Output voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controller

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.