Modelling and Optimization of Microbial Fuel Cell

Abstract

Microbial Fuel Cells (MFCs) are an innovative technology that stands at the intersection of renewable energy production and wastewater treatment. MFCs provide a sustainable solution for the production of energy by utilizing the metabolic processes of microbes to transform organic substrates into electrical energy. MFCs model must be optimized to improve their performance. Due to some problems such as low power density, substrate constraints, and inefficiencies in electron transmission, MFC can’t achieve their potential. In this study we have adopted a comprehensive mathematical model for MFCs, incorporating critical factors like electron transmission, microbial activity and substrate consumption. This thesis investigates the application of advanced optimization techniques to improve MFC performance metrics, particularly focusing on current output and overall efficiency. To replicate MFC operations, a thorough mathematical model is adopted, taking into account important variables including anodic and cathodic reactions, microbial activity, and substrate concentration. In order to optimize MFC parameters, the study uses Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Gradient-Based Optimization (GBO). Through comparative evaluations of numerical simulations, the effectiveness of various methodsis assessed. The outcomesreveal substantial improvements in current output, with GWO exhibiting remarkable efficiency in systems with complicated dynamics and GA and PSO obtaining greater enhancements in the earlier phases. Despite being less computationally demanding, GBO offers a reliable starting point for parameter optimization. The study's conclusion highlightsthe necessity of interdisciplinary approachesto fully realize the potential of this sustainable technology and offers insights into how optimization techniques will be integrated into MFC design in the future