Machine learning solutions for enhanced performance in plant-based microbial fuel cells

Abstract

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.