Routes to optimum conditions of plant based microbial fuel cells by reinforcement learning

Abstract

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.