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

Journal: Bioresource technology
Published Date: Nov 1, 2020

Abstract

This study aims to analyze and model cathodic H recovery (r), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H production rate and total energy recovery with inputs of r 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, CE, H production rate and total energy recovery prediction with high R values. The sum square error values for r (0.0017), CE (0.0163), H 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 and r were the most effective factors for r, CE, H production rate and total energy recovery, respectively.

Authors

  • Ahmad Hosseinzadeh
    Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
  • John L Zhou
    Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia. Electronic address: Junliang.zhou@uts.edu.au.
  • Ali Altaee
    Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
  • Mansour Baziar
    Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran.
  • Donghao Li
    Department of Chemistry, MOE Key Laboratory of Biological Resources of Changbai Mountain & Functional Molecules, Yanbian University, Yanji 133002, Jilin Province, PR China.

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