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

Ahmad Hosseinzadeh; John L. Zhou; Ali Altaee; Mansour Baziar; Donghao Li

doi:10.1016/j.biortech.2020.123967

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

Ahmad Hosseinzadeh, John L. Zhou, Ali Altaee, Mansour Baziar, Donghao Li

Research output: Journal Publication › Article › peer-review

48 Citations (Scopus)

Abstract

This study aims to analyze and model cathodic H₂ recovery (r_cat), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H₂ production rate and total energy recovery with inputs of r_cat 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, H₂ production rate and total energy recovery prediction with high R² values. The sum square error values for r_cat (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, r_cat and r_cat were the most effective factors for r_cat, CE, H₂ production rate and total energy recovery, respectively.

Original language	English
Article number	123967
Journal	Bioresource Technology
Volume	316
DOIs	https://doi.org/10.1016/j.biortech.2020.123967
Publication status	Published - Nov 2020
Externally published	Yes

Keywords

ANFIS
ANN
Bio-hydrogen
Machine learning
MEC

ASJC Scopus subject areas

Bioengineering
Environmental Engineering
Renewable Energy, Sustainability and the Environment
Waste Management and Disposal

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.biortech.2020.123967

Cite this

@article{fbc402a751d94f389beef1d7877f577a,

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

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.",

keywords = "ANFIS, ANN, Bio-hydrogen, Machine learning, MEC",

author = "Ahmad Hosseinzadeh and Zhou, \{John L.\} and Ali Altaee and Mansour Baziar and Donghao Li",

note = "Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",

year = "2020",

month = nov,

doi = "10.1016/j.biortech.2020.123967",

language = "English",

volume = "316",

journal = "Bioresource Technology",

issn = "0960-8524",

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}

TY - JOUR

T1 - Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system

AU - Hosseinzadeh, Ahmad

AU - Zhou, John L.

AU - Altaee, Ali

AU - Baziar, Mansour

AU - Li, Donghao

PY - 2020/11

Y1 - 2020/11

N2 - 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.

AB - 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.

KW - ANFIS

KW - ANN

KW - Bio-hydrogen

KW - Machine learning

KW - MEC

UR - https://www.scopus.com/pages/publications/85089138931

U2 - 10.1016/j.biortech.2020.123967

DO - 10.1016/j.biortech.2020.123967

M3 - Article

C2 - 32777721

AN - SCOPUS:85089138931

SN - 0960-8524

VL - 316

JO - Bioresource Technology

JF - Bioresource Technology

M1 - 123967

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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