Employing Artificial Intelligence for Enhanced Microbial Fuel Cell Performance through Wolf Vitamin Solution Optimization

Hamed Farahani, Mostafa Ghasemi (), Mehdi Sedighi and Nitin Raut
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Hamed Farahani: Department of Chemical Engineering, University of Qom, Qom 3716146611, Iran
Mostafa Ghasemi: Chemical Engineering Section, Faculty of Engineering, Sohar University, Sohar 311, Oman
Mehdi Sedighi: Department of Chemical Engineering, University of Qom, Qom 3716146611, Iran
Nitin Raut: Chemical Engineering Section, Faculty of Engineering, Sohar University, Sohar 311, Oman

Sustainability, 2024, vol. 16, issue 15, 1-17

Abstract: The culture medium composition plays a critical role in optimizing the performance of microbial fuel cells (MFCs). One under-investigated aspect of the medium is the impact of the Wolf vitamin solution. This solution, known to contain essential vitamins like biotin, folic acid, vitamin B12, and thiamine, is believed to enhance bacterial growth and biofilm formation within the MFC. The influence of varying Wolf vitamin solution concentrations (2, 4, 6, 8, and 10 mL) on microbial fuel cell (MFC) performance is investigated in this study. Python 3.7.0 software is employed to enhance and anticipate the performance of MFC systems. Four distinct machine-learning algorithms, namely adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), categorical boosting algorithm (CatBoost), and support vector regression (SVR), are implemented to predict power density. In this study, a data split of 80% for training and 20% for testing was employed to optimize the artificial intelligence (AI) model. The analysis revealed that the optimal concentration of Wolf mineral solution was 5.8 mL. The corresponding error percentages between the experimental and AI-predicted values for current density, power generation, COD removal, and coulombic efficiency were found to be remarkably low at 0.79%, 0.5%, 1.89%, and 1.27%, respectively. These findings highlight the significant role of Wolf mineral solution in maximizing MFC performance and demonstrate the exceptional precision of the AI model in accurately predicting MFC behavior.

Keywords: microbial fuel cell; sustainability; Wolf mineral solution; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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