Exploring the application of artificial intelligence for bioelectrochemical systems: A review of recent research

Abstract

Bioelectrochemical systems (BES) offer promising solutions for sustainable energy production and wastewater treatment. However, their complex biological and electrochemical dynamics pose significant challenges for traditional modeling approaches. This review explores the recent advancements in applying artificial intelligence (AI) techniques to enhance the performance and scalability of BES technologies. We detailed the roles of machine learning (ML) algorithms, such as artificial neural networks (ANNs), support vector regression (SVR), and random forest regression (RFR), in predicting critical BES performance metrics. Additionally, we discussed metaheuristic optimization techniques that have improved system design and operational parameters, yielding significant gains in energy recovery and stability. The integration of real-time monitoring and adaptive control systems, powered by AI, is highlighted for its potential to dynamically adjust BES operations in response to fluctuating environmental conditions. Despite these advancements, challenges remain, particularly in data standardization and modeling biological complexity within BES. We outline current limitations and future directions, emphasizing the need for robust datasets, standardized methodologies, and advanced AI frameworks to further unlock the potential of AI-driven BES systems in achieving sustainable bioenergy solutions.

Keywords

Bioelectrochemical systems / Artificial intelligence / Modeling / Sustainable energy / Wastewater treatment

CRediT authorship contribution statement

Miguel Esteban Pardo Gómez: Writing - review & editing, Writing - original draft, Visualization, Methodology, Investigation. Evan Park: Writing - review & editing. Ying Zheng: Writing - review & editing. Amarjeet Bassi: Writing - review & editing. Tianlong Liu: Writing - review & editing, Writing - original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by Western University (WSS NSERC Seed Grant R7502A02) and Natural Sciences and Engineering Research Council of Canada (Discovery Grant: RGPIN-2025-06368).

References

[1] Abdollahfard, Y., Sedighi, M., Ghasemi, M., 2023. A new approach for improving microbial fuel cell performance using artificial intelligence. Sustainability 15, 1312. https://doi.org/10.3390/su15021312.

[2] Abimannan, S., El-Alfy, E.S.M., Chang, Y.S., Hussain, S., Shukla, S., Satheesh, D., 2023. Ensemble multifeatured deep learning models and applications: a Survey. IEEE Access 11, 107194-107217. https://doi.org/10.1109/ACCESS.2023.3320042.

[3] Alibrahim, H., Ludwig, S.A., 2021. Hyperparameter optimization: comparing genetic algorithm against grid search and bayesian optimization. In: 2021 IEEE Congr. Evol. Comput., CEC, pp. 1551-1559. https://doi.org/10.1109/CEC45853.2021.9504761.

[4] Samuel, A.L., 1959. Some studies in machine learning using the game of checkers. IBM J Res Dev 3, 210-229. https://doi.org/10.1147/rd.33.0210.

[5] Asrul, M.A.M., Atan, M.F., Yun, H.A.H., Wahab, N.A., Hung, H.H., Lai, J.C.H., Tan, I.A. W., 2024. Optimization of mathematical modeling OF microbial electrolysis cell for the production of hydrogen from sago wastewater substrate. ASEAN Engineering Journal 14, 7-17. https://doi.org/10.11113/aej.V14.20419.

[6] Badrick, T., 2021. Biological variation: understanding why it is so important? Pract. Lab. Med. 23. https://doi.org/10.1016/J.PLABM.2020.E00199.

[7] Barabási, A.-L., 2016. Network Science. Cambridge University Press, Cambridge.

[8] Bazina, N., Ahmed, T.G., Almdaaf, M., Jibia, S., Sarker, M., 2023. Power generation from wastewater using microbial fuel cells: a review. J. Biotechnol 374, 17-30. https://doi.org/10.1016/j.jbiotec.2023.07.006.

[9] Blum, C., Roli, A., 2003. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35, 268-308. https://doi.org/10.1145/937503.937505.

[10] Breiman, L., 2001. Random Forests. Mach Learn 45, 5-32. https://doi.org/10.1023/A:1010933404324.

[11] Cai, W., Lesnik, K.L., Wade, M.J., Heidrich, E.S., Wang, Y., Liu, H., 2019. Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells. Biosens. Bioelectron. 133, 64-71. https://doi.org/10.1016/j.bios.2019.03.021.

[12] Chalak Qazani, M.R., Ghasemi, M., Asadi, H., 2024. Optimizing microbial fuel cells with multiple-objectives PSO and type-2 fuzzy neural networks. Fuel 372, 132090. https://doi.org/10.1016/j.fuel.2024.132090.

[13] Chang, T., Liu, T., Ma, X., Wu, Q., Wang, X., Cheng, J., Wei, W., Zhang, F., Liu, H., 2024. Fault detection in industrial wastewater treatment processes using manifold learning and support vector data description. Ind. Eng. Chem. Res. 63, 15562-15574. https://doi.org/10.1021/acs.iecr.4c00424.

[14] Chou, J.S., Cheng, T.C., Liu, C.Y., Guan, C.Y., Yu, C.P., 2022. Metaheuristics-optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells. Int. J. Energy Res. 46, 21001-21027. https://doi.org/10.1002/ER.8538;WGROUP:STRING:PUBLICATION.

[15] Clayton, E.A., Pujol, T.A., McDonald, J.F., Qiu, P., 2020. Leveraging TCGA gene expression data to build predictive models for cancer drug response. BMC Bioinf. 21, 1-11. https://doi.org/10.1186/S12859-020-03690-4/FIGURES/4.

[16] Cortes, C., Vapnik, V., Saitta, L., 1995. Support-vector networks. In: Machine Leaming, 20. Kluwer Academic Publishers, pp.273-297. https://doi.org/10.1007/BF00994018.

[17] de Lima, Cardozo, M.H.A., Rodrigues, I.C.B., Fabiano, D.P., Nucci, E.R., 2024. Microbial fuel cells performance evaluation through the application of fuzzy logic. Rev. Gest-ao Soc. e Ambiental 18. https://doi.org/10.24857/RGSA.V18N1-119.

[18] de Ramón-Fernández, A., Salar-García, M.J., Ruiz Fernández, D., Greenman, J., Ieropoulos, I.A., 2020. Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells. Energy 213. https://doi.org/10.1016/j.energy.2020.118806.

[19] Demir, M.H., Eren, B., 2022. Output voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controller. Int. J. Hydrogen Energy 47, 19837-19849. https://doi.org/10.1016/j.ijhydene.2022.03.113.

[20] Du, L., Yan, Y., Li, T., Liu, H., Li, N., Wang, X., 2022. Machine learning enables quantification of multiple toxicants with microbial electrochemical sensors. ACS ES and T Engineering 2, 92-100. https://doi.org/10.1021/acsestengg.1c00287.

[21] Elman, J.L., 1990. Finding Structure in Time. Cogn Sci 14, 179-211. https://doi.org/10.1207/S15516709COG1402_1.

[22] Emaminejad, S.A., Sparks, J., Cusick, R.D., 2023. Integrating bio-electrochemical sensors and machine learning to predict the efficacy of biological nutrient removal processes at water resource recovery facilities. Environ. Sci. Technol. 57, 18372-18381. https://doi.org/10.1021/acs.est.3c00352.

[23] Erable, B., Etcheverry, L., Bergel, A., 2011. From microbial fuel cell (MFC) to microbial electrochemical snorkel (MES): maximizing chemical oxygen demand (COD) removal from wastewater. Biofouling 27, 319-326. https://doi.org/10.1080/08927014.2011.564615.

[24] Eraslan, G., Avsec, Ž., Gagneur, J., Theis, F.J., 2019. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389-403. https://doi.org/10.1038/S41576-019-0122-6.

[25] Escapa, A., Mateos, R., Martínez, E.J., Blanes, J., 2016. Microbial electrolysis cells: an emerging technology for wastewater treatment and energy recovery. From laboratory to pilot plant and beyond. Renew. Sustain. Energy Rev. 55, 942-956. https://doi.org/10.1016/J.RSER.2015.11.029.

[26] Esfandyari, M., Fanaei, M.A., Gheshlaghi, R., Mahdavi, M.A., 2016. Neural network and neuro-fuzzy modeling to investigate the power density and columbic efficiency of microbial fuel cell. J. Taiwan Inst. Chem. Eng. 58, 84-91. https://doi.org/10.1016/j.jtice.2015.06.005.

[27] Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S., 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (7639), 115-118. https://doi.org/10.1038/nature21056, 2017 542.

[28] Fang, F., Zang, G.L., Sun, M., Yu, H.Q., 2013. Optimizing multi-variables of microbial fuel cell for electricity generation with an integrated modeling and experimental approach. Appl. Energy 110, 98-103. https://doi.org/10.1016/j.apenergy.2013.04.017.

[29] Farahani, H., Ghasemi, M., Sedighi, M., Raut, N., 2024. Employing artificial intelligence for enhanced microbial fuel cell performance through wolf vitamin solution optimization. Sustainability 16. https://doi.org/10.3390/su16156468.

[30] Faramarzi, A., Heidarinejad, M., Stephens, B., Mirjalili, S., 2020. Equilibrium optimizer: a novel optimization algorithm. https://doi.org/10.1016/j.knosys.

[31] Feng, Y., Kayode, O., Harper, W.F., 2013. Using microbial fuel cell output metrics and nonlinear modeling techniques for smart biosensing. Sci. Total Environ. 449, 223-228. https://doi.org/10.1016/j.scitotenv.2013.01.004.

[32] Fu, L., 2025. A novel robust control for disturbed uncertain microbial fuel cell with noisy output. J. N. Mater. Electrochem. Syst. 28, 76-84. https://doi.org/10.14447/JNMES.V28I1.A08.

[33] Fu, L., Wang, J., Fu, X., Zhao, G., 2025. Finite-time Pade-based adaptive FNN controller implementation for microbial fuel cell with delay and multi-disturbance. Int. J. Hydrogen Energy 98, 1034-1043. https://doi.org/10.1016/J.IJHYDENE.2024.10.372.

[34] Gadkari, S., Gu, S., Sadhukhan, J., 2019a. Two-dimensional mathematical model of an air-cathode microbial fuel cell with graphite fiber brush anode. J. Power Sources 441, 227145. https://doi.org/10.1016/J.JPOWSOUR.2019.227145.

[35] Gadkari, S., Shemfe, M., Sadhukhan, J., 2019b. Microbial fuel cells: a fast converging dynamic model for assessing system performance based on bioanode kinetics. Int. J. Hydrogen Energy 44, 15377-15386. https://doi.org/10.1016/J.IJHYDENE.2019.04.065.

[36] Garg, A., Lam, J.S.L., 2017. Design of explicit models for estimating efficiency characteristics of microbial fuel cells. Energy 134, 136-156. https://doi.org/10.1016/j.energy.2017.05.180.

[37] Garg, A., Vijayaraghavan, V., Mahapatra, S.S., Tai, K., Wong, C.H., 2014. Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Syst. Appl. 41, 1389-1399. https://doi.org/10.1016/j.eswa.2013.08.038.

[38] Gautam, R., Nayak, J.K., Ress, N.V., Steinberger-Wilckens, R., Ghosh, U.K., 2023. Bio- hydrogen production through microbial electrolysis cell: structural components and influencing factors. Chem. Eng. J. 455, 140535. https://doi.org/10.1016/J.CEJ.2022.140535.

[39] Ghasemi, M., Nassef, A.M., Al-Dhaifallah, M., Rezk, H., 2021. Performance improvement of microbial fuel cell through artificial intelligence. Int. J. Energy Res. 45, 342-354. https://doi.org/10.1002/er.5484.

[40] Ghasemi, M., Qazani, M.R.C., Lennon, C.W., Sedighi, M., 2023. Analysis and prediction of microbial fuel cell behaviour using MLP and SVR. J. Taiwan Inst. Chem. Eng. 151, 105101. https://doi.org/10.1016/j.jtice.2023.105101.

[41] Ghasemi, M., Rezk, H., 2024. Performance improvement of microbial fuel cell using experimental investigation and fuzzy modelling. Energy 286, 129486. https://doi.org/10.1016/j.energy.2023.129486.

[42] Gürbüz, T., Günay, M.E., Tapan, N.A., 2024. Machine learning solutions for enhanced performance in plant-based microbial fuel cells. Int. J. Hydrogen Energy 78, 1060-1069. https://doi.org/10.1016/j.ijhydene.2024.06.417.

[43] Gurjar, R., Behera, M., 2023. Integrating operating conditions to develop a neural network for predicting organics removal and power density in an earthen microbial fuel cell treating leachate. Biofuels 14, 49-58. https://doi.org/10.1080/17597269.2022.2116769.

[44] Gustave, W., Yuan, Z.F., Sekar, R., Ren, Y.X., Liu, J.Y., Zhang, J., Chen, Z., 2019. Soil organic matter amount determines the behavior of iron and arsenic in paddy soil with microbial fuel cells. Chemosphere 237, 124459. https://doi.org/10.1016/J.CHEMOSPHERE.2019.124459.

[45] Haykin, S.S., 2009. Neural networks and learning machines, third ed. Pearson Education, Upper Saddle River, NJ.

[46] Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H., 2019. Harris hawks optimization: algorithm and applications. Future Gener. Comput. Syst. 97, 849-872. https://doi.org/10.1016/j.future.2019.02.028.

[47] Herrmann, M., Probst, P., Hornung, R., Jurinovic, V., Boulesteix, A.-L., 2020. Large-scale benchmark study of survival prediction methods using multi-omics data. Briefings Bioinf. 22, bbaa167. https://doi.org/10.1093/bib/bbaa167.

[48] Hess-Dunlop, A., Kakani, H., Josephson, C., 2024a. Towards Deep Learning for Predicting Microbial Fuel Cell Energy Output. In: Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS ’24. Association for Computing Machinery, New York, NY, USA, pp. 330-338. https://doi.org/10.1145/3674829.3675358.

[49] Hess-Dunlop, A., Kakani, H., Taylor, S., Louie, D., Eshraghian, J., Josephson, C., 2024b. Time-series forecasting of microbial fuel cell energy generation using deep learning. Front. Comput. Sci. 6, 1447745. https://doi.org/10.3389/FCOMP.2024.1447745/BIBTEX.

[50] Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Comput. 9, 1735-1780. https://doi.org/10.1162/NECO.1997.9.8.1735.

[51] Hosseinzadeh, A., Zhou, J.L., Altaee, A., Baziar, M., 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. Bioresour. Technol. 316, 123967. https://doi.org/10.1016/j.biortech.2020.123967.

[52] Ibrahim, I., Salehmin, M.N.I., Balachandran, K., Hil Me, M.F., Loh, K.S., Abu Bakar, M.H., Jong, B.C., Lim, S.S., 2023. Role of microbial electrosynthesis system in CO₂ capture and conversion: a recent advancement toward cathode development. Front. Microbiol 14. https://doi.org/10.3389/fmicb.2023.1192187.

[53] Ismail, Z.Z., Al-wared, A.I., Jaeel, A.J., 2019. Recourse recovery of bioenergy from cellulosic material in a microbial fuel cell fed with giant reed-loaded wastewater. Biofuels 10, 737-745. https://doi.org/10.1080/17597269.2017.1409057.

[54] Jaeel, A.J., Al-Wared, A.I., Ismail, Z.Z., 2016. Prediction of sustainable electricity generation in microbial fuel cell by neural network: effect of anode angle with respect to flow direction. J. Electroanal. Chem. 767, 56-62. https://doi.org/10.1016/j.jelechem.2016.02.015.

[55] Jang, J.-S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23, 665-685. https://doi.org/10.1109/21.256541.

[56] Jin, M., Xue, H., Wang, Z., Kang, B., Ye, R., Zhou, K., Du, M., Zhang, Y., 2024. Prollm: Protein chain-of-thoughts Enhanced LLM for Protein-Protein Interaction Prediction.

[57] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S.A.A., Ballard, A.J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A.W., Kavukcuoglu, K., Kohli, P., Hassabis, D., 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596 (7873), 583-589. https://doi.org/10.1038/s41586-021-03819-2, 2021 596.

[58] Kamilaris, A., Prenafeta-Boldú F.X., 2018. A review of the use of convolutional neural networks in agriculture. J. Agric. Sci. 156, 312-322. https://doi.org/10.1017/S0021859618000436.

[59] Karimi Alavijeh, M., Mardanpour, M.M., Yaghmaei, S., 2015. A generalized model for complex wastewater treatment with simultaneous bioenergy production using the microbial electrochemical cell. Electrochim. Acta 167, 84-96. https://doi.org/10.1016/J.ELECTACTA.2015.03.133.

[60] Kennedy, J., Eberhart, R.C., gov, bls, 1995. Particle swarm optimization, in:Proceedings of the IEEE International Conference on Neural Networks 1942-1948.

[61] Khew Mun Hong, G., Hussain, M.A., Abdul, Wahab, A.K., 2021. Fuzzy logic controller implementation on a microbial electrolysis cell for biohydrogen production and storage. Chin. J. Chem. Eng. 40, 149-159. https://doi.org/10.1016/j.cjche.2021.03.057.

[62] Khoshgoftar Manesh, M.H., Davadgaran, S., Mousavi Rabeti, S.A., 2024. Experimental study of biological wastewater recovery using microbial fuel cell and application of reliability and machine learning to predict the system behavior. Energy Convers. Manag. 314. https://doi.org/10.1016/j.enconman.2024.118658.

[63] Koch, C., Korth, B., Harnisch, F., 2018. Microbial ecology-based engineering of microbial electrochemical Technologies. Microb. Biotechnol. 11, 22-38. https://doi.org/10.1111/1751-7915.12802.

[64] Korth, B., Harnisch, F., 2019. Modeling microbial electrosynthesis,in: HoltmannD. (Eds.). Bioelectrosynthesis. Springer International Publishing, Cham, pp. 273-325. https://doi.org/10.1007/10_2017_35.

[65] Kuutti, S., Bowden, R., Jin, Y., Barber, P., Fallah, S., 2021. A survey of deep learning applications to autonomous vehicle control. IEEE Trans. Intell. Transport. Syst. 22, 712-733. https://doi.org/10.1109/TITS.2019.2962338.

[66] Lesnik, K.L., Cai, W., Liu, H., 2020. Microbial community predicts functional stability of microbial fuel cells. Environ. Sci. Technol. 54, 427-436. https://doi.org/10.1021/acs.est.9b03667.

[67] Lesnik, K.L., Liu, H., 2017. Predicting microbial fuel cell biofilm communities and bioreactor performance using artificial neural networks. Environ. Sci. Technol. 51, 10881-10892. https://doi.org/10.1021/acs.est.7b01413.

[68] Li, C., Guo, D., Dang, Y., Sun, D., Li, P., 2023. Application of artificial intelligence-based methods in bioelectrochemical systems: recent progress and future perspectives. J. Environ. Manag. 344, 118502. https://doi.org/10.1016/J.JENVMAN.2023.118502.

[69] Li, C., Li, H., Li, P., Dang, Y., Sun, D., Guo, D., 2024. Enhancing predictions of acetate and ethanol production from microbial electrosynthesis using optimized machine learning models. ACS Sustain. Chem. Eng. 12, 4264-4275. https://doi.org/10.1021/acssuschemeng.3c08356.

[70] Libbrecht, M.W., Noble, W.S., 2015. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321-332. https://doi.org/10.1038/NRG3920.

[71] Lim, C.E., Chew, C.L., Pan, G.T., Chong, S., Arumugasamy, S.K., Lim, J.W., Al-Kahtani, A. A., Ng, H.S., Abdurrahman, M., 2024. Predicting microbial fuel cell biofilm communities and power generation from wastewaters with artificial neural network. Int. J. Hydrogen Energy 52, 1052-1064. https://doi.org/10.1016/j.ijhydene.2023.08.290.

[72] Liu, H., Lv, Y., Xu, S., Chen, Z., Lichtfouse, E., 2020. Configuration and rapid start-up of a novel combined microbial electrolytic process treating fecal sewage. Sci. Total Environ. 705, 135986. https://doi.org/10.1016/J.SCITOTENV.2019.135986.

[73] Liu, T., Nadaraja, A.V., Shi, J., Roberts, D.J., 2021. Stable performance of microbial fuel cell technology treating winery wastewater irrespective of seasonal variations. J. Environ. Eng. 147, 04021043. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001921.

[74] Logan, B.E., 2008. Microbial fuel cells. Wiley-Blackwell. https://doi.org/10.1002/9780470258590.

[75] Logan, B.E., Hamelers, B., Rozendal, R., Schröder, U., Keller, J., Freguia, S., Aelterman, P., Verstraete, W., Rabaey, K., 2006. Microbial fuel cells: methodology and technology. Environ. Sci. Technol. 40, 5181-5192. https://doi.org/10.1021/es0605016.

[76] Lu, Y., Wang, J., Liu, T., Yoo, C.K., Liu, H., 2024. Integration of dynamic slow feature analysis and deep neural networks for subway indoor PM2.5 prediction. IEEE Trans. Instrum. Meas. https://doi.org/10.1109/TIM.2024.3476591.

[77] Luo, E., Cheng, G., Liu, Y., Zhao, Y., Wang, X., Dong, C., 2025. Optimization of kitchen wastewater-based microbial fuel cells by machine learning. J. Power Sources 632, 236379. https://doi.org/10.1016/J.JPOWSOUR.2025.236379.

[78] Ma, F., Wang, C., Zhu, B., Ge, J., Zhang, F., Sun, J., 2025. An improved chaos particle swarm optimization approach in FOPID controller for microbial fuel cells. IEEE Trans Industr Inform 21, 3890-3900. https://doi.org/10.1109/TII.2025.3529922.

[79] Madondo, N.I., Rathilal, S., Bakare, B.F., Tetteh, E.K., 2023. Application of bioelectrochemical systems and anaerobic additives in wastewater treatment: a conceptual review. Int. J. Mol. Sci. 24, 4753. https://doi.org/10.3390/IJMS24054753.

[80] Malang, J., Yeo, W.S., Chua, Z.Y., Nandong, J., Saptoro, A., 2023. A comparison study between different kernel functions in the least square support vector regression model for penicillin fermentation process. MATEC Web of Conferences 377, 01025. https://doi.org/10.1051/MATECCONF/202337701025.

[81] Mansoorian, H.J., Mahvi, A., Nabizadeh, R., Alimohammadi, M., Nazmara, S., Yaghmaeian, K., 2020. Evaluating the performance of coupled MFC-MEC with graphite felt/MWCNTs polyscale electrode in landfill leachate treatment, and bioelectricity and biogas production. J. Environ. Health Sci. Eng. 18, 1067-1082. https://doi.org/10.1007/S40201-020-00528-2.

[82] Mcculloch, W.S., Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology 5, 127-147.

[83] Medvedev, I., Kornaukhova, M., Galazis, C., Lóránt, B., Tardy, G.M., Losev, A., Goryanin, I., 2023. Using AI and BES/MFC to decrease the prediction time of BOD5 measurement. Environ. Monit. Assess. 195, 1018. https://doi.org/10.1007/s10661-023-11576-0.

[84] Mehta, J., Chatterjee, S., Shah, M., 2024. Leveraging microbial synergy: predicting the optimal consortium to enhance the performance of microbial fuel cell using Subspace-kNN. J. Environ. Manag. 369, 122252. https://doi.org/10.1016/j.jenvman.2024.122252.

[85] Mirjalili, S., Mirjalili, S.M., Lewis, A., 2014. Grey wolf optimizer. Adv. Eng. Software 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.

[86] Mohammad, S.J., Ishaq, A., 2024. Electricity generation from renewable source- A leachate based microbial fuel cell machine learning approach. https://doi.org/10.21203/RS.3.RS-5119060/V1.

[87] Mowbray, M., Savage, T., Wu, C., Song, Z., Cho, B.A., Del Rio-Chanona, E.A., Zhang, D., 2021. Machine learning for biochemical engineering: a review. Biochem. Eng. J. 172, 108054. https://doi.org/10.1016/J.BEJ.2021.108054.

[88] Munoz-Cupa, C., Bassi, A., 2024. Investigation of heavy metal removal from salty wastewater and voltage production using Shewanella oneidensis MR-1 nanowires in a dual-chamber microbial fuel cell. Environ Prog Sustain Energy 43, e14237. htt ps:// doi.org/10.1002/EP.14237.

[89] Munoz-Cupa, C., Hu, Y., Xu, C., Bassi, A., 2021. An overview of microbial fuel cell usage in wastewater treatment, resource recovery and energy production. Sci. Total Environ. 754, 142429. https://doi.org/10.1016/J.SCITOTENV.2020.142429.

[90] Murugesu, G.V., Khalid, S.N., Shareef, H., Khan, S.S., 2025. Correlation between input and output parameters of microbial fuel cell. Indonesian Journal of Electrical Engineering and Computer Science 37, 1452-1463. https://doi.org/10.11591/IJEECS.V37.I3.PP1452-1463.

[91] Naik, S., Eswari, J.S., 2021. Experimental and validation with neural network time series model of microbial fuel cell bio-sensor for phenol detection. J. Environ. Manag. 290. https://doi.org/10.1016/j.jenvman.2021.112594.

[92] Nasrabadi, A.M., Moghimi, M., 2022. Energy analysis and optimization of a biosensor- based microfluidic microbial fuel cell using both genetic algorithm and neural network PSO. Int. J. Hydrogen Energy 47, 4854-4867. https://doi.org/10.1016/J.IJHYDENE.2021.11.125.

[93] Nguyen, V.T., Ta, Q.T.H., Nguyen, P.K.T., 2022. Artificial intelligence-based modeling and optimization of microbial electrolysis cell-assisted anaerobic digestion fed with alkaline-pretreated waste-activated sludge. Biochem. Eng. J. 187, 108670. https://doi.org/10.1016/j.bej.2022.108670.

[94] Obasi, L.A., Nevo, C.O., 2024. Evaluating the predictive potential of RSM and ANN techniques in treatment of greywater-syrup mixture using Ekowe clay-PEM microbial fuel cell. Academia Green Energy 1. https://doi.org/10.20935/acadenergy7273.

[95] Odunlami, O.A., Vershima, D.A., Tagbo, C.V., Ogunlade, S., Nkongho, S., 2023. Microbial desalination cell technique - a review. S. Afr. J. Chem. Eng. 46, 312-329. https://doi.org/10.1016/J.SAJCE.2023.07.011.

[96] Oyedeji, M.O., Alharbi, A., Aldhaifallah, M., Rezk, H., 2023. Optimal data-driven modelling of a microbial fuel cell. Energies (Basel) 16. https://doi.org/10.3390/en16124740.

[97] Panja, T., Meharia, P., 2024. Enhancing microbial fuel cell performance prediction and IoT integration using machine learning and renewable energy. Journal of Strategic Innovation and Sustainability 19. https://doi.org/10.33423/jsis.v19i1.6787.

[98] Pant, D., Singh, A., Van Bogaert, G., Irving Olsen, S., Singh Nigam, P., Diels, L., Vanbroekhoven, K., 2012. Bioelectrochemical systems (BES) for sustainable energy production and product recovery from organic wastes and industrial wastewaters. RSC Adv. 2, 1248-1263. https://doi.org/10.1039/c1ra00839k.

[99] Patel, J., Shah, S., Thakkar, P., Kotecha, K., 2015. Predicting stock and stock price index movement using trend Deterministic data preparation and machine learning techniques. Expert Syst. Appl. 42, 259-268. https://doi.org/10.1016/J.ESWA.2014.07.040.

[100] Phan, T.P., Phan, Q.H.H., Nguyen, P.K.T., 2024. Dynamic-based artificial intelligence model for simulation and optimization of the single chamber anode brush microbial electrolysis cell. Fuel 375, 132629. https://doi.org/10.1016/j.fuel.2024.132629.

[101] Quashie, F.K., Fang, A., Wei, L., Kabutey, F.T., Xing, D., 2023. Prediction of biogas production from food waste in a continuous stirred microbial electrolysis cell (CSMEC) with backpropagation artificial neural network. Biomass Convers Biorefin 13, 287-298. https://doi.org/10.1007/S13399-020-01179-X/FIGURES/7.

[102] Rabaey, K., Rozendal, R.A., 2010. Microbial electrosynthesis — revisiting the electrical route for microbial production. Nat. Rev. Microbiol. 8 (10), 706-716. https://doi.org/10.1038/nrmicro2422, 2010 8.

[103] Rezk, H., Olabi, A.G., Abdelkareem, M.A., Sayed, E.T., 2023. Artificial intelligence as a novel tool for enhancing the performance of urine fed microbial fuel cell as an emerging approach for simultaneous power generation and wastewater treatment. J. Taiwan Inst. Chem. Eng. 148, 104726. https://doi.org/10.1016/j.jtice.2023.104726.

[104] Rezk, H., Sayed, E.T., 2024. Increasing bio-hydrogen production from microbial electrolysis cell using artificial gorilla troops optimization. Front. Energy Res. 12. https://doi.org/10.3389/fenrg.2024.1419785.

[105] Rudolph, M., Kurz, S., Rakitsch, B., 2023. Hybrid Modeling Design Patterns.

[106] Russel, S., Norvig, P., 2009. Artificial Intelligence A Modern Approach 3rd ed. Prentice Hall, Upper Saddle River.

[107] Sayed, E.T., Rezk, H., Abdelkareem, M.A., Olabi, A.G., 2024. Artificial neural network based modelling and optimization of microalgae microbial fuel cell. Int. J. Hydrogen Energy 52, 1015-1025. https://doi.org/10.1016/j.ijhydene.2022.12.081.

[108] Sewsynker, Y., Gueguim Kana, E.B., Lateef, A., 2015. Modelling of biohydrogen generation in microbial electrolysis cells (MECs) using a committee of artificial neural networks (ANNs). Biotechnol. Biotechnol. Equip. 29, 1208-1215. https://doi.org/10.1080/13102818.2015.1062732.

[109] Shabani, F., Philamore, H., Matsuno, F., 2021. An energy-autonomous chemical oxygen demand sensor using a microbial fuel cell and embedded machine learning. IEEE Access 9, 108689-108701. https://doi.org/10.1109/ACCESS.2021.3101496.

[110] Shemfe, M., Gadkari, S., Yu, E., Rasul, S., Scott, K., Head, I.M., Gu, S., Sadhukhan, J., 2018. Life cycle, techno-economic and dynamic simulation assessment of bioelectrochemical systems: a case of formic acid synthesis. Bioresour. Technol. 255, 39-49. https://doi.org/10.1016/J.BIORTECH.2018.01.071.

[111] Singh, N.K., Mathuriya, A.S., Mehrotra, S., Pandit, S., Singh, A., Jadhav, D., 2024. Advances in bioelectrochemical systems for bio-products recovery. Environ. Technol. 45, 3853-3876. https://doi.org/10.1080/09593330.2023.2234676.

[112] Singh, S., Srinivasan, K., Chen, B.Y., Singh, H., Goyal, A., Garg, A., Cui, X., 2019. A novel method for determination of a time period for stabilization of power generation of microbial fuel cell with effect of microorganisms. Int. J. Energy Res. 43, 5834-5840. https://doi.org/10.1002/er.4685.

[113] Sun, J., Zhang, X., Guan, J., He, Z., 2024. Volatile fatty acid production through arresting methanogenesis by electro-synthesized hydrogen peroxide in anaerobic digestion and subsequent recovery by electrodialysis. ACS ES and T Engineering 4, 2964-2973. https://doi.org/10.1021/acsestengg.4c00384.

[114] Tardast, A., Rahimnejad, M., Najafpour, G., Ghoreyshi, A., Premier, G.C., Bakeri, G., Oh, S.E., 2014. Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell. Fuel 117, 697-703. https://doi.org/10.1016/j.fuel.2013.09.047.

[115] Thatikayala, D., Pant, D., Min, B., 2021. MnO2/reduced graphene oxide nanohybrids as a cathode catalyst for the microbial reduction of CO₂ to acetate and isobutyric acid. Sustain. Energy Technol. Assessments 45, 101114. https://doi.org/10.1016/J.SETA.2021.101114.

[116] Tsompanas, M.A., You, J., Philamore, H., Rossiter, J., Ieropoulos, I., 2021. Neural networks predicting microbial fuel cells output for soft robotics applications. Front Robot AI 8. https://doi.org/10.3389/frobt.2021.633414.

[117] Tsompanas, M.A., You, J., Wallis, L., Greenman, J., Ieropoulos, I., 2019. Artificial neural network simulating microbial fuel cells with different membrane materials and electrode configurations. J. Power Sources 436, 226832. https://doi.org/10.1016/j.jpowsour.2019.226832.

[118] Verma, M., Singh, V., Mishra, V., 2023. Bioelectricity generation by using cellulosic waste and spent engine oil in a concentric photobioreactor-microbial fuel cell. J. Environ. Chem. Eng. 11. https://doi.org/10.1016/j.jece.2023.110566.

[119] Verma, M., Singh, V., Mishra, V., 2024. Optimization of banana peel waste based microbial fuel cells by machine learning. Biomass Convers Biorefin. https://doi.org/10.1007/s13399-023-04344-0.

[120] Wang, C., Zhu, B., Ma, F., Sun, J., 2024. Design of a PID controller for microbial fuel cells using improved particle swarm optimization. Electronics (Switzerland) 13. https://doi.org/10.3390/electronics13173381.

[121] Wang, H.Y., Bernarda, A., Huang, C.Y., Lee, D.J., Chang, J.S., 2011. Micro-sized microbial fuel cell: a mini-review. Bioresour. Technol. 102, 235-243. https://doi.org/10.1016/J.BIORTECH.2010.07.007.

[122] Wang, J., Wang, Q., 2019. Intelligent explicit model predictive control based on machine learning for microbial desalination cells. Proc. IME J. Syst. Control Eng. 233, 751-763. https://doi.org/10.1177/0959651818816845.

[123] Wang, J., Wang, Q., Zhou, J., Wang, X., Cheng, L., 2018. Operation space design of microbial fuel cells combined anaerobic-anoxic-oxic process based on support vector regression inverse model. Eng. Appl. Artif. Intell. 72, 340-349. https://doi.org/10.1016/j.engappai.2018.04.005.

[124] Wang, L., Long, F., Liang, D., Xiao, X., Liu, H., 2021. Hydrogen production from lignocellulosic hydrolysate in an up-scaled microbial electrolysis cell with stacked bio-electrodes. Bioresour. Technol. 320, 124314. https://doi.org/10.1016/j.biortech.2020.124314.

[125] Wei, L., Li, Z., Hong, T., Tang, Y., Ge, Y., Ji, D., Zhang, Q., Luo, L., Kong, J., Ding, X., 2024. Insight into electricity production performance from dried waste activated sludge in dual chamber microbial fuel cells: influencing factors, neural network modelling and microbial community analysis. J. Environ. Chem. Eng. 12, 113477. https://doi.org/10.1016/j.jece.2024.113477.

[126] Xiao, J., Liu, C., Ju, B., Xu, H., Sun, D., Dang, Y., 2021. Estimation of in-situ biogas upgrading in microbial electrolysis cells via direct electron transfer: two-stage machine learning modeling based on a NARX-BP hybrid neural network. Bioresour. Technol. 330, 124965. https://doi.org/10.1016/j.biortech.2021.124965.

[127] Yan, B., Nam, Y., Li, L., Deek, R.A., Li, H., Ma, S., 2025. Recent advances in deep learning and language models for studying the microbiome. Front Genet 15. https://doi.org/10.3389/fgene.2024.1494474.

[128] Yewale, A., Methekar, R., Agrawal, S., 2020. Multiple model-based control of multi variable continuous microbial fuel cell (CMFC) using machine learning approaches. Comput. Chem. Eng. 140, 106884. https://doi.org/10.1016/j.compchemeng.2020.106884.

[129] Yoon, J., Cheong, D.Y., Baek, G., 2024. Predicting current and hydrogen productions from microbial electrolysis cells using random forest model. Appl. Energy 371, 123641. https://doi.org/10.1016/j.apenergy.2024.123641.

[130] Yu, K., Yi, S., Li, B., Guo, F., Peng, X., Wang, Z., Wu, Y., Alvarez-Cohen, L., Zhang, T., 2019. An integrated meta-omics approach reveals substrates involved in synergistic interactions in a bisphenol A (BPA)-degrading microbial community. Microbiome 7, 16. https://doi.org/10.1186/S40168-019-0634-5.

[131] Zadeh, L.A., 1965. Fuzzy sets. Inf. Control 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.

[132] Zakir Hossain, S.M., Sultana, N., Haji, S., Talal Mufeez, S., Esam Janahi, S., Adel Ahmed, N., 2023. Modeling of microbial fuel cell power generation using machine learning-based super learner algorithms. Fuel 349, 128646. https://doi.org/10.1016/j.fuel.2023.128646.

[133] Zhang, K., Wang, X., Liu, T., Wei, W., Zhang, F., Huang, M., Liu, H., 2024a. Enhancing water quality prediction with advanced machine learning techniques: an extreme gradient boosting model based on long short-term memory and autoencoder. J. Hydrol. (Amst.) 644, 1321115. https://doi.org/10.1016/j.jhydrol.2024.132115.

[134] Zhang, Q., Ding, K., Lyv, T., Wang, X., Yin, Q., Zhang, Y., Yu, J., Wang, Y., Li, Xiaotong, Xiang, Z., Feng, K., Zhuang, X., Wang, Z., Qin, M., Zhang, M., Zhang, J., Cui, J., Huang, T., Yan, P., Xu, R., Chen, Hongyang, Li, Xiaolin, Fan, X., Xing, H., Chen, Huajun, 2024b. Scientific Large Language Models: a Survey on Biological & Chemical Domains. ACM Comput. Surv. 57. https://doi.org/10.1145/3715318.

[135] Zhang, T., Nie, H., Bain, T.S., Lu, H., Cui, M., Snoeyenbos-West, O.L., Franks, A.E., Nevin, K.P., Russell, T.P., Lovley, D.R., 2013. Improved cathode materials for microbial electrosynthesis. Energy Environ. Sci. 6, 217-224. https://doi.org/10.1039/C2EE23350A.

[136] Zou, L., Zhao, X., Wu, K., Liang, C., Liu, J., Yang, H., Wang, C., Yang, B., Yin, F., Zhang, W., 2024. Multi-objective optimization for microbial electrolysis cell-assisted anaerobic digestion of swine manure. Int. J. Hydrogen Energy 77, 545-556. https://doi.org/10.1016/j.ijhydene.2024.06.166.

[137] Zou, S., He, Z., 2018. Efficiently “pumping out” value-added resources from wastewater by bioelectrochemical systems: a review from energy perspectives. Water Res. 131, 62-73. https://doi.org/10.1016/J.WATRES.2017.12.026.