Experimental evaluation, backpropagation neural network prediction and bibliometric analysis of bioelectricity production from agricultural wastes by microbial fuel cell.

Abstract

In this study, a two-stage bioprocess combining enzymatic hydrolysis and a microbial fuel cell (MFC) was established to evaluate electricity generation from three agricultural wastes (mushroom sticks, rice husks and bamboo shavings). The highest glucose concentration of 4 g/L was obtained in the mushroom sticks hydrolysate. However, rice husk hydrolysate produced the highest power density of 26.02 mW/m2 at a current density of 29.45 mA/m2. The calorific value of the rice-husk hydrolysis residue (18.662 MJ/kg) was comparable to that of the original rice husks (19.424 MJ/kg), suggesting potential for further energy recovery through combustion. The backpropagation (BP) neural network model could be used to predict the MFC power density with the mean square error (MSE) of 0.0166 and the correlation coefficient (R) of 0.8917. Bibliometric analysis revealed that the volume of publications and citation frequency in the field of biomass-MFC technology exhibited distinct phased growth from 2006 to 2025, indicating that research outcomes continued to attract significant academic attention and the influence of core literature accumulated steadily. The research focus shifted from a singular emphasis on power generation to the resource-oriented and environmentally synergistic utilization centered on biomass. The results obtained from this study could contribute to renewable energy and circular economy by recycling agricultural wastes.