium cruentum Grown in Ultra-Filtered Swine Wastewater and Its Effects on Microalgae Growth Productivity and Fatty Acid Composition. Energies 2020, 13, 3194”

Graphical Abstract

3.1. Abstract Microalgae have been extensively tested for their ability to create bio-based fuels. Microalgae have also been explored as an alternative wastewater treatment solution due to their significant uptake of nitrogen and phosphorus, as well as their ability to grow in different water types.

Recently, there has been significant interest in combining these two characteristics to create economic and environmentally friendly biofuel using wastewater. This study examined the growth and lipid production of the microalgae Porphyridium (P.) cruentum grown in swine wastewater (ultra-filtered and raw) as compared with control media (L−1, modified f/2) at two different salt concentrations (seawater and saltwater). The cultivation of P. cruentum in the treated swine wastewater media (seawater = 5.18 ± 2.3 mgl−1day−1, saltwater = 3.32 ± 1.93 mgl−1day−1) resulted in a statistically similar biomass productivity compared to the control medium (seawater = 2.61 ± 2.47 mgl−1day−1, saltwater = 6.53 ± 0.81 mgl−1day−1) at the corresponding salt concentration. Furthermore, no major differences between the fatty acid compositions of microalgae in the treated swine wastewater medium and the control medium were observed. For all conditions, saturated acids were present in the highest amounts (≥67%), followed by polyunsaturated (≤22%) and finally monounsaturated (≤12%). This is the first study to find that P. cruentum could be used to remediate wastewater and then be turned into fuel by using swine wastewater with a similar productivity to the microalgae grown in control media.

Keywords: microalgae; Porphyridium cruentum; wastewater treatment; ultrafiltration

3.2. Introduction Microalgae have a significant potential to be used for the development of alternative bio-based fuels [1]. Under optimized conditions, microalgae have been reported to have a high productivity of lipids (for biodiesel) or carbohydrates (for bioethanol or biobutanol), depending on the type of

microalgae and growth conditions. Additional advantages of microalgae include high lipid yield per unit area [2], short cultivation periods, better resistance to diverse environments like seawater or eutrophic waters [3], and the production of valuable co-products, such as proteins and residual biomass [4]. In addition, microalgae strains can thrive in saltwater, seawater, and wastewater [5–

7]. Work by Solovchenko et al. (2015) showed that animal manure provides a very rich source of phosphorous required for microalgae growth [8]. Thus, the purpose of this paper is to show that swine wastewater can be used to cultivate microalgae with little change, as compared with other nitrogen and phosphorous sources.

For growth and productivity, microalgae require significant amounts of nitrogen and phosphorous. One economical and environmentally friendly source for these nutrients is wastewater. Although nitrogen levels are often high in wastewater, phosphorus is often at lower levels than the desired ratio for algal growth. Animal (swine and dairy) wastewaters have been reported to contain a higher ratio of phosphorus to nitrogen than primary and secondary wastewaters (primary wastewater is a result of the capture of suspended solids and organics through sedimentation. The secondary wastewater is a removal of organic matter using microorganism) [9]. As nitrogen and phosphorous are expensive, and often come from a petroleum-derived source, microalgae growth from wastewater sources is attractive. Oswald et al.

(1957) first proposed the use of microalgae to clean up wastewater [10]. There have been many reported cases of using microalgae to clean up wastewater, including Chlorella vulgaris for the clean-up of wastewater from ethanol and citric acid production [11], Chlorella vulgaris for the biodegradation of hydrocarbons [12], Nannochloris sp. for the treatment of trimethoprim, sulfamethoxazole, and triclosan [13]. While there have been numerous studies on the nitrogen and phosphorous removal abilities of microalgae on wastewater, and numerous studies on the growth

of algal biomass on wastewater, there have been limited studies evaluating the fatty acid composition of the microalgae grown in swine wastewater. Since there are many different types of wastewater, here the focus will be on swine wastewater and the products produced, as compared with conventional growth media.

Several previous studies have explored the growth rates of different microalgae species on swine wastewater, such as Scenedesmus intermedius (0.014 mg chlorophyll h−1), Nannochloris sp.

(0.011 mg chlorophyll h−1) [14], and Chlorella vulgaris (40 mgl−1day−1) [15]. Both of these studies found that swine wastewater was suitable for microalgae growth but did not look at the product breakdown of the fatty acids. However, a few studies have also evaluated the fatty acid content of microalgae grown in swine waste. Hu et al. (2012) compared the growth of Chlorella sp. in fresh and anaerobically digested swine wastewater [16]. They found a growth of 75.7 mgl−1day−1 in raw diluted swine wastewater and a growth of 164.3–224.7 mgl−1day−1 in diluted swine wastewater supplemented with volatile fatty acids (acetic, propionic, and butyric). Depending on the amount of volatile fatty acids added, the fatty acid composition was ~43–57% saturated, ~8–10% monosaturated, and 35–48% polyunsaturated fatty acids. However, the fatty acid composition was not determined for microalgae grown in the raw diluted swine wastewater, which was the medium for the study below. In a study by Mulbry et al. (2008), the microalgae were grown in raw swine wastewater at different effluent loadings and had a growth of 6.8–10.7 gm−2 day−1 [17]. They found that the dominant algal species was Rhizoclonium sp. with a fatty acid composition of ~53–58% saturated, 16–20% monosaturated, and 22–26% polyunsaturated fatty acids, depending on the concentration of the swine waste. In this study, the polyunsaturated fatty acids were lower, but the microalgae culture was mixed. Another study by Wu et al. looked at the growth of

Nannochloropsis oculata in anaerobically and aerobically treated diluted swine wastewater [18].

The growth rate was 0.59 gl−1day−1 (50% diluted) and 0.42 gl−1day−1 (25% diluted). The fatty acid composition was determined to be ~39% (25% diluted) and ~38% (50% diluted) saturated, ~19%

(25% diluted) and ~17% (50% diluted) monosaturated, ~31% (25% diluted) and ~32% (50% diluted) polyunsaturated, and ~11% (25% diluted) and ~13% (50% diluted) undetermined fatty acids. Most of the studies above used digestion as a way of preparing the nutrients for microalgae growth, but none of these studies looked at ultrafiltration for swine wastewater purification.

Further, Porphyridium cruentum was not used in any of these swine wastewater studies, and it is felt that this alga is important to characterize because of its ability to make pharmaceuticals, food products, and fuels.

The red, unicellular microalgae Porphyridium (P.) cruentum, also called P. purpureum, has often been studied for its ability to produce high-value products, including phycobiliproteins and omega fatty acids [19]. While there have been limited reports, it has also been explored for its biofuel potential [20,21]. P. purpureum had a lipid productivity and carbohydrate production similar to or higher than the green microalgae Chlamydomonas reinhardtii [20]. Sandefur et al.

(2016) studied ultrafiltration for treating swine wastewater in the contaminant-free production of lipids using Porphyridium cruentum [22], and Kim et al. (2017) studied the same organism for use in bioethanol production [23].

To date, there have been no studies looking at P. cruentum grown in swine wastewater media for biofuel production and comparing this with culture media. It is desirable to establish that a biofuel-producing organism can be grown in swine wastewater with parity compared to culture media. The purpose of this study was to determine if the biofuel potential of P. cruentum would have significantly altered the growth or lipid composition when grown in swine wastewater. To

our knowledge, this is the first study to report the fatty acid composition of P. cruentum grown in swine wastewater compared with standard culture media.

3.3. Materials and Methods

3.3.1. Strain and Culture Medium The marine microalgae P. cruentum (CCMP1328) were obtained from the Provasoli-Guillard

National Center for Marine microalgae and Microbiota (NCMA, East Boothbay, ME, USA). P. cruentum cells are red, spherical and 5–8 μm in length. In the experiments, six different media were used. Each of them differed by the type of water used, the addition of nutrients, the medium type, and the presence of raw swine wastewater. The medium type was either a control medium

(CM) or a swine wastewater medium (SWM). L1-medium, which is a modified f/2 medium, was chosen as the control medium and for culture maintenance. It contained NaNO3, NaH2PO4·H2O,

Na2EDTA·2H2O, FeCl3·6H2O, MnCl2·4H2O, ZnSO4·7H2O, CoCl2·6H2O, CuSO4·5H2O,

Na2MoO4·2H2O, H2SeO3, NiSo4·6H2O, Na3VO4, K2CrO4, Thiamine HCl (Vitamin B1), cyanocobalamin (Vitamin B12), and seawater or saltwater. Filtered seawater was obtained from the National Center for Marine Algae and Microbiota (NCMA), W Eel Pond, Woods Hole,

Massachusetts, USA. Sodium chloride and distillated water were used to make a stock solution of

2.5% NaCl in deionized water for the saltwater solutions. P. cruentum was pre-cultured at 22 °C with natural illumination in a 500 ml glass bottle containing 250 ml of the sterilized medium

(autoclaved at 127 °C for 30 min).

3.3.2. Swine Wastewater Preparation and Ultrafiltration Swine wastewater samples were obtained from a manure holding lagoon located at a grow- finish swine farm in Savoy, AR, USA, as previously described in Sandefur et al. [22]. An ultrafiltration system was used to remove biological contaminants and inorganic solids.

Ultrafiltration (UF) has previously been used for wastewater treatment to achieve regulatory levels

of total suspended solids, chemical oxygen demand, and coliform levels [24,25]. The UF system included 1-inch hollow fiber membrane cartridges (50,000 MWCO; Koch Romicon PM50,

Wilmington, MA, USA) and was operated at a transmembrane pressure of 17.5 psi. The permeate samples were taken after two hours of ultrafiltration operation in the recycle mode. After processing, the permeate samples were cultured using the IDEXX Colilert method (IDEXX

Laboratories, Westbrook, ME, USA) [26] to check for the presence of E. coli and coliforms.

Additionally, the permeate samples were analyzed for total phosphorus (TP), total nitrogen (TN), total organic carbon (TOC), and ammonia-N using APHA (American Public Health Association) methods [26]. After ultrafiltration, the complete rejection of E. coli and coliforms was observed for the swine wastewater samples (<1.0 CFU ml−1). The concentrations of TP, TN, ammonia-N, and TOC in the permeate samples were 69.1, 695.6, 422.8 and 598.0 mgl−1, respectively.

Additional characterization information for the ultra-filtered swine wastewater is available in

Sandefur et al. [22].

3.3.3. Microalgae Growth Experiments Algal cultivation was performed in 150 ml corning sterile bottles from VWR International, a global laboratory supplier (Radnor, PA, USA). The inoculum volume for each sample was 5 ml containing 5000 cells ml−1, which was obtained from the pre-cultured microalgae in the early exponential growth phase. There were six different media used to investigate the effects of swine wastewater on microalgae growth rate. The total volume of the medium added to each microalgae sample was 95 ml. The control media (prepared as described in the culture medium section) were solutions based on either seawater or saltwater. The UF-treated swine wastewater media consisted of 65 ml of swine wastewater and 30 ml of appropriate control medium. The details of the microalgae growth media are given in Table 1.

Table 1: Microalgae growth media compositions. Sample Water Medium Contents Name Type 95 ml seawater medium, 5 ml C-SEA Control Seawater algae SW-UF- Swine 30 ml seawater, 65 ml swine Seawater SEA Waste waste, 5 ml algae 95 ml saltwater medium, 5 ml C-SALT Control Salt Water algae 30 ml deionized water, 65 ml SW-UF- Swine Salt Water swine waste, 1.6 g NaCl, 5 ml SALT Waste algae

During the experiment, the containers were maintained at ambient laboratory temperature

(18–22 °C) and illuminated using four fluorescent lamps under a light-dark cycle of 13:11 hours, respectively. The average light intensity was 140 (130–150) E m−2s−1. This condition was selected according to previous studies on the optimum growth condition of P. cruentum [27]. The biomass was harvested after 24 days in the stationary phase, using centrifugation at 2800 rcf for

15 min in 50 ml falcon tubes. Microalgae was harvested in the stationary phase because, when making polyunsaturated fats, it is often required to do nutrient starvation to force the desired product breakdown. The harvested biomass pellets were washed with deionized water to remove mineral salt precipitates, and then were lyophilized for direct transesterification. The biomass productivity was calculated using the Equation below:

Biomass productivity (mgl−1day−1) = (5) dried microalgae biomass (mg)

working volume (l) × cultivation day

3.3.4. Direct Transesterification Direct transesterification was used because it can be used on smaller sample sizes and has been shown to have consistent results with traditional transesterification. For direct

transesterification, the samples were weighed and moved to 10 ml flasks [28]. Then, a solution of

H2SO4/methanol with a final volume ratio of 5:100, respectively, was added into the flasks. The flasks were stirred at 70 °C for one hour since transesterification and extraction were being performed in the same step. After one hour, the flasks were cooled down to room temperature by running tap water over the outside of the flask. Next, 2 ml of hexane and 0.75 ml of distilled water were added to the flasks, and all of the flasks were vortexed for 30 s. After vortexing, the mixture had two phases: the upper hexane layer containing the fatty acid methyl esters (FAMEs) and the lower aqueous layer containing the residues. In the last step of direct transesterification, the upper hexane layer was transferred to gas chromatography (GC) vials.

Although lipids are traditionally extracted from microalgae before transesterification, in situ transesterification or direct transesterification can be performed by contacting biomass directly with the alcohol and catalyst required. This process reduces the number of unit operations to produce FAMEs from biomass [29]. This process was used to convert the biomass of P. cruentum into FAMEs. The major fatty acid composition of the tested microalgae was determined by using

GC analysis. Mass fractions were normalized according to the total fatty acids found from the GC analysis.

3.3.5. Gas Chromatography Analysis In order to analyze FAMEs, the gas chromatograph, GC-2014 (Shimadzu, Columbia, MD,

USA) equipped with a flame ionization detector (FID) and an auto sampler was used. The GC column used to separate the FAMEs was a Zebron™ZB-FFAP polar capillary column (30 m ×

0.32 mm × 0.25 µm) film thickness; Phenomenex, Torrance, CA, USA). Helium was used as a carrier gas with a linear velocity of 35 cm/s. The column temperature was programmed from 150

(held for 3 min) to 240 °C at 1.5 °C/min. Sample volumes of 2 µl were injected with a split ratio

of 10:1. The detector temperature was set at 250 °C. The peaks obtained from the GC were compared with Marine Oil Test Mix. and Fame #13 Mix (Restek Corp., Bellefonte, PA, USA)

FAME standards.

3.3.6. Statistical Analysis Statistical differences in the data were determined using GraphPad (GraphPad Software Inc.,

San Diego, CA, USA). GraphPad QuickCalcs was used for the unpaired t-test and GraphPad Prism

(version 8.3.1) was used for one-way, nonparametric ANOVA and Tukey analysis. While the t- test and Tukey compared the average of individual values to each other to determine statistical significance, ANOVA was used to compare multiple values to each other. Values were considered to have a statistically significant difference if the p value was less than 0.05.

3.4. Results and Discussion

3.4.1. Growth and Productivity The growth and fatty acid productivity of P. cruentum was evaluated in control and diluted ultra-filtered swine wastewater. Figure 1 shows the biomass productivity of each culture. For the samples grown in seawater, those containing treated swine wastewater (SW-UF-SEA) had almost double the average biomass productivity (5.18 mgl−1day−1) than those grown in the control medium

(C-SEA, 2.61 mgl−1day−1). Alternatively, for the samples grown in saltwater, those containing swine wastewater (SW-UF-SALT) had about half the average biomass productivity (3.31 mgl−1day−1) of those grown in the control medium (C-SALT, 6.52 mgl−1day−1). However, it is important to note that there was wide variation among the samples and, therefore, the average biomass was statistically the same. A previous study by Lee and Bazin (1991) determined that the optimum growth for P. cruentum occurred at a similar concentration of NaCl to our saltwater samples (0.42 M ~ 24.5 ppt), and that the next highest growth occurred at a concentration of NaCl

similar to our seawater concentration (0.59 M ~ 34.5 ppt) [30]. Overall, the average biomass productivity was statistically the same between the microalgae grown in the control media and the microalgae grown in the treated swine wastewater media, as indicated by p≥, as shown in Table 2, for the different analysis methods. This indicates that ultra-filtered swine wastewater can be used to grow microalgae (specifically P. cruentum) without any significant loss to the biomass productivity.

Table 2: Statistical analysis of biomass productivity for different growth conditions. ANOVA + Growth Conditions t test ANOVA Tukey C-SEA/SW-UF-SEA 0.26 0.32 0.14 C-SALT/SW-UF-SALT 0.06 0.08 C-SEA/C-SALT 0.06 0.13 0.08 SW-UF-SEA/SW-UF- 0.05 0.56 SALT Note: Values are considered statistically significant if p < 0.05. For the ANOVA from top to bottom the values compare all the seawater, all the saltwater, and all values to each other.

3.4.2. FAME Composition

There were interesting differences between the FAME compositions in all of the growth methods (Figure 2). P. cruentum observed for all of the growth conditions were C16:0 (palmitic acid; 42–51%), C18:0 (stearic acid; 19–30%), C20:5 (EPA; 6–10%), and C24:0 (lignoceric acid;

4–7%). Uncommon fatty acids (C14:0, C14:1, C22:0, C22:1, and C24:1) were either not observed or observed at very low values (<3%). These values agree with previous studies [31–33]. Several other fatty acids (C16:1, C18:2, C18:3, C20:0, C20:1, C20:3, and C20:4) were also either not observed or observed at low concentrations (≤5%). Although there was some variation in the exact composition of the fatty acids among the samples as shown in Figure 2, statistically they were the same. The values were statistically the same when comparing saltwater with seawater, as well as when comparing treated swine wastewater with control media. The similarity of the fatty acid compositions between the samples again indicates that swine wastewater media compares favorably with control media.

Figure 2: Fatty acid methyl esters (FAME) composition of P. cruentum grown in control and swine wastewater media. N = 2 for C-SEA and SW-UF-SEA, N = 1 for C-SALT and SW-UF- SALT.

As shown in Figure 3, P. cruentum has more saturated FAMEs (C-SEA: 91.3%, SW-UF-SEA:

74.09%, C-SALT: 77.40%, SW-UF-SALT: 93.47%) than the unsaturated (monounsaturated and polyunsaturated) FAMEs (C-SEA: 8.97%, SW-UF-SEA: 25.91%, C-SALT: 22.60%, SW-UF-

SALT: 6.53%). The composition of saturated or unsaturated fatty acids affects the quality of the biofuel produced from the microalgae. High levels of saturated fatty acids provided better combustion but lead to high kinematic viscosity [34]. Biodiesel with high levels of unsaturated fatty acids has optimum chemical properties, but higher NOx emissions and a lower cetane number that lead to longer ignition delays [35]. The ideal biodiesel, as an alternative to fossil fuels, should contain both saturated and unsaturated fatty acids with a higher portion of saturated fatty acids for the efficiency of fuel [34].

Figure 3: Saturated and unsaturated fatty acid compositions of P. cruentum grown in control and treated swine wastewater media. N = 2 for C-SEA and SW-UF-SEA, N = 1 for C-SALT and SW-UF-SALT.

3.5. Conclusion and Future Perspectives In this study, the growth and productivity of Porphyridium cruentum were examined in swine wastewater versus control media with different salinities. While the biomass productivity of P.

56 cruentum varied in the different media, there was no statistical difference between the swine wastewater and the control media. FAME analysis of P. cruentum grown in the control and swine wastewater media also showed no significant differences in composition. P. cruentum yielded a higher percentage of saturated fatty acids compared with unsaturated fatty acids, indicating that it has the potential to be used as a biofuel. Therefore, UF-treated swine wastewater has the potential to be used as an alternative growth medium for microalgae in biofuel production, which in turn will help with global issues of eutrophication.

The development of microalgae cultivation in swine wastewater has plenty of environmental benefits, due to the high growth rate of microalgae and environmental pollution control. However, these noteworthy results, achieved in swine wastewater-grown P. cruentum, promote the further investigation of this environmentally friendly method of microalgae cultivation, with the objective of improving their harvesting on a large scale. There are several studies that have focused on the strategies to achieve this target, such as the use of non-poisonous additives, bio-magnetic flocculant, and the genetic modification of microalgae [36–38]. These studies provided a good starting point for further research into overcoming the difficulties of the large-scale harvesting of

P. cruentum and other microalgae grown in swine wastewater.

Author Contributions: Conceptualization, H.B.U.E. and M.L.M.; methodology, H.B.U.E.,

M.L.M., H.S., E.G., J.V., and M.A.; formal analysis, H.B.U.E., M.L.M, C.H., writing-original draft preparation, H.B.U.E., writing-review and editing, H.B.U.E, C.H., and J.H., visualization,

H.B.U.E., and C.H., supervision, C.H. and J.H. All authors have read and agreed to the published version of the manuscript.

Funding: This research received no external funding.

Conflicts of Interest: The authors declare no conflict of interest.

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Chapter 4. Effects of Resin Chemistries on the Selective Removal of Industrially Relevant

Metal Ions Using Wafer-Enhanced Electrodeionization

Humeyra B. Ulusoy Erol, Christa N. Hestekin, Jamie A. Hestekin

Ralph E. Martin Department of Chemical Engineering, University of Arkansas, Fayetteville,

AR, United States, 72701

“Ulusoy Erol, H.B.; Hestekin, C.N.; Hestekin, J.A. Effects of Resin Chemistries on the Selective Removal of Industrially Relevant Metal Ions Using Wafer-Enhanced Electrodeionization. Membranes 2021, 11, 45. https://doi.org/10.3390/membranes11010045”

Abstract: Wafer-enhanced electrodeionization (WE-EDI) is an electrically driven separations technology that occurs under the influence of an applied electric field and heavily depends on ion exchange resin chemistry. Unlike filtration processes, WE-EDI can be used to selectively remove ions even from high concentration systems. Because every excess ion transported increases the operating costs, the selective separation offered by WE-EDI can provide a more energy-efficient and cost-effective process, especially for highly concentrated salt solutions. This work reports the performance comparison of four commonly used cation exchange resins (Amberlite IR120 Na+, Amberlite IRP 69, Dowex MAC 3 H+, and Amberlite

CG 50) and their influence on the current efficiency and selectivity for the removal of cations from a highly concentrated salt stream. The current efficiencies were high for all the resin types studied. Results also revealed that weak cation exchange resins favor the transport of the monovalent ion (Na+) while strong cation exchange resins either had no strong preference or preferred to transport the divalent ions (Ca2+ and Mg2+). Moreover, the strong cation exchange resins in powder form generally performed better in wafers than those in the bead form for the selective removal of divalent ions (selectivity > 1). To further understand the impact of particle

size, resins in the bead form were ground into a powder. After grinding the strong cation resins displayed similar behavior (more consistent current efficiency and preference for transporting divalent ions) to the strong cation resins in powder form. This indicates the importance of resin size in the performance of wafers.

Keywords: selective separation; ion-exchange resin; wafer-enhanced electrodeionization; desalination

4.1. Introduction

The increase in population and industrial development has triggered physical and economic water scarcity. For instance, in various industries such as the semiconductor, pharmaceutical, power, and hydraulic fracturing industries, an average facility can use 2 to4 million gallons of water per day [1]. Specifically, the consumption of large volumes of fresh water and the generation of highly contaminated wastewater has drawn negative attention from both the public and environmental groups. Besides this attention, excessive freshwater use can create hardships for industries, households, farmers, and wildlife [2]. Hydraulic fracturing, commonly known as fracking, is used to release natural gas and oil and also uses large amounts of water in its production [3,4]. Produced wastewater contains a high concentration of dissolved solids which often exceeds 50,000 parts per million (ppm) and is about 2–6 times higher than seawater concentration [5]. The fracking wastewater contains divalent cations (such as calcium and magnesium) and monovalent ions (such as sodium and potassium) as well as other anions, chemicals, and bacteria [6].

Due to the high concentration of dissolved solids, fracking wastewater can threaten the environment and alter the health of agriculture, aquatic life, and humans. Considering the health threats, fracking water cannot be discharged into freshwater streams or treated at municipal

wastewater treatment plants. Currently, there are several ways to dispose of fracking wastewater with the cost ranging from $1 to $10 per barrel [7]. In addition, logistics and water hauling can increase the water management costs when the disposal outlet is not nearby, and it may increase the cost of disposal to $94 per barrel per hour of transport [7].

Hence, there is a need for on-site wastewater treatment to minimize the freshwater use and damaging effects of fracking wastewater. If the wastewater can be reused or reduced, then the expenses from transportation and disposal can be decreased or eliminated. Membrane-based technologies have become a remedy for the removal of particulates, ionic, gaseous, and organic impurities from aqueous streams without the use of hazardous chemicals due to their reliability and cost-effectiveness. Wastewater treatment technologies using membranes appear to be the more practical and feasible strategies to overcome one of the primary issues the world faces; the shortage of freshwater supplies and degradation of water quality [8]. Membrane technologies also have essential advantages such as the simplicity of operation, high flexibility and stability

[9], low energy requirements [10], high economic compatibility [11], and easy control of operations and scale-up under abroad array of operating conditions and good compatibility between different integrated membrane system operations [12].

Electrodeionization (EDI) is a hybrid technology that is based on electrodialysis (ED), which employs electrical current and semi-impermeable membranes, and ion exchange (IE)that contains ion exchange resins [13] to overcome the disadvantages of both technologies such as concentration polarization, chemical regeneration [14], and excessive power utilization at low ion concentrations [15–17]. EDI can be operated in both continuous and batch modes and does not require a separate step to regenerate resins. Furthermore, EDI can work with low concentration streams with a lower power requirement compared to ED [15,16,18].

Even though there are major advantages of EDI over ED and ion exchange processes, there are also several disadvantages of EDI. The ion exchange resins are inserted into a pair of anionic- and cationic-exchange membranes loosely. This loose resin structure complicates sealing between compartments and leads to leakage of ions from one compartment to another due to convection instead of diffusion [19,20]. Another disadvantage of loose resins in EDI systems is the uneven distribution of flow within the channels which decreases the separation efficiency [20–23]. Previous studies have found ways to eliminate leakage issues by using spiral- wound configurations [24] or the channeling problem by immobilizing the resin using magnetic fields [25]. Each method was able to eliminate only one of the disadvantages of conventional

EDI. Therefore, there was a need for a new system specifically designed to overcome both disadvantages. As a result, an integrated approach, wafer-enhanced electrodeionization (WE-

EDI), was proposed by Arora et al. [26].

The wafer-enhanced electrodeionization (WE-EDI) is one of the methods that enable on- site wastewater treatments and maintenance, and removal of hardness causing ions and metals

[26,27]. In WE-EDI, the loose ion exchange resin structure of conventional EDI is replaced by a wafer inserted between the two membranes as the spacer. The wafer is a mixture of immobilized cation- and anion-exchange resins using a polymer as a binding agent. Compared to conventional

EDI, WE-EDI can be easily built and run more efficiently, and it prevents uneven flow distribution and leakage of ions between the compartments simultaneously [28]. Because there is less leakage, WE-EDI can be used for more selective separations such as the removal of acidic impurities from corn stove hydrolysate liquor, CO2 capture, and purification of organic acids

[26,29].

Besides treating wastewater for the removal of impurities, there is a need for an efficient and economical process of ion-selective separation. In wastewater treatment processes, not every ion has the same priority to be removed. Depending on the application, the user may need a selective removal of an ion relative to the remaining ions in the system. Also, because every ion transported that does not need to be transported increases the operating costs, there is a need for ion selectivity to create an energy-efficient and cost-effective process. Ion selectivity in WE-EDI processes heavily depends on ion exchange resin chemistry [23]. However, there are no studies that show the effect of commonly used resins (Amberlite IR 120 Na+, Amberlite IRP 69,

Amberlite CG 50, and Dowex MAC 3 H+) on the ion selectivity and current efficiency in systems with a high salt concentration to the best of our knowledge. Amberlite IR 120 Na+ and

Amberlite IRP 69 are strong cation exchange resins whereas Amberlite CG 50 and Dowex MAC

3 H+ are weak cation exchange resins. These resins are widely used in applications of conventional EDI and ion exchange chromatography such as metal removal [30–32], water softening [33,34], drug delivery [35], and enzyme immobilization and purification [36,37].

While these four resins have been commonly used in applications requiring ion transport at low salt concentrations, this study explores their use for selective and energy-efficient removal of ions in a highly concentrated system using wafer-enhanced electrodeionization (WE-EDI). The unique wafers used in WE-EDI enhance the effects of transport by diffusion. Therefore, the effect of resin size in resins with the same chemistry was also evaluated.

4.2. Materials and Methods

4.2.1. Chemicals

Cationic exchange resins (Amberlite IR 120 Na+, Amberlite IRP 69, Dowex MAC 3 H+, and

Amberlite CG 50), anionic exchange resin (Amberlite IRA-400 Cl−), sucrose, low-density polyethylene, sodium chloride, magnesium chloride, and calcium chloride were purchased from

VWR International. The technical specifications of each resin are shown in Table 1. Neosepta food-grade anionic and cationic exchange membranes (AMX and CMX, respectively) were purchased from Ameridia Innovative Solutions, Inc. (Somerset, NJ, USA).

Table 1: Cation exchange resins and their properties.

Functional Particle Size Exchange Name Matrix Group (Mesh) * Capacity (eq/L) 16–50 mesh Amberlite IR120 Styrene-divinylbenzene Sulfonic Acid (0.297 to 1.19 ≥2.0 Na+ (gel) mm) 100–200 mesh Amberlite IRP Crosslinked styrene- Sulfonic Acid (0.074 to 5 69 divinylbenzene 0.149 mm) 16–50 mesh Dowex MAC 3 Carboxylic Polyacrylic- (0.297 to 1.19 3.8 H+ Acid divinylbenzene (gel) mm) 100–200 mesh Amberlite CG Carboxylic Methacrylic (0.074 to 3.5 50 Acid (macroporous) 0.149 mm) *: Mesh is a measurement for the particle size that is used to determine the particle size distribution of a granular material. Particle size conversion (mesh to mm) was determined from [38].

4.2.2. Wafer Composition, Fabrication and System Setup

The wafer recipe has been previously published [23], but briefly consists of anion and cation exchange resins, polymer, and sucrose (Figure 1). The cationic exchange resins used were

Amberlite IR 120 Na+, Amberlite IRP 69, Dowex MAC 3 H+, and Amberlite CG 50. The first two are strong cationic exchange resins and the latter two are weak cationic exchange resins. The

anion exchange resin bead was Amberlite IRA 400 Cl−. Polyethylene (500 micron-low density) and sucrose were used to bind the resins and create porosity, respectively. The ratios of cation exchange resin, anionic resin, polymer, and sucrose in the mixture were 23:23:10:15, respectively. The mixture then was uniformly combined using a FlackTeck Inc (Landrum, SC,

USA). SpeedMixer™ (model: DAC 150 SP) at a rate of 300 rpm for 5 s. The combined mixture for the wafer was cast in a steel mold and placed in a Carver press (model 3851-0) heated to

250◦F at 10,000 psi for ninety min. This process was followed by a 20-min cooling period via pressurized air treatment. The wafer was pre-soaked in deionized (DI) water for 24-h to create porosity. The thickness of the final product was 2 mm. The wafer was then cut to size to fit within the WE-EDI cell.

Membranes used in the WE-EDI system were Neosepta food-grade AMX and CMX membranes and were conditioned in the dilute (feed) solution (described in the next section) 24 h prior to the experiments. WE-EDI was performed within a Micro Flow Cell (ElectroCell North

America, Inc.). The MicroFlow Cell was tightened to 25 in-lbs across all bolts to ensure even flow throughout the system and prevent leakage. The cations tested for selective separation were

Na+, Ca2+, and Mg2+ and the counter ion for all cations was Cl−.

Figure 1: Illustration of typical wafer fabrication and particle size reduction (grinding) of ion exchange resins for wafer fabrication.

4.2.3. Size Reduction for IR 120 Na

In order to compare the effects of bead size on the system performance, the size of the IR 120

Na resins was reduced. The IR 120 Na resins were firstly washed with deionized water, and then dried using freeze dryer (Labconco FreeZone Plus 12 Liter #7960044, Kansas City, MO, USA).

Dried resins were ground using mortar and pestle and passed through sieves to get resin particles of less than 149 m (100 mesh). Ground resins were then used to make a wafer using the same recipe given in Section 4.2.2.

4.2.4. Particle Image Analysis

Both the original IR 120 Na and the ground IR 120 Na resins were examined with the microscope. The calibration and particle size detection was completed with ImageJ image processing tool [39].

4.2.5. WE-EDI Chamber Setup and Sample Collection

The setup (Figure 2) for ion removal used four separate solutions of equal volume. The concentrate solution was 300 mL of 2% wt (20 g/L in DI water) sodium chloride solution and

both rinse chamber solutions were 300 mL of 0.3 M (42.6 g/L in DI water) sodium sulfate

(Na2SO4). The feed (dilute) was 50,000 ppm sodium (126.8 g of NaCl/L in DI water), 1,000 ppm of calcium (2.7 g of CaCl2/L in DI water), and 1,000 ppm of magnesium (3.9 g of MgCl2/L in DI water) mixtures for the dilute chamber solutions. The dilute (feed) stream is the stream which ions are being transported out of (removed).

All experiments were performed in a continuous mode with recycling. A constant current of 0.2

Amps was used for all experiments. Experiments were run for 8 h, with samples collected at the initial (0-h), 2-h, 4-h, and 8-h marks. To determine the concentration of individual ions, ion chromatography (Dionex ™ ICS-6000 Standard Bore and Microbore HPIC ™ Systems, Thermo

Scientific, Waltham, MA, USA) was used because of its speed, precision, and sensitivity.

Figure 2: Illustration for wafer-enhanced electrodeionization (EDI) setup

4.2.6. Statistical Analysis

Statistical differences in the data were determined using an unpaired t-test in GraphPad

QuickCalcs (GraphPad Software Inc., San Diego, CA, USA). Values were considered to have a statistically significant difference if the p value was less than 0.05.

4.2.7. FTIR-ATR Spectroscopy

The bead chemistry of the resin and their properties in the wafer were identified using Fourier

Transform Infrared – Attenuated Total Reflection (FTIR-ATR) Spectroscopy (Perkin Elmer

LR64912C, Waltham, MA, USA). The individual peaks were evaluated in terms of wavenumber and intensity.

4.3. Results and Discussion

4.3.1. Current Efficiency

The current efficiency (휂) for the WE-EDI system indicates how efficiently a particular ion is being transferred across the membranes and the wafer due to the electrical field applied to the system. It is defined as:

푧퐹푉(퐶 −퐶 ) 휂 = 푖 푓 푥 100% (6) 푡퐼푀푤

where z is the ionic valence of the ion (2 for calcium and magnesium, and 1 for sodium), F is the Faraday’s constant, V is the volume of the feed chamber, Ci is the initial concentration of the feed chamber, Cf is the final concentration of the feed chamber, t is the total operation time, I is the current, and Mw is the molecular weight of the ion.

Figure 3 shows that the total current efficiency is similar between weak cation exchange and strong cation exchange wafers. The total current efficiency for each strong cation exchange resin wafer was close to 100% and for each weak cation, resin wafer was over 100%. While current efficiencies should be below 100%, other studies have previously reported efficiencies greater

than 100%. Pan et al., showed that current efficiency increased in resin wafer EDI as the ion concentration in the dilute stream increased [20]. Luo and Wu [40] observed that the overall current efficiency of their system was greater than 100% at high concentrations. Lopez and

Hestekin [29] reported that high ion diffusion during the experiment coupled with ion transport due to potential gradients can cause greater than 100% current efficiency. Another reason why these current efficiencies may exceed 100% is that the concentration of the solution in the dilute chamber is higher than in the concentrate chamber and therefore the electrically driven transport is being assisted by the concentration gradient. In this study, the strong cation exchange IRP 69 resin wafer had a current efficiency that was more consistently approximately 100% whereas the

IR 120 Na+ wafer showed a lot of variabilities, which makes it less desirable for the selective removal of ions. In terms of the weak cation exchange resin wafers, both resin wafers showed similar average values and smaller variability in their current efficiencies.

140

120

100

Current Efficiency (%) Efficiency Current 40

0 Dowex MAC 3 CG 50 IR 120 Na IRP 69 Wafer Weakly cationic resins Strongly cationic resins

Figure 3: Overall current efficiencies for strong (IR 120 Na+ and IRP 69) and weak cation exchange wafers (Dowex MAC 3 H+ and CG 50).

4.3.2. Selectivity

Selectivity is a measure of the removal rate of one ion compared to another. Selectivity is determined using the separation coefficient () that is calculated using the following equation:

푓 푠 푠 (퐶푖 −퐶푖 )/퐶푖 훼 = 푓 푠 푠 (7) (퐶푗 −퐶푗 )/퐶푗

f s where Ci is the final concentration of ion i (calcium or magnesium ion), Ci is the starting

f s concentration of ion i, Cj is the final concentration of ion j (sodium ion), and Cj is the starting concentration of ion j. If  is greater than one, it indicates the preferential transport of ion i. If  is less than one, then it indicates the preferential transport of ion j.

Figure 4 shows the selectivity values for calcium and magnesium relative to sodium for strongly and weakly cationic resin wafers. The selectivity of calcium to sodium was greater than one for the IRP 69 resin wafer (strong cation exchange) which indicated that calcium ions were preferentially transported compared to sodium ions. In the IR 120 Na+ resin (strong cation exchange), the selectivity for calcium relative to sodium was close to one which indicated that there was not a strong preference for the transport of sodium or calcium ions. The statistical analysis showed that there was a statistically significant difference between IR 120 Na+ and IRP

69 resins for calcium selectivity (p < 0.02). In Dowex MAC 3 H+ and CG 50 (weak cation exchange resin wafers), the selectivity values for calcium relative to sodium were less than one which indicated that both resin wafers prefer to transport sodium ions over calcium ions. Our statistical analysis showed no difference between Dowex MAC 3 H+ and CG 50 resin wafers for calcium removal (p > 0.2).

A similar situation was observed for the selectivity of magnesium relative to sodium. The

IRP 69 demonstrated a selectivity greater than one, indicating that magnesium was preferentially transported over sodium. For IR 120 Na+ resin, the selectivity was at or below one indicating that

there was no preference for the transport of magnesium. However, statistical analysis showed that the difference between IR 120 Na+ and IRP 69 resin for magnesium selectivity was not significant (p > 0.15). In the weak cation exchange resin wafers formed from Dowex MAC 3 H+ and CG 50, the selectivity values were less than one which indicated that both resin wafers preferred to transport sodium ions over magnesium ions. The statistical analysis showed no difference between Dowex MAC 3 H+ and CG 50 resin wafers for magnesium removal (p > 0.8).

Ca vs. Na Mg vs. Na 1.6

1.4

1.2

1.0

0.8

Selectivity 0.6

0.4

0.2

0.0 Dowex MAC 3 CG 50 IR 120 Na IRP 69 Wafer

It is well established that resins with sulfonic acid groups have a higher affinity for divalent ions than resins with carboxylic acid functional groups [41,42]. For the Amberlite IR 120 Na+ sulfonic acid resin, it has been previously reported that the order of selectivity is Ca2+ > Mg2+ >

Na+ [41]. Weak cation exchange resins, on the other hand, have more affinity towards

monovalent ions. Specifically, the carboxyl group exhibits a very high affinity towards H+ which may result in its lower affinity for other ions [42]. Alternatively, the sulfonic acid group has a higher affinity for Ca2+ and Mg2+ and a low affinity for Na+ and H+ [42].

A study by Zhang and Chen used EDI to separate ions in groundwater using Amberlite resins with sulfonic acid functional groups and their data indicated that there was no significant preference for divalent over monovalent ions [43]. However, it is important to note that they used different resins, had more types of ions present, and their system was at a much lower ion concentration. Another study using WE-EDI to remove ions from fracking water found that sulfonic acid resins (Amberlite 120 Na+) tended to have a preference for divalent cations more than carboxylic acid resins (Dowex MAC 3 H+) [44].

4.3.3. FTIR-ATR Spectroscopy Analysis

The IR 120 Na+ and IRP 69 resins have the same functional group of sulfonic acid which makes the resins strong cation exchangers. Since both resins had the same functional group, it was expected that their current efficiencies and selectivity values would be similar. However, it was observed that the IRP 69 wafer had a current efficiency that was consistently around 100% whereas IR 120 Na+ had a lower average value as well as a lot of variability, which made it less desirable for the selective removal of ions. Since these resins have the same chemistry, perhaps the difference in their performance was due to a variation in the accessibility of the active site.

To better understand their differences, FTIR-ATR was performed. As shown in Figure 5, four peaks were observed between 1000 cm−1 and 1200 cm−1 that correspond to sulfonic acid functional groups. The peaks between 1030 to 1200 cm−1 have been previously reported to correspond to the symmetric and asymmetric stretching vibration of the −SO3− group of sulfonic acid [45]. The peaks at ~1000 cm−1 have been typically associated with an S-O stretch. While

these groups were clearly present in IRP 69 wafer, their intensity was much lower in IR 120 Na+ wafer which indicated a significant decrease of the sulfur content and exposure of −SO3− groups.

Specifically, in the IR 120 Na+ wafer, the intensity of the sulfonic acid peaks was around 10% of the resin’s value while for IRP 69 wafer the peaks were 65–70% of the resin’s value (exact values are provided in Supplementary Table S1). This could indicate that polyethylene is covering the IR 120 Na+ resin’s larger bead form and thereby decreasing the availability of the sulfonic acid functional groups. This may explain the high variability seen in the current efficiency and selectivity of the IR 120 Na+ wafer.

Polyethylene Sulfonic Acid 1.2 Groups

1.0

0.8

0.6 Intensity 0.4

0.2

0.0

4000 3500 3000 2500 2000 1500 1000 500 Wavenumber (cm–1)

IRP 69 - Wafer IRP 69 - Resin IR120 Na - Wafer IR120 Na - Resin

Figure 5: The FTIR-ATR spectrum of strong cation exchange resins and wafers including these resins.

To verify that this was not the result of a single batch issue or due to analysis placement, another batch of IR 120 Na+ wafer was made and multiple locations were tested using FTIR-

ATR. Figure 6 shows that the second batch of IR 120 Na+ wafer also had lower intensities of sulfonic acid functional groups compared to the IR 120 Na+ resin, especially in the middle of the wafer (~10% of the resin’s value). While the edge of the wafer showed decreased intensity of the sulfonic acid functional groups compared to the resin, it was higher than the middle of the wafer with a value that was between 30–35% of the resin’s value (see Supplementary Table S1 for exact values). This could be due to the resin bead being more exposed at the edge of the wafer than it can be in the middle of the wafer. This finding supports the theory that the availability of the sulfonic acid functional groups of IR 120 Na+ have decreased availability possibility due to being covered by the polyethylene binding polymer.

A recent study by Palakkal et al. using SEM observed that polyethylene was partially covering their cation exchange resin (Purolite PFC100E) which had sulfonic acid functional groups and was a similar size to the Amberlite IR 120 Na+ resins at around 0.3 to 0.5 mm [28].

When they used an ionomer binder rather than polyethylene, they observed significantly less coverage of their cation exchange resin. Another possible reason for the difference between the intensity of the sulfonic acid functional groups between the resin and wafer could be due to thermal degradation during the wafer making process. However, a study by Singare et al. showed that during FTIR analysis the sulfonic acid group peaks for Amberlite 120 were present at a significant intensity up to 200 °C (392 °F) while they disappear at around 400 °C (752 °F) [46].

This is well above the wafer making temperature of 250 °F, which further supports the idea that the reduction is due to interactions with the binding polymer.

Sulfonic Acid Groups

Polyethylene 1.2

1.0

0.8

0.6 Intensity 0.4

0.2

0.0

4000 3500 3000 2500 2000 1500 1000 500 Wavenumber (cm-1) IR 120 Na - Resin IR 120 Na - Batch 1 IR 120 Na - Batch 2 (middle) IR 120 Na - Batch 2 (edge)

Figure 6: The FTIR-ATR spectrum of IR 120 Na resin alone and in two different wafers.

The weak cation exchange resins both have carboxylic acid functional groups which should have a peak between 1760 to 1690 cm−1 for the C=O stretch and a peak between 1320 to 1210 cm−1 for the C–O stretch [47]. Unlike the strong cation exchange resin wafers, the current efficiencies and selectivity values were similar between the two weak cation exchange resin wafers. However, the size of the cation exchange resins was also different between the Dowex

MAC 3 H+ (bead form) and the CG 50 (powder form). As shown in Figure 7, the intensity of the carboxylic acid functional groups for powdered CG 50 resin was only about 20% of the intensity of the Dowex MAC 3 H+ bead resin. Once incorporated into a wafer, the Dowex MAC 3 H+

wafer had around 10% of the peak intensity of the resin alone (exact values are provided in

Supplementary Table S2). For the CG 50 (powder) wafer, the wafer peak intensities were actually around 40–50% higher than the resin alone. As the CG 50 resin intensities were so much lower than the Dowex MAC 3 H+, it is possible that interference from other groups present in the wafer (from the polyethylene or anion exchange resin) led to the higher intensities.

Polyethylene Carboxylic Acid Groups

0.9

0.8

0.7

0.6

0.5

0.4 Intensity

0.3

0.2

0.1

0.0

4000 3500 3000 2500 2000 1500 1000 500 Wavenumber (cm-1) Dowex MAC 3 - Wafer Dowex MAC 3 - Resin CG 50 - Wafer CG 50 - Resin

Figure 7: The FTIR-ATR spectrum of weak cation exchange resins and wafers formed using these resins.

To confirm that the bead resins led to less availability of the function groups, two different batches and multiple wafer positions of Dowex MAC 3 H+ resin wafers were tested by FTIR-

ATR. In both batches, the intensity of the carboxylic acid functional groups was significantly reduced at both the edge and the middle with intensity values of around 10–18% of the resin alone (Figure 8, Supplementary Table S2). It is interesting to note that this reduction did not appear to have any effect on the performance of the Dowex MAC 3 H+ resin wafer unlike what was observed with the strong cation exchange resin bead (IR 120 Na+).

Carboxylic Acid Groups

1.2 Polyethylene

1.0

0.8

0.6 Intensity 0.4

0.2

0.0

4000 3500 3000 2500 2000 1500 1000 500 Wavenumber (cm-1) Dowex MAC 3 - Resin Dowex MAC 3 - Batch 1 Dowex MAC 3 - Batch 2 (middle) Dowex MAC 3 - Batch 2 (edge)

Figure 8: The FTIR-ATR spectrum of Dowex MAC 3 H+ resin alone and in two different wafers.

The difference might be explained by how the functional groups interact with the polyethylene. Sulfonic acid functional groups tend to attach to polyethylene. This behavior can be positive for membrane processes as it has been reported to increase ion transport [48] and

lower fouling [49]. However, this attachment may be decreasing the availability of sulfonic acid functional groups in the wafer and thereby, decreasing the efficiency and the performance of the resin wafer for the removal of ions from high concentration wastewaters.

4.3.4. Performance comparison of the powdered and bead form IR 120 Na

The interaction of polyethylene with the sulfonic acid groups does not fully explain the difference in performance between the two strong cation exchange resins. Therefore, we decided to evaluate if decreasing the particle size of the IR 120 Na+ resin would increase its performance when incorporated in a wafer. Using the same method outlined in Section 2.3, a new batch of wafers were produced from ground IR 120 Na+ resins.

Figure 9 clearly shows the particle size difference between the original IR 120 Na+ resin and the ground IR 120 Na+ resin. The original IR 120 Na+ resin had a particle diameter of 536 ± 65

µm (N = 8) and the ground IR 120 Na+ resin had a particle diameter of 30 ± 20 µm (N = 1101).

Figure 9: Optical microscopy images of (a) unground IR 120 Na+ resin and (b) ground IR 120 Na+ resin

Figure 10 shows the ground IR 120 Na+ wafer had a higher and less variable current efficiency compared to the unground IR 120 Na+ wafer. In addition, the ground IR 120 Na+ wafer looked similar in performance to the powdered IRP 69 resin wafer. However, it is important to note that all the current efficiency values were statistically the same (p > 0.4).

140

120

100

Current Efficiency (%) Efficiency Current 40

0 Unground IR 120 Na Ground IR 120 Na IRP 69 Wafer

Figure 10: Current efficiencies for unground bead form IR 120 Na+ and ground IR 120 Na+

In addition to current efficiency, the cation selectivity of the two different forms of the IR

120 Na+ resin in wafers were compared. As shown in Figure 11, the average selectivity of calcium to sodium of ground IR 120 Na+ wafer was greater than one which indicated that the ground IR 120 Na+ wafer preferentially transported calcium ions over sodium. For the unground

IR 120 Na+ resin, the selectivity was close to one which indicated that there was not a strong preference for the transport of sodium or calcium ions. However, statistical analysis showed that the difference between the wafer produced from ground versus unground IR 120 Na+ for calcium selectivity was not significant (p > 0.05). A similar situation was observed for the selectivity of magnesium over sodium. While the ground IR 120 Na+ demonstrated selectivity for magnesium over sodium which the unground did not, their values were statistically the same (p > 0.1) When compared to the powder resin IRP 69, the selectivity of ground IR 120 Na+ resin wafers were statistically the same (p > 0.05) for both calcium to sodium and magnesium to sodium. Overall,

significantly better performance was produced by wafers composed of the ground IR 120 Na+ resin compared to its bead form which indicates the importance of strong cation exchange resin size when being used in an electrodeionization wafer.

Ca vs. Na 1.8 Mg vs. Na

1.6

1.4

1.2

1.0

0.8 Selectivity 0.6

0.4

0.2

0.0 Unground IR 120 Na Ground IR 120 Na IRP 69 Wafer

Figure 11: Selectivity of the unground IR 120 Na+ and the ground IR 120 Na+.

4.4. Conclusion

Four different cation exchange resins were tested for their performance in electrodeionization wafers for the removal of monovalent and divalent cations. Wafers made from weak cation exchange resins and strong cation exchange resins showed similar current efficiencies, although they showed differences in their degree of variability. Based on the selectivity values, weak cation exchange resins seemed to favor the transport of the monovalent ion (sodium), while strong cation exchange resins either had no preference or a preference for the divalent ions (calcium and magnesium), which are usually the more valuable ions in wastewaters.

In addition, the strong cation exchange resins in powder form generally performed better in wafers for the selective removal of divalent ions. This could be due to a more homogeneous mixing with the other wafer materials or it could be due to differences in how it interacts with the polyethylene binding polymer during the formation of wafers. Specifically, wafers formed from

IRP 69 strong cation exchange resin in powder form gave the most promising results for the removal of divalent ions.

The positive impact of powder form was also verified by testing two different forms (ground vs. unground) of the same strong cation exchange resin for their performance in electrodeionization wafers for the removal of monovalent and divalent ions. The resin in powder form from the grinding process showed higher overall current efficiencies compared to the unground form (bead) of the resin. Based on the selectivity values, the ground resin seemed to favor the transport of divalent ions (calcium and magnesium) that are more valuable, while the unground resin did not show any preference for either monovalent or divalent ions.

Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1,

Table S1: FTIR sulfonic acid functional group peak intensity values for strong cation exchange resins alone and incorporated into wafers, Table S2: FTIR carboxylic acid functional group peak intensity values for weak cation exchange resins alone and incorporated into wafers.

Author Contributions: Conceptualization, H.B.U.E.; methodology, H.B.U.E.; validation,

H.B.U.E.; formal analysis, H.B.U.E.; writing-original draft preparation, H.B.U.E.; writing-review and editing, H.B.U.E., C.N.H., and J.A.H.; visualization, H.B.U.E.; supervision, C.N.H. and

J.A.H.; project administration, C.N.H. and J.A.H.; funding acquisition, C.N.H. and J.A.H.

Funding: Funding for this work was provided by the National Science Foundation

Industry/University Cooperative Research Center for Membrane Science, Engineering and

Technology (IIP 1361809 and IIP 1822101) and the University of Arkansas.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: Data is contained within the article or supplementary material.

Acknowledgments: Authors thank Erik Pollock of Biological Sciences at the University of

Arkansas for his help with the ion chromatography and John P. Moore II for his help with particle image analysis.

Conflicts of Interest: The authors declare no conflict of interest.

Supplementary Table S1: FTIR sulfonic acid functional group peak intensity values for strong cation exchange resins alone and incorporated into wafers

Wavelength IR 120 IR 120 Na+ IR 120 Na+ IR 120 Na+ IRP 69 IRP 69 (cm-1) Na+ resin wafer B1 wafer B2 wafer B2 resin wafer mid mid edge 1173 0.3435 0.0254 0.0181 0.1062 0.2431 0.1643 1126 0.3110 0.0243 0.0288 0.1032 0.2172 0.1531 1036 0.3222 0.0266 0.0430 0.1118 0.2221 0.1633 1008 0.3205 0.0263 0.0379 0.1112 0.2218 0.1629 B1 = batch 1, B2 = batch 2, mid = middle of wafer

Supplementary Table S2: FTIR carboxylic acid functional group peak intensity values for weak cation exchange resins alone and incorporated into wafers

Wavelength Dowex Dowex Dowex Dowex MAC CG 50 CG 50 (cm-1) MAC 3 MAC 3 H+ MAC 3 H+ 3 H+ wafer B2 resin wafer H+ resin wafer B1 wafer B2 edge mid mid 1760 - 1690 0.2463 0.0260 0.0406 0.0340 0.0504 0.0713 1320 - 1210 0.1742 0.0198 0.0314 0.0287 0.0315 0.0496 B1 = batch 1, B2 = batch 2, mid = middle of wafer

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Chapter 5. Modeling of Wafer-Enhanced Electrodeionization using Data Science and

Machine Learning Techniques

5.1. Introduction

Many industrial applications such as hydraulic fracturing, semiconductor, pharmaceutical, and power industries uses 2 to 4 million gallons of fresh water every day and produce the world’s majority of wastewater [1]. Even though there are different treatment methods for wastewater, these conventional metal removal methods such as coagulation, chemical precipitation, solvent extraction, biosorption, and ion exchange/adsorption on solid surfaces have major limitations including high capital costs, requiring regeneration and high land area, and a need for extra labor [2]. In addition, most membrane processes that are used for wastewater treatment cannot be used for metal removal as these metals are in ionized form of dissolved solids and they can pass through the membranes except reverse osmosis membranes. In reverse osmosis; however, the selective separation of ions cannot be performed.

Wafer enhanced electrodeionization (WE-EDI) is a widely used wastewater treatment technique that utilizes ion exchange membranes and ion exchange resins for the separation of ions. Wafer enhanced electrodeionization uses electricity as a driving force to transport ions across the membrane and resin wafer. The fundamentals of WE-EDI and its applications were studied in literature [3, 4, 5, 6].

In WE-EDI systems, not every ion has a priority to be removed. Depending on the application, the user may need a selective removal of an ion relative the remaining ions in the system. Also, because every ion transported that does not need to be transported costs money, there is a need for an energy and cost-effective measure and a process. Ion selectivity in WE-EDI processes depends on ion exchange resin chemistry [7, 8] as well as other parameters of the

process such as membrane and wafer thickness, exchange capacity of resins, membrane surface area, and resin bead size. In order to gain insights to optimize the design of WE-EDI units and scale it up for maximizing selectivity and efficiency, the modeling of the WE-EDI systems is crucial.

To understand the parameters and their effects on the electromembrane separation systems, different approaches have been investigated. One of the first models is by Glueckauf [9] that developed a steady state model of an electrodeionization (EDI) unit and derived an analytical solution for monovalent ion electrolyte solutions. However, this model was based on Fick’s law only and did not consider the electromigration. Another study by Verbeek et al. (1998) [10] developed a 2D model of an EDI unit that simulated the concentration profiles of ions in the liquid and solid phases. However, both studies focused on the conventional EDI units rather than the wafer-enhanced EDI. Additionally, these studies did not include all parameters that have an impact on the selectivity and the efficiency of the EDI system. Thus, they were limited due to oversimplification of transport mechanisms [9, 10].

One of the more recent studies by Mahmoud et al. [11] also focused experimental tests and modeling of an electrodeionization cell for the treatment of dilute copper solutions. Even though this study was more detailed compared to previous studies and compared the lengths of resin beds and their impact on the efficiency of the EDI system, this paper only focuses on single cation, moderately dilute solutions. Next, Lu et al. [12] developed a numerical simulation for an

EDI unit that accounts for water dissociation. They developed a 2D model and used COMSOL

Multiphysics for solving the nonlinear set of PDEs. However, this study had a discrepancy between the current efficiency of the model and experimental data. Additionally, this study did

not include the resin wafer structure as it was specifically modelled for the conventional EDI unit with single-cation solutions (except for H+ and OH-).

Kurup et al. (2009) [7] proposed to use steady state WE-EDI model that was an extension of

Glueckauf model [9]. Even though this paper claimed to model WE-EDI, the wafer was still modeled as a packed bed between two membranes. In this model, authors accounts for the presence of multiple ions and were able to predict experimental data practically well so that the optimization framework of this study can be adapted to other EDI systems. However, the most important variables of WE-EDI such as resin properties (size, ion exchange capacity, etc.) were not considered in the model.

The study by Sadrzadeh et al. (2009) [13] is one of the most recent studies that used artificial neural network to model a water treatment process for lead separation. They modeled the electrodialysis using a multilayer network with two hidden layers and were able to predict the separation percent and current efficiency of lead ions within 1% of standard deviation. This study leads the way for using data science and machine learning methods in electrochemical wastewater treatment methods as these methods have many promising features such as efficiency, simplicity and generalization.

Even though all these different studies focus on modeling of electromembrane separations, the modeling of wafer-enhanced electrodeionization with high concentration multi-ion solutions still remains complicated for the traditional mathematical modeling techniques. Data science and machine learning approach have been successfully used in other scientific and engineering application for years. However, there are no studies that uses data science and machine learning techniques in the modeling of ion removal from electromembrane separation systems to the best of my knowledge. In this study, the wafer-enhanced electrodeionization was modeled with data

science and machine learning techniques for a deeper understanding of the impact of ion exchange resins, the prediction of current efficiency and the final concentrations of sodium, calcium, and magnesium ions, as well as for the relevant feature selection.

5.2. Data Collection and Cleansing

Data is the fuel of a machine learning algorithm which makes collecting data one of the most crucial steps of the data science lifecycle. Having the right data and data quality are keys to constructing machine learning algorithms and successfully using in the other data science life cycle steps. In order to collect the most relevant data, it is important to assess two questions:

What kind of data do you need and how can you access it? To be able to answer these questions, it is essential to understand the problem that needs to be solved.

The question that needs to be answered in this dissertation is how to model a wafer-enhanced electrodeionization with high concentration multi-ion solutions in order to predict the performance of the system. The data of the WE-EDI model were gathered through real life experiments.

The samples from wafer-enhanced electrodeionization are collected at 0-hr, 2-hr, 4-hr and 8- hr. After samples are collected, they are diluted with dilution factor of eight (8) and given into the ion chromatography to obtain the cation (sodium, calcium, and magnesium) concentrations.

The detailed data collection method is given in Chapter 4. The collected experimental data were then restructured into the tabular format and cleansed to ensure there is not any empty cell in the tabular format. Tabular format is simply information presented in the form of a table with rows and columns. Each column represents input or output variables. Each row represents one type of wafer (IR 120 Na+, IRP 69, Dowex MAC 3 H+, and CG 50). Each experiment is indexed from

zero to 10 where 0 and 1 are CG 50 resin, 2-4 are Dowex MAC 3 resin, 5 and 6 are IRP 69 resin, and 7-10 are IR 120 Na resin.

Our input variables included 5 values (Table 1) and output variables included 3 outputs (Table

3). For different experiment, we formed our data into different formats. There are also 16 parameters that are constant but can be added into model when change in different experiments

(Table 2).

Table 1: Input Variables of Wafer-Enhanced EDI model Input Variables Unit Bead Size mm Total Exchange Capacity meq/g Sodium Initial Concentration g/L Calcium Initial Concentration g/L Magnesium Initial Concentration g/L

Table 2: Other parameters of WE-EDI model Other Parameters Unit Wafer Thickness mm Membrane Thickness mm Membrane Surface Area mm2 Sodium Molecular Weight g/mol Calcium Molecular Weight g/mol Magnesium Molecular Weight g/mol Sodium Initial Volume L Calcium Initial Volume L Magnesium Initial Volume L Faraday Constant Sec.Amps/mol Total Volume L Flow Rate L/sec Electric Current Amps Width of the Column mm Length of the Column mm Cell Number dimensionless

Table 3: Output variables for WE-EDI modeling Output Variables Units Sodium Final Concentration g/L Calcium Final Concentration g/L Magnesium Final Concentration g/L

5.3. Methodology

5.3.1. Development Environment Setup

For the modeling of wafer-enhanced electrodeionization, Python programming language and

Scikit-learn that is a free machine learning library for the Python were used. For the visualization, the matplotlib library of the Python was used. As a development environment,

PyCharm which is an integrated development environment (IDE) [14] for Python language was used.

5.3.2. Evaluation Metric

In this model, the mean squared error of scikit-learn were used for performance evaluation.

The error function is a function where the differences between actual value and predicted value is measured. The mean squared error (MSE) tells us how close a regression line is to a set of points, and it is calculated using the equation below.

(8)

where ŷi is the predicted value of i-th sample, and yiis the corresponding true value.

5.3.3. Model Selection

Model selection is a process of selecting one machine learning model from among a collection of candidate models. There are different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights such as supervised, unsupervised and semi-supervised learning.

Figure 1: Model selection pipeline for machine learning algorithms

For wafer-enhanced EDI modeling, supervised learning algorithms were chosen because their objective is to predict the mapping function so well that when we have new input data (X) that we can predict the output variables (y) for that data. Supervised learning is one of the most common branches of machine learning, and super learning algorithms are designed to “learn by example” [15, 16]. In supervised learning, we can use an algorithm to learn the mapping function from the input (X) to the output (y).

Mapping function: y = f(X) (9)

The goal is to estimate the function so well that we can predict the output variables (y) when we have new input data (X). The learning process in supervised learning has two steps: training

and testing. In training process, the learning algorithm receives features as input data along with their corresponding correct outputs. The algorithm learns by comparing the actual outputs to correct outputs to find errors. Then, the model is modified accordingly. In testing process, learning model makes prediction for the test data.

Figure 2: Operational model of supervised learning.

The common application of supervised learning is predicting future events based on historical data. Some examples of supervised learning includes anticipating fraudulent activities on credit card transactions, movie recommend systems for users, recognition systems for multicolor images to determine if they are a galaxy or star [17], or predicting the species of iris based on the measurements of its flower [18].

Supervised learning problems can be grouped into regression and classification problems based on the type of their output data. In both problems, the goal is constructing a succinct model that can predict the value of the dependent attribute from the attribute variables. The difference

between the two tasks is the fact that the dependent attribute is numerical for regression and categorical for classification. Based on this information, my modeling problem is a regression problem.

Figure 3: Diagram of linear regression overview [19].

5.3.4 Algorithm Selection and Evaluation

After the model selection, the next step is the algorithm selection. While choosing an algorithm, there is no straightforward answer because the answer depends on many factors such as the size of the training data, the accuracy/interpretability of the output, speed and training time, linearity, and the number of features.

For the size of training data, it is recommended to gather a good amount of data to get reliable predictions. However, the availability of data may often be a constraint. So, if the training data is smaller, it is better to choose algorithms with high bias/low variance like linear regression, Naïve Bayes, or linear support vector machine (SVM) [20]. On the other hand, algorithms like neural networks work well with massive data and a large number of features [21].

When we talk about interpretability of an algorithm, we are talking about its power to explain its predictions [21]. Accuracy of a model means that the function predicts a response value for a given observation which is close to the true response for that observation [20]. Algorithms like the k-nearest algorithm or linear regression are highly interpretable algorithms which means that one can easily understand how any individual predictor is associated with the response [20, 21].

Speed and training time of an algorithm is also related to accuracy as the higher accuracy typically means higher training time. Also, the algorithms require more time for training on larger datasets. Algorithms like Naïve Bayes and linear regression are easy to implement and quick to run whereas SVM, neural networks, and random forests need more time to train data

[20, 21, 22].

Understanding linearity of data is another necessary step for algorithm selection. It helps determining the regression line which guides us to the algorithms we can use. If the data can be separated by a straight line or if it can be represented by a linear model, then the SVM, linear regression, or logistic regression algorithms can be chosen. Otherwise, deep neural networks or ensemble models can be used [21, 20].

Lastly, the number of data points and features (rows and columns) have an essential function for choosing a suitable algorithm. While machine learning algorithms can be used with different datasets in terms of a size of data points and features, it is still important to understand how an algorithm handles different sized datasets to make training time feasible. For example, SVM can work with a limited number feature while neural networks can handle massive number data points and features. Also, principal component analysis (PCA) and feature selection techniques can be used to reduce the dimensionality of data and training time.

a. Multi-Output Regression and Leave One Out Cross Validation

Multioutput regression are regression problems that involve predicting two or more numerical values given an input example. With the information given in above, the wafer- enhanced modeling can be identified as a multioutput regression problem. Many machine learning algorithms are designed for predicting a single numeric value, referred to simply as regression.

In multioutput regression, typically the outputs are dependent upon the input and upon each other. This means that often the outputs are not independent of each other and may require a model that predicts both outputs together or each output contingent upon the other outputs which is also true for our problem. In order to solve this multioutput regression problem, the scikit- learn library multioutput regression module was utilized.

After training the model, it cannot just be assumed that the model is going to work well on data that it has not seen before. In other words, we cannot be sure that the model has the desired accuracy or variance. So, there is need for assurance of the accuracy of the predictions that the model is giving. Thus, it is important to evaluate and validate the model.

For evaluation of the multioutput regression, the cross-validation technique was used. Cross- validation is a technique used where the goal is prediction and to estimate how accurately a predictive model performs in practice. In cross validation, the dataset is divided into two groups: training set and testing set. Training set consists of a dataset of known data on which training is run. A dataset of unknown data (or first seen data) against which the model is tested is called testing set. The reason for using unknown data in testing is to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset.

However, the main problem with my dataset is its small size (11 rows). Usually the dataset used in data science experiments have more data points. To provide a better comparison, the number of data points in toy datasets of scikit-learn library and wafer-enhanced EDI dataset

(EDI) were plotted (Figure 4).

Figure 4: Comparison of the sizes of toy datasets of scikit-learn and WE-EDI.

Due to the small dataset size, the best suitable cross validation technique for the wafer- enhanced modeling was the leave-one-out cross validation. Leave-one-out cross validation is a special case of cross validation. In leave-one-out cross validation, the number of folds are equal to the number of instances in the dataset which means that the learning algorithm is applied once for each instance, using all other instances as a training set and using the selected instance as a single-item test set [23]. Working mechanism of leave-one-out cross validation is given below and in Figure 5.

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Figure 5: Illustration of leave-one-out cross validation [24]

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5.3.5. Neural Networks for Multioutputs

Neural networks (NNs), also known as artificial neural networks (ANNs), are a subset of machine learning and are at the heart of deep learning algorithms. Gurney et al defined neural networks as ‘an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron. The processing ability of the network is stored in the interunit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns’ [25]. The name and structure of neural networks are inspired by human brain, and they mimic the way that the biological neurons interact each other

[26].

Neural networks are comprised of node layers that contain an input layer, hidden layers, and an output layer (Figure 6). Each node, also known as artificial neuron, has an associated weight and threshold. If the output of individual node is above the threshold, that node is activated and sends data to the next layer in the network [26].

Neural networks models natively support multioutput regression and have the benefit of learning a continuous function that can model a more ‘graceful relationship’ between changes in input and output [27]. Multi-output regression can be supported directly by neural networks simply by specifying the number of target variables there are in the problem as the number of nodes in the output layer. For example, a task that has three output variables will require a neural network output layer with three nodes in the output layer, each with the linear (default) activation function.

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Figure 6: Neural network structure

In this study, I used Scikit-learn multi-layer perceptron regressor (MLPRegressor) [28]. My

WE-EDI dataset has five inputs and three outputs; therefore, the network requires an input layer that expects five inputs. As an activation function, I used popular rectified linear unit (ReLU) in the hidden layer [29]. The hidden layer in my network has 100 nodes, which were chosen after optimization. I fit the model using mean squared error.

Neural networks rely on training data to learn and improve their accuracy over time. If the dataset is small, it is good practice to evaluate neural network models repeatedly on the same dataset and report the mean performance across the repeats. For cross validation, I used leave- one-out cross validation that is described in the previous section. The MLP model was evaluated and returned an evaluation score, in my case, MSE score (described in section 5.3.2.).

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5.3.6. Feature Selection

Feature selection is the process of identifying and selecting the input variables that are most relevant to the target output variable. Feature selection on regression predictive modeling is the simplest case as both the input variables and target variable are numerical values. There are three popular feature selection or elimination techniques that can be used for numerical input data and numerical target variables. They are correlation feature selection, mutual information feature selection, and recursive feature elimination.

Correlation is a well-known measure of how two variables change together [30]. Pearson’s

Correlation is the most common correlation measure that assumes a Gaussian distribution to each variable and gives their relationship. According to correlation, if two features are linearly dependent, their correlation coefficient is between 1 and -1. If they are uncorrelated that means they have no relationship, and their correlation coefficient is 0. But for the feature selection, we are interested in a positive score with the larger value because the larger the positive value, the larger the relationship. In the literature the linear correlation coefficient (r) for a pair of variables

(X, Y) is calculated by:

∑(푋푖−푋̅푖)(푌푖−푌̅푖) 푟 = 2 2 (10) √∑(푋푖−푋̅푖) √∑(푌푖−푌̅푖)

where Xi and Yi are the individual points indexed with i, and 푋̅ and 푌̅ are the mean values.

The scikit-learn machine learning library provides and implementation of this correlation in the f_regression() function [31]. This function is not a free-standing feature selection procedure but a scoring function that can be used in a feature selection procedure.

The next feature selection technique is the mutual information feature selection. Mutual information is the application of information gain in the field of information theory and calculated between two variables. It measures the dependency between the variables. The mutual

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information is equal to zero if and only if two random variables are independent. Higher mutual information values mean higher dependency [32].

The scikit-learn machine learning library, again, provides an implementation for mutual information feature selection in the mutual_info_regression() function [33].

The next feature selection algorithm is the recursive feature elimination, or RFE for short, which is a popular feature selection algorithm. RFE is popular because it is easy to configure and use and because it is effective at selecting those features (columns) in a training dataset that are more or most relevant in predicting the target variable. Using RFE, I was interested in which features to select and which to remove for the best possible outcome.

Also, because most variables do not actually “vary” in our experiments and our dataset is relatively small, we may not be able to successfully choose the most relevant features. For the best results, it would be better to work with different variable values and bigger datasets in future studies.

5.4. Experiments

5.4.1. Run01-Multioutput Regression

The full configurations of Run01 can be seen in Table 4. For Run01, I utilized the multioutput regression and leave-one-out cross validation, the concentrations of sodium, calcium and magnesium were predicted. Figure show the observed and predicted sodium, calcium, and magnesium ion concentrations. These figures indicate that my model was able to predict the sodium concentration with 87% accuracy.

For the prediction of calcium and magnesium concentrations, this accuracy is lower compared to sodium. However, the model was able to simulate the similar trends of concentration change for both calcium and magnesium as the experimental data.

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Next, the mean squared error was calculated for the Run01. According to Figure 10, it is observed that the 2nd and 6th indexed models are the best models for my dataset as they are the closest to regression line. If we look at these experiments in terms of real-life experiments done in the lab, we see that some experiments are more consistent compared to others. For example, the most consistent experiments are CG 50 and IRP 69 resin wafer experiments (Chapter 4).

These resins are in powder form, and in real-life experiments, these resin wafers also give better results than others. IR 120 Na had the most variability in data science experimental results. We see this inconsistency here again with IR 120 Na.

Table 4: Configurations for the Run01 Run Configurations Experiment Name Run01 Input Data 5 columns Output Data 3 columns (ion concentrations) Regressor Model Multioutput Regression

Figure 7: Comparison of actual and predicted sodium concentrations for the Run01

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Figure 8: Comparison of actual and predicted calcium concentrations for the Run01

Figure 9: Comparison of actual and predicted calcium concentrations for the Run01

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Figure 10: Mean squared error scores for the Run 01

5.4.2. Run02 - Single Output (Current Efficiency)

The full configurations of Run02 can be seen in Table 5. For Run02, I again utilized the multioutput regression and leave-one-out cross validation, and the current efficiency values were predicted. Figure 11 shows the observed and predicted values of current efficiency. The single output wafer-enhanced EDI model was able to predict the current efficiency values with approximately 7% difference (Figure 11). The outlier was still observed in the 7th indexed model which is one of the IR 120 Na+ wafer experiments whereas experiments with other resin wafers showed consistency. The similar trend can also be observed in the mean squared error scores

(Figure 12). It can be seen that the CG 50 (0th and 1st) and IRP 69 (5th and 6th) resin wafer experiments had the most consistent results. IR 120 Na+ (7th through 10th) resin wafer, again, gave the most inconsistent result. In fact, the outlier was again observed with IR 120 Na+ resin.

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Table 5: Configurations for Run02 Run Configurations Experiment Name Run02 Input Data 5 columns Output Data Current efficiency Regressor Model Linear Regression

Figure 11: Comparison of actual and predicted current efficiency values

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Figure 12: The mean squared error scores for the Run02

5.4.3. Run03-Neural Networks Multi-Layer Perceptron Regressor

The full configurations of Run03 can be seen in Table 6. I used Scikit-learn multi-layer perceptron regressor (MLPRegressor), and the concentrations of sodium, calcium and magnesium were predicted (Figures 12 -14). For sodium concentration predictions in Figure, 0th,

2nd, 4th, and 9th indexed experiments resulted in significantly increased results. The remaining experiments were predicted with the same performance as multioutput regression explained in

Run01. For the predictions of calcium and magnesium concentrations, the performance of neural networks MLP regressor is lower compared to sodium. However, the MLP regressor was able to closely predict more experiments in terms of magnesium concentrations compared to calcium concentrations.

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Table 6: Configurations for Run03 Run Configurations Experiment Name Run03 Input Layer 5 Nodes Output Layer Multioutput Hidden Layer 100 Nodes Regressor Model MLP Regressor

Figure 12: Comparison of actual and predicted sodium concentrations for the Run03

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Figure 13: Comparison of actual and predicted calcium concentrations for the Run03

Figure 14: Comparison of actual and predicted magnesium concentrations for the Run03

Next, the mean squared error was calculated for the Run03. According to Figure 15, it was observed that 0th and 1st indexed models are the best models for my dataset with MLP

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regressor as they are closest to the regression line. Compared to multioutput regression, neural network MLP regressor performed significantly better at predicted concentrations. This result was expected because neural networks use perceptions to perform forward or backward propagation over data versus linear regression which attempts to fit most examples along the line. Due to this, neural networks are resistant to outliers and other factors that might cause under/overfitting of data, especially if nature of data is unknown. Thus, linear regression is better for simpler modelling while neural network is better for complex or multiple-level/category modelling like WE-EDI modeling.

Figure 15: Mean squared error scores for the Run03

5.5. Conclusions

Wafer-enhanced electrodeionization is one of the most promising wastewater technologies.

Through this research, the modeling of wafer-enhanced electrodeionization with high

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concentration multi-ion solution has been accomplished. This paper is the first study that uses data science and machine learning techniques for the modeling of wafer-enhanced electrodeionization with high concentration multi-ion solutions. With the use of data science and machine learning, the sodium, calcium, and magnesium ion concentrations were predicted with multioutput regression and neural networks multilayer perceptron (NN-MLP), and the observed effects of different resin wafers were confirmed using both multioutput and single output regression as well as leave-one-out cross validation and NN-MLP. This modeling approach also serves as a bridge to close the gap in modeling applications for electromembrane separations as it includes all variables that were not possible to be integrated in traditional physical models.

Acknowledgements

The author would like to acknowledge the Membrane, Science, Engineering, and Technology

(MAST) NSF Industry/University Cooperative Research Center for the funding on this project.

The author also thanks Recep Erol for his mentorship on data science approach.

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Chapter 6. Conclusion

Wastewater Treatment with Microalgae and Wafer-Enhanced Electrodeionization

In this dissertation, microalgae and wafer-enhanced electrodeionization as well as its modeling were investigated for use in wastewater treatment. Microalgae and wafer-enhanced electrodeionization have been used in a wide variety of applications in industry. Introductory chapter discussed the concepts and motivation for research into microalgae, biofuel production, wafer-enhanced electrodeionization. Chapter 2 divulged greater detail on the use of microalgae, biofuel production, wafer-enhanced electrodeionization as well as the modeling using supervised learning, state-of-the-art-performance, and major researchers in each subject area. In Chapter 3, proof-of-concept experiments for the growth and productivity of Porphyridium cruentum in swine wastewater and in control media were studied. The results showed that swine wastewater has the potential to be used as an alternative growth medium for microalgae in biofuel production. In terms of biomass productivity and fatty acid methyl ester analysis of P. cruentum, no significant difference has been observed between cultivation in swine wastewater and cultivation in culture media.

Chapter 4 discussed the effects of the four different ion exchange resins on the performance of wafer-enhanced electrodeionization for the removal of sodium, magnesium, and calcium ions.

Implementation of ion exchange wafers made with strongly cationic resins resulted in higher selectivity values for divalent ions. In addition, the positive impact of powder form resins was verified. Through this research, wafer-enhanced electrodeionization can allow other powder form or smaller particle size resins to be considered for valuable divalent ion recovery.

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Machine Learning with Wafer-Enhanced Electrodeionization for Deeper

Understanding of System Parameters

Understanding the impact of ion exchange resins and how the system performance changes holds tremendous importance for the applications of wafer-enhanced electrodeionization. Data science and machine learning are leading technologies for extracting knowledge and understanding from data. These techniques are not only powerful for massive amount of data, but also, they are necessary to overcome the limitations of physical modeling such as strong assumptions, lack of important variables, and reproducibility. Currently, the data science and machine learning are yet to be implemented in wafer-enhanced electrodeionization, especially for its use in high concentration multiple ion solutions.

Chapter 5 discussed the implementation of supervised learning for the modeling of wafer- enhanced electrodeionization with high concentration multi-ion solution. With the use of multioutput regression and leave-one-out cross validation, the individual ion concentrations were predicted, and the observed effects of different resin wafers were confirmed. Utilization of principal component analysis for the multi-output data resulted in finding relevant features for multioutput regression which would not have otherwise achieved. With the use of single output linear regression, the current efficiency was predicted, and three feature elimination methods were used to understand the impacts of input features. Through this research, machine learning can allow other values for variables to be considered for the selective product removal by simulating complex wafer-enhanced electrodeionization experiments.

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Future Work

Following the completion of these studies, there exists a multitude of experiments and research investigations that can be pursued in order to further the science created and improved by the research described previously.

Figure 2: Future outlook of completed research progress. The top squares indicate the work presented in Chapters 3, 4, and 5. Each square below the top briefly describes subsequent research projects that can be developed as a result of the work accomplished.

The noteworthy results achieved in swine wastewater-grown P. cruentum promote the further investigation of this environmentally friendly method of microalgae cultivation with the objective of improving their harvesting on a large scale. There are several studies that have focused on the strategies to achieve this target, such as the use of non-poisonous additives, bio- magnetic flocculant, and the genetic modification of microalgae. These studies would provide a good starting point for further research into overcoming the limitations of the large-scale harvesting of P. cruentum and other microalgae species grown in swine wastewater.

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Research into the development of special design of wafer-enhanced electrodeionization for the electrochemical conversion of carbon dioxide to bicarbonate would also improve the carbon uptake of microalgae which then would lead to improved growth and biomass productivity. The development of membrane that limit water co-transport would result in higher purity products and carbon dioxide conversion. This research could occur through membrane modification of existing ion exchange membranes or by the synthesis of novel membranes designed for increased gas conversion.

A study into the development of wafers specifically designed for wafer-enhanced electrodeionization may lead to higher current efficiency and selectivity. Investigations in new wafer chemistries using a different range of particle size ion exchange resin may lead to an enhanced the divalent ion transfer, increased current efficiency, and a reduction of required energy for water splitting. Use of different wafers and input parameters would also provide a bigger and better dataset for the use in machine learning algorithms. Additional research into other machine learning algorithms such as decision tree, and support vector machine (SVM) could look into determining the best algorithms for the modeling of wafer-enhanced electrodeionization. Application of these algorithms may provide additional insights into the potential of wafer-enhanced electrodeionization for product removal. Finally, research into using machine learning algorithms for other electro-membrane separation systems (i.e. electrodialysis, reverse electrodialysis, wafer-enhanced reverse electrodialysis, etc.) can provide detailed comparative study on the effects of membrane type, ion exchange resin type, and other input parameters on these membrane separation systems.

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