Artificial intelligence as a novel tool for enhancing the performance of urine fed microbial fuel cell as an emerging approach for simultaneous power generation and wastewater treatment

Background: Microbial fuel cells are effectively used in simultaneous wastewater treatment and electricity generation. Deciding the optimum operating parameters plays a significant role in cell performance. Methods: In this research, the best parameters controlling the performance of a ceramic-based microbial fuel cell (CMFC) used for urine removal have been determined by applying fuzzy modelling and an equilibrium optimizer. The target is to increase the output power of CMFC. Therefore, three input-controlling parameters were taken into consideration: membrane thickness (mm), external resistance (Ω), and anode area (cm²). Based on a measured dataset, an accurate fuzzy model is created to simulate the output power of CMFC in terms of the input parameters. Then, using equilibrium optimizer (EO), the optimal values of input parameters are identified. To confirm the superiority of the proposed strategy, a comparison with RSM has been conducted. Significant Findings: The integration between fuzzy and EO increased ceramic-based microbial fuel cell output power by around 5% compared to experimental work and RSM.