Experimental study of biological wastewater recovery using microbial fuel cell and application of reliability and machine learning to predict the system behavior

Abstract

Today, the water crisis has necessitated wastewater recovery management more than ever. With the advancement of technology and the emergence of microbial fuel cells (MFCs), wastewater treatment has become an exceptional opportunity for electricity production. In the present work, an experimental study has been carried out for the possibility of treating the wastewater of Borujerd textile factories in a period of ten days using an MFC. In order to save time and costs for experimental studies, the reliability analysis has been used to predict the behavior of fuel cells made in simple mode and multi-anode shared cathode mode. In addition, in order to evaluate the performance of MFCs that are made at the experimental level, deep learning has been used to extract comprehensive relationships. Finally, the economic and environmental evaluation of the simple and multi-anode shared cathode MFC has been done. The research results show that the COD removal efficiency in the system constructed for textile wastewater is 73 %, while the constructed fuel cell maximum power density is 281.5 mW/m2 from wastewater recycling. Reliability analysis of the studied system shows that adding anode to the MFC increases its operational life so for a fuel cell with 6-anodes, the life of the fuel cell increases by approximately 74 days. But in the design of an MFC, it is necessary to pay attention to the cost and emission of environmental pollutants, because the increase of the anode increases the costs and emission of environmental pollutants so that the built fuel cell will function in the most optimal way possible.