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Updated: Aug 9, 2026
Electrochemically and Bioelectrochemically Induced Ammonium Recovery
Published on: January 22, 2015
Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and
Ahmad Hosseinzadeh1, John L Zhou1, Ali Altaee1
1Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
Abstract:
This study aims to analyze and model cathodic H2 recovery (rcat), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H2 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 rcat, CE, H2 production rate and total energy recovery prediction with high R2 values. The sum square error values for rcat (0.0017), CE (0.0163), H2 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, rcat and rcat were the most effective factors for rcat, CE, H2 production rate and total energy recovery, respectively.
