OPTIMIZATION OF MATHEMATICAL MODELING OF MICROBIAL ELECTROLYSIS CELL FOR THE PRODUCTION OF HYDROGEN FROM SAGO WASTEWATER SUBSTRATE
DOI:
https://doi.org/10.11113/aej.v14.20419Keywords:
Biohydrogen, microbial electrolysis cell, biofilm growth, artificial neural network, mathematical model, optimizationAbstract
The nonlinear phenomenon of the profile of substrate concentration and hydrogen production rate over 16 retention days in a 4 L double chamber of a microbial electrolysis cell (MEC) for bioelectrochemical production of hydrogen from sago wastewater validates the mathematical modeling results based on simplified microbial biofilm growth. The stoichiometric reaction and kinetics affect the substrate concentration curve behaviour, but the effects also include the bioelectrochemical balance for hydrogen production rate. The artificial neural network (ANN) predicts the experimental hydrogen production rate according to the input of pH of the catholyte at controlled applied potential of 0.8 V and current density of 0.632 A‧m-2. The convex method assists the model in finding the optimal input values that lead to the minimum mean square error (MSE) between modelling and experimental data. Evaluation of the COD removed efficiency, coulombic efficiency, and energy efficiency determines the process limit of the model MEC. At an optimum applied potential of 0.485 V, anode surface area of 0.098 m2, anodic chamber volume of 4 L, and initial substrate concentration of 2,500.99 mg‧L-1, the MEC model reached maximum steady-state percentage at 81.99% of COD removed efficiency, 69.01% of Coulombic efficiency, and 7.47% of energy efficiency.
References
Aboelela, D., Soliman, Moustafa, A. and Ashour, I. 2020. A reduced model for microbial electrolysis cells. International Journal of Innovative Technology and Exploring Engineering, 9(4): 1724–1730. DOI: https://doi.org/10.35940/ijitee.D1613.029420
Varanasi, J. L., Veerubhotla, R., Pandit, S. and Das, D. 2019. Biohydrogen Production using Microbial Electrolysis Cell. In Microbial Electrochemical Technology. 843–869. Elsevier. DOI: https://doi.org/10.1016/B978-0-444-64052-9.00035-2
Hernández-García, K. M., Cercado, B., Rodríguez, F. A., Rivera, F. F. and Rivero, E. P. 2020. Modeling 3D current and potential distribution in a microbial electrolysis cell with augmented anode surface and non-ideal flow pattern. Biochemical Engineering Journal, 162: 107714. DOI: https://doi.org/10.1016/j.bej.2020.107714
Hernández-García, K. M., Cercado, B., Rivero, E. P. and Rivera, F. F. 2020. Theoretical and experimental evaluation of the potential-current distribution and the recirculation flow rate effect in the performance of a porous electrode microbial electrolysis cell (MEC). Fuel, 279: 118463. DOI: https://doi.org/10.1016/j.fuel.2020.118463
Xing, D., Yang, Y., Li, Z., Cui, H., Ma, D., Cai, X. and Gu, J. 2020. Hydrogen Production from Waste Stream with Microbial Electrolysis Cells. In Bioelectrosynthesis. 39–70. Wiley. DOI: https://doi.org/10.1002/9783527343829.ch2
Flores-Estrella, R. A., de Jesús Garza-Rubalcava, U., Haarstrick, A. and Alcaraz-González, V. 2019. A dynamic biofilm model for a microbial electrolysis cell. Processes, 7(4): 183. DOI: https://doi.org/10.3390/pr7040183
Hua, T., Li, S., Li, F., Zhou, Q. and Ondon, B. S. 2019. Microbial electrolysis cell as an emerging versatile technology: a review on its potential application, advance and challenge. Journal of Chemical Technology and Biotechnology, 94(6): 1697–1711. DOI: https://doi.org/10.1002/jctb.5898
Deaver, J. A., Kerr, C. A. and Popat, S. C. 2022. Primary sludge-based blackwater favors electrical current over methane production in microbial electrochemical cells. Journal of Water Process Engineering 47: 102848. DOI: https://doi.org/10.1016/j.jwpe.2022.102848
Sharma, M., Salama, E.-S., Thakur, N., Alghamdi, H., Jeon, B.-H. and Li, X. 2023. Advances in the biomass valorization in bioelectrochemical systems: A sustainable approach for microbial-aided electricity and hydrogen production. Chemical Engineering Journal. 465: 142546. DOI: https://doi.org/10.1016/j.cej.2023.142546
Kurniawan, S., Abdullah, S., Imron, M., Said, N., Ismail, N., Hasan, H., Othman, A. and Purwanti, I. 2020. Challenges and opportunities of biocoagulant/bioflocculant application for drinking water and wastewater treatment and its potential for sludge recovery. International Journal of Environmental Research and Public Health, 17(24): 9312. DOI: https://doi.org/10.3390/ijerph17249312
Alazaiza, M., Albahnasawi, A., Ali, G., Bashir, M., Nassani, D., Al Maskari, T., Amr, S. and Abujazar, M. 2022. Application of natural coagulants for pharmaceutical removal from water and wastewater: A review. Water, 14(2): 140. DOI: https://doi.org/10.3390/w14020140
Anusha, P., Ragavendran, C., Kamaraj, C., Sangeetha, K., Thesai, A. S., Natarajan, D. and Malafaia, G. 2023. Eco-friendly bioremediation of pollutants from contaminated sewage wastewater using special reference bacterial strain of Bacillus cereus SDN1 and their genotoxicological assessment in Allium cepa. Science of The Total Environment, 863: 160935. DOI: https://doi.org/10.1016/j.scitotenv.2022.160935
Periyasamy, P. 2021. Estimation of economic loss of agricultural production and livestock population in Tamil Nadu due to sago industrial pollution: A case study. Grassroots Journal of Natural Resources, 4(2): 165–178. DOI: https://doi.org/10.33002/nr2581.6853.040212
Alcaraz–Gonzalez, V., Rodriguez–Valenzuela, G., Gomez–Martinez, J. J., Dotto, G. L. and Flores–Estrella, R. A. 2021. Hydrogen production automatic control in continuous microbial electrolysis cells reactors used in wastewater treatment. Journal of Environmental Management, 281: 111869. DOI: https://doi.org/10.1016/j.jenvman.2020.111869
Guo, Z. and Yang, C. 2020. Microbial Metabolism Kinetics and Interactions in Bioelectrosynthesis System. In Bioelectrosynthesis, pp. 363–394. Wiley. DOI: https://doi.org/10.1002/9783527343829.ch15
Pinto, R. P., Srinivasan, B., Escapa, A. and Tartakovsky, B. 2011. Multi-population model of a microbial electrolysis cell. Environmental Science and Technology, 45(11): 5039–5046. DOI: https://doi.org/10.1021/es104268g
Thirugnanasambandham, K. and Shine, K. 2016. Hydrogen gas production from sago industry wastewater using electrochemical reactor: Simulation and validation. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 38(15): 2258–2264. DOI: https://doi.org/10.1080/15567036.2016.1174755
Flores-Estrella, R. A., Rodríguez-Valenzuela, G., Ramírez-Landeros, J. R., Alcaraz-González, V. and González-Álvarez, V. 2020. A simple microbial electrochemical cell model and dynamic analysis towards control design. Chemical Engineering Communications, 207(4): 493–505. DOI: https://doi.org/10.1080/00986445.2019.1605360
Dudley, H. J., Lu, L., Ren, Z. J. and Bortz, D. M. 2019. Sensitivity and bifurcation analysis of a differential-algebraic equation model for a microbial electrolysis cell. SIAM Journal on Applied Dynamical Systems, 18(2): 709–728. DOI: https://doi.org/10.1137/18M1172223
Kyazze, G., Popov, A., Dinsdale, R., Esteves, S., Hawkes, F., Premier, G. and Guwy, A. 2010. Influence of catholyte pH and temperature on hydrogen production from acetate using a two chamber concentric tubular microbial electrolysis cell. International Journal of Hydrogen Energy, 35(15): 7716–7722. DOI: https://doi.org/10.1016/j.ijhydene.2010.05.036
Hosseinzadeh, A., Zhou, J. L., Altaee, A., Baziar, M. and Li, D. 2020. Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system. Bioresource Technology, 316: 123967. DOI: https://doi.org/10.1016/j.biortech.2020.123967
Wang, Y., Yang, G., Sage, V., Xu, J., Sun, G., He, J. and Sun, Y. 2021. Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach. Environmental Progress and Sustainable Energy, 40(1): e13485. DOI: https://doi.org/10.1002/ep.13485
Cheng, D., Ngo, H. H., Guo, W., Chang, S. W., Nguyen, D. D., Zhang, S., Deng, S., An, D. and Hoang, N. B. 2022. Impact factors and novel strategies for improving biohydrogen production in microbial electrolysis cells. Bioresource Technology, 346: 126588. DOI: https://doi.org/10.1016/j.biortech.2021.126588
Muddasar, M., Liaquat, R., Aslam, A., Ur Rahman, M. Z., Abdullah, A., Khoja, A. H., Latif, K. and Bahadar, A. 2022. Performance efficiency comparison of microbial electrolysis cells for sustainable production of biohydrogen—A comprehensive review. International Journal of Energy Research, 46(5): 5625–5645. DOI: https://doi.org/10.1002/er.7606
Rani, G., Banu, J. R., Kumar, G. and Yogalakshmi, K. N. 2022. Statistical optimization of operating parameters of microbial electrolysis cell treating dairy industry wastewater using quadratic model to enhance energy generation. International Journal of Hydrogen Energy, 47(88): 37401-37414. DOI: https://doi.org/10.1016/j.ijhydene.2022.03.120
Yahya, A. M., Hussain, M. A. and Abdul Wahab, A. K. 2015. Modeling, optimization, and control of microbial electrolysis cells in a fed-batch reactor for production of renewable biohydrogen gas. International Journal of Energy Research, 39(4): 557–572. DOI: https://doi.org/10.1002/er.3273
Dudley, H. J., Ren, Z. J. and Bortz, D. M. 2019. Competitive exclusion in a DAE model for microbial electrolysis cells. Mathematical Biosciences and Engineering, 17(5): 6217–6239. DOI: https://doi.org/10.48550/arXiv.1906.02086
Lu, L., Vakki, W., Aguiar, J. A., Xiao, C., Hurst, K., Fairchild, M., Chen, X., Yang, F., Gu, J. and Ren, Z. J. 2019. Unbiased solar H2 production with current density up to 23 mA cm-2 by Swiss-cheese black Si coupled with wastewater bioanode. Energy and Environmental Science, 12(3): 1088–1099. DOI: https://doi.org/10.1039/c8ee03673j