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BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System

Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understanding of electrochemical treatment without increasing...

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Autores principales: Mei, Yu, Yang, Jiaqian, Lu, Yin, Hao, Feilin, Xu, Dongmei, Pan, Hua, Wang, Jiade
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679230/
https://www.ncbi.nlm.nih.gov/pubmed/31295918
http://dx.doi.org/10.3390/ijerph16142454
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author Mei, Yu
Yang, Jiaqian
Lu, Yin
Hao, Feilin
Xu, Dongmei
Pan, Hua
Wang, Jiade
author_facet Mei, Yu
Yang, Jiaqian
Lu, Yin
Hao, Feilin
Xu, Dongmei
Pan, Hua
Wang, Jiade
author_sort Mei, Yu
collection PubMed
description Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understanding of electrochemical treatment without increasing the cost. The backpropagation artificial neural network (BP–ANN) model was applied to predict chemical oxygen demand (COD) removal efficiency and total energy consumption (TEC). Current density, pH, supporting electrolyte concentration, and oxidation–reduction potential (ORP) were used as input parameters in the 2-chlorophenol synthetic wastewater model. Prediction accuracy was increased by using particle swarm optimization coupled with BP–ANN to optimize weight and threshold values. The particle swarm optimization BP–ANN (PSO–BP–ANN) for the efficient prediction of COD removal efficiency and TEC for testing data showed high correlation coefficient of 0.99 and 0.9944 and a mean square error of 0.0015526 and 0.0023456. The weight matrix analysis indicated that the correlation of the five input parameters was a current density of 18.85%, an initial pH 21.11%, an electrolyte concentration 19.69%, an oxidation time of 21.30%, and an ORP of 19.05%. The analysis of removal kinetics indicated that oxidation–reduction potential (ORP) is closely correlated with the chemical oxygen demand (COD) and total energy consumption (TEC) of the electro-oxidation degradation of 2-chlorophenol in wastewater.
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spelling pubmed-66792302019-08-19 BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System Mei, Yu Yang, Jiaqian Lu, Yin Hao, Feilin Xu, Dongmei Pan, Hua Wang, Jiade Int J Environ Res Public Health Article Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understanding of electrochemical treatment without increasing the cost. The backpropagation artificial neural network (BP–ANN) model was applied to predict chemical oxygen demand (COD) removal efficiency and total energy consumption (TEC). Current density, pH, supporting electrolyte concentration, and oxidation–reduction potential (ORP) were used as input parameters in the 2-chlorophenol synthetic wastewater model. Prediction accuracy was increased by using particle swarm optimization coupled with BP–ANN to optimize weight and threshold values. The particle swarm optimization BP–ANN (PSO–BP–ANN) for the efficient prediction of COD removal efficiency and TEC for testing data showed high correlation coefficient of 0.99 and 0.9944 and a mean square error of 0.0015526 and 0.0023456. The weight matrix analysis indicated that the correlation of the five input parameters was a current density of 18.85%, an initial pH 21.11%, an electrolyte concentration 19.69%, an oxidation time of 21.30%, and an ORP of 19.05%. The analysis of removal kinetics indicated that oxidation–reduction potential (ORP) is closely correlated with the chemical oxygen demand (COD) and total energy consumption (TEC) of the electro-oxidation degradation of 2-chlorophenol in wastewater. MDPI 2019-07-10 2019-07 /pmc/articles/PMC6679230/ /pubmed/31295918 http://dx.doi.org/10.3390/ijerph16142454 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mei, Yu
Yang, Jiaqian
Lu, Yin
Hao, Feilin
Xu, Dongmei
Pan, Hua
Wang, Jiade
BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System
title BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System
title_full BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System
title_fullStr BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System
title_full_unstemmed BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System
title_short BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System
title_sort bp–ann model coupled with particle swarm optimization for the efficient prediction of 2-chlorophenol removal in an electro-oxidation system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679230/
https://www.ncbi.nlm.nih.gov/pubmed/31295918
http://dx.doi.org/10.3390/ijerph16142454
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