Journal
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 47, Issue 45, Pages 19837-19849Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2022.03.113
Keywords
Microbial fuel cell; Control; Neuro-fuzzy; Optimization; Particle swarm; Grey wolf
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An optimization-based neuro-fuzzy inference controller is proposed to improve the voltage tracking performance of microbial fuel cells (MFC). The results show that controllers based on Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) have efficient performances in quickly responding to reference voltage patterns and robustly handling external load changes.
Microbial fuel cell (MFC) has become a very important biotechnological tool to produce clean energy in recent years. It is very important to adjust the output voltage and power density in order to obtain the desired energy quickly and smoothly at the output of the MFC. In this study, an optimization-based neuro-fuzzy inference controller is proposed for improving voltage tracking performance of the MFC. A double-chambers MFC model including biochemical reactions, Butler-Volmer expressions and mass/charge balances was studied and Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) algorithms are used to adjust the parameters of the neuro-fuzzy controller. The results show that PSO and IGWO based controllers have efficient performances to follow the reference voltage pattern quickly and robustly against external load changes, distributions and parameter uncertainties. Moreover, it was observed that IGWO was a more stable and robust controller than PSO according to rise time, overshoot and peak time. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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