A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence

Yaser Abdollahfard, Mehdi Sedighi and Mostafa Ghasemi ()
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Yaser Abdollahfard: Petroleum Engineering Department, Amirkabir University of Technology, Tehran 158754413, Iran
Mehdi Sedighi: Department of Chemical Engineering, University of Qom, Qom 3716146611, Iran
Mostafa Ghasemi: Chemical Engineering Section, Faculty of Engineering, Sohar University, Sohar 311, Oman

Sustainability, 2023, vol. 15, issue 2, 1-14

Abstract: Microbial fuel cells have recently received considerable attention as a potential source of renewable energy. Due to its complex and hybrid nature, it has significant nonlinear features and substantial hysteresis behavior, making it hard to optimize and control its power generation directly. This study modeled power density and COD removal using random forest regression and gradient boost regression trees. System inputs are three key parameters that affect performance and commercialization. There is a range of 0.1–0.5 mg/cm 2 of Pt, a degree of sulfonation of sulfonated polyether-etherketone varying from 20% to 80%, and a cathode aeration rate of 10–150 mL/min. Based on the model’s accuracies, gradient boost regression was selected for power density prediction and random forest for COD removal prediction. Particle swarm optimization was used as the optimization algorithm after selecting the best models to maximize COD removal and power density. It was found that DS was the most critical parameter for COD removal, and Pt was the most critical parameter for power density. There is a different optimal input value for each model. In order to maximize power density, DS (%) must be 67.7087, Pt (mg/cm 2 ) must be 0.3943, and Aeration (mL/min) must be 117.7192. To maximize COD removal, the DS (%) must be 75.8816, the Pt (mg/cm 2 ) must be 0.3322, and the Aeration (mL/min) must be 75.1933.

Keywords: artificial intelligence; microbial fuel cell; machine learning; random forest regression; gradient boost regression tree; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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