Evidence map›Paper›PMID 31295918›Full record

ArticleInternational journal of environmental research and public health2019

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

Yu Mei, Jiaqian Yang, Yin Lu, Feilin Hao, Dongmei Xu, Hua Pan, Jiade Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yu MeiCollege of Environment, Zhejiang University of Technology, Hangzhou 310032, China.
Jiaqian YangCollege of Environment, Zhejiang University of Technology, Hangzhou 310032, China.
Yin LuCollege of Biological and Environmental Engineering, Zhejiang Shuren University, Hangzhou 310005, China.
Feilin HaoCollege of Biological and Environmental Engineering, Zhejiang Shuren University, Hangzhou 310005, China.
Dongmei XuCollege of Biological and Environmental Engineering, Zhejiang Shuren University, Hangzhou 310005, China.
Hua PanCollege of Biological and Environmental Engineering, Zhejiang Shuren University, Hangzhou 310005, China.
Jiade WangCollege of Environment, Zhejiang University of Technology, Hangzhou 310032, China. jdwang@zjut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Neural Networks, ComputerOxidation-ReductionBiological Oxygen Demand AnalysisChlorophenolsElectrochemical TechniquesKineticsWastewaterWater Pollutants, ChemicalWater Purification2-chlorophenolChlorophenolsWastewaterWater Pollutants, ChemicalBP–ANNelectro-oxidationPSO–ANN

Identifiers

PMID31295918
PMCPMC6679230

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.