Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system

摘要

This study aims to analyze and model cathodic H-2 recovery (r(cat)), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H-2 production rate and total energy recovery with inputs of rcat and CE in a microbial electrolysis cell using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) procedures. Both ANN and ANFIS models demonstrated great goodness of fit for r(cat), CE, H-2 production rate and total energy recovery prediction with high R-2 values. The sum square error values for r(cat) (0.0017), CE (0.0163), H-2 production rate (0.1062) and total energy recovery (0.0136) in ANN models were slightly higher than those in ANFIS models at 0.0005, 0.0091, 0.1247 and 0.0148 respectively. Sensitivity analysis by ANN models demonstrated that voltage, EC, r(cat) and r(cat) were the most effective factors for rcat, CE, H-2 production rate and total energy recovery, respectively.