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    Machine learning solutions for enhanced performance in plant-based microbial fuel cells
    (Pergamon-Elsevier Science Ltd, 2024) Gurbuz, Tugba; Gunay, M. Erdem; Tapan, N. Alper
    It is well known that numerous operational, material and design variables act upon the performance of a plantbased microbial fuel cell which is an emerging sustainable and versatile energy device like hydrogen fuel cells. However, due to the high complexity of these bioelectrochemical systems, new solutions are required to optimize performance and uncover hidden relationships between dominant fuel cell variables. For this purpose, a database of 229 observations was created for plant-based microbial fuel cells (PMFCs) with 159 descriptor variables and a target variable (maximum power density) based on experimental results from 51 recent publications. Then, some machine learning solutions like principal component analysis (PCA), classification trees and SHapley Additive exPlanations (SHAP) analysis were applied. The PCA indicated mainly two routes involving low and high chemical oxygen demand (COD) towards high maximum power density which consists of the plant family, wastewater type, support media, construction design, separator type, anode and cathode electrodes and light source. SHAP analysis revealed that the most important factors for high performance are operating temperature, natural light, soil support medium, and constructed wetland design. Finally, the classification tree successfully demonstrated nine routes towards high maximum power density which exclude the use of graphite plate cathode electrodes.
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    Routes to optimum conditions of plant based microbial fuel cells by reinforcement learning
    (Pergamon-Elsevier Science Ltd, 2025) Tapan, N. Alper; Gunay, M. Erdem; Gurbuz, Tugba
    Plant-based microbial fuel cells (PMFC) are fascinating technologies that have the potential to combine plants and bacteria to produce electricity from different solid and aqueous media like constructed wetlands and wastewater treatment facilities. Although PMFCs are evolving and demonstrating promising performance results for the development of sustainable energy and water treatment, they have not reached their full potential due to issues with continuous bioenergy generation and fuel cell system optimization through plant selection, operating conditions, electrodes, and light source, all of which are critical for optimum microbial activity on the roots and exudate rhizodeposition. In light of this, the Q learning algorithm was used in this study to determine the routes that lead to the best operating and material conditions for PMFCs. A database of 231 observations from 51 recent publications with 271 descriptors (input variables) and 3 output variables (under 9 categories were used to determine the routes leading to high maximum power density, medium open circuit potential and current density. It was seen that high maximum power density routes are achievable through the nodes of stainless-steel mesh cathode with metal-based chitosan smart catalysts, bicarbonate wastewater, and anaerobic wetland sediment. Data visualizations by radar charts also exhibited similar results for cathode material and wastewater type. For medium open circuit potential, Iris pseudacorus and for medium maximum current density anaerobic sludge inoculation and steel wire mesh/nickel current collectors are found to be important indicators.