A novel method for determination of a time period for stabilization of power generation of microbial fuel cell with effect of microorganisms

Microbial fuel cells (MFCs) are quickly gaining traction in the mainstream industry due to their capabilities in simultaneous power generation and wastewater purification. They use bacteria like Shewanella and Geobacter as primary units for the same. However, their power generation capabilities are limited by a lack of stability in certain configurations. For the development of appropriate power storage and management systems, this instability must be investigated. Therefore, the present study proposes the artificial intelligence (AI) methodology of artificial neural search (ANS) networks to predict the period for stabilization of power generation of microbial fuel cell in the presence of microorganisms. An output voltage has been measured as a function of time (approximately 1600 h). A stabilization period of power generation has been predicted from the slope obtained from the graph of voltage vs time. The analysis of the ANS model indicated that the power generation stabilization has occurred between 12th and 16th weeks. Experiments were then performed to validate the findings from the ANS model. This may serve as an indication for the development of energy management and storage systems that can account for the trends observed during this study.