Artificial neural network (ANN) and response surface methodology (RSM) algorithm-based improvement, kinetics and isotherm studies of electrocoagulation of oily wastewater

The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments wer...

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Published inJournal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering Vol. 57; no. 7; pp. 584 - 592
Main Authors Changmai, Murchana, Singh, Monika
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 22.07.2022
Taylor & Francis Ltd
Subjects
Algorithms
analysis of variance
artificial neural network
Artificial neural networks
Electrocoagulation
Electrodes
Grease
kinetics
Neural networks
oil and grease
oils
Optimization
Parameters
pH effects
Reaction time
Response surface methodology
Variance analysis
Wastewater
Wastewater treatment
Online AccessGet full text
ISSN1093-4529
1532-4117
1532-4117
DOI10.1080/10934529.2022.2090192

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Abstract The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m - 2 , 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.
AbstractList The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m–2, 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.
The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m-2, 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m-2, 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.
The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m– ², 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.
The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m - 2 , 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.
Author Changmai, Murchana
Singh, Monika
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SubjectTerms Algorithms
analysis of variance
artificial neural network
Artificial neural networks
Electrocoagulation
Electrodes
Grease
kinetics
Neural networks
oil and grease
oils
Optimization
Parameters
pH effects
Reaction time
Response surface methodology
Variance analysis
Wastewater
Wastewater treatment
Title Artificial neural network (ANN) and response surface methodology (RSM) algorithm-based improvement, kinetics and isotherm studies of electrocoagulation of oily wastewater
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