Artificial intelligence-based modeling and optimization of microbial electrolysis cell-assisted anaerobic digestion fed with alkaline-pretreated waste-activated sludge

Abstract

Microbial electrolysis cell-assisted anaerobic digestion (MEC-AD) is a promising emerging strategy to enhance simultaneously waste treatment and biomethane recovery from various biowastes, particularly waste-activated sludge (WAS). However, MEC-AD is still in the early stages of development, with numerous experimental studies but no modeling or optimization. Thus, to provide an effective modeling and optimization tool for this process, this study proposed applying artificial intelligence for the first time. The literature-based experimental data of MEC-AD fed with alkaline-pretreated waste-activated sludge (al-WAS) were used for this purpose. Accordingly, a two-hidden-layer artificial neural network (ANN) with topology 2-25-34-6, obtained from the response surface methodology, showed the best agreement between actual and predicted data with a low mean squared error of 0.0579 and a high R-value of 0.9870. This best ANN model was then optimized by particle swarm optimization. As a result, an Eapp of 0.63 V was found to be optimal for al-WAS-fed MEC-AD with a highest net energy output of 41.3 KJ/L-reactor (-2.6 MJ/kg-WAS) and highest net monetary value of 0.72 $/L-reactor (-45 $/kg-WAS), which enhanced around 160 % and 300 % compared to AD alone. These findings can support decision-making for managers and operators in wastewater treatment, biomass waste management, and renewable energy sectors.