Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works

Generally, in India, determining the chlorine and coagulant dosage in a WTP depends on the proficiency of operators, which may lead to overdosing or underdosing of coagulants and chlorine. Nevertheless, the determination of both coagulant and chlorine dosages frequently changes as inlet water qualit...

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Main Author: Dnyaneshwar V. Wadkar, Ganesh C. Chikute, Pravin S. Patil, Pallavi D. Wadkar and Manasi G. Chikute
Format: Article
Language:English
Published: Technoscience Publications 2024-12-01
Series:Nature Environment and Pollution Technology
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Online Access:https://neptjournal.com/upload-images/(31)B-4155.pdf
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author Dnyaneshwar V. Wadkar, Ganesh C. Chikute, Pravin S. Patil, Pallavi D. Wadkar and Manasi G. Chikute
author_facet Dnyaneshwar V. Wadkar, Ganesh C. Chikute, Pravin S. Patil, Pallavi D. Wadkar and Manasi G. Chikute
author_sort Dnyaneshwar V. Wadkar, Ganesh C. Chikute, Pravin S. Patil, Pallavi D. Wadkar and Manasi G. Chikute
collection DOAJ
description Generally, in India, determining the chlorine and coagulant dosage in a WTP depends on the proficiency of operators, which may lead to overdosing or underdosing of coagulants and chlorine. Nevertheless, the determination of both coagulant and chlorine dosages frequently changes as inlet water quality varies which demands extensive laboratory analyses, leading to prolonged experimentation periods in water treatment plants. So objective of the study is to develop the precise relationship between coagulant dose and chlorine dose in a water treatment plant by using an artificial neural network (ANN). As a result, ANN models were developed to predict chlorine dose using coagulant dose by comparing the performance of the number of ANN models. It has been found that radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN) modeling provide better prediction. In RBFNN and GRNN modeling, the spread factor is varied from 0.1 to 15 to establish a stable and accurate model with high predictive accuracy. It is observed that the RBFNN model showed good prediction (R2 = 0.999). The application of a soft computing model for defining doses of coagulant and chlorine that are inextricably linked at a Water treatment plant (WTP) will be highly beneficial for WTP Managers.
format Article
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institution Kabale University
issn 0972-6268
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publishDate 2024-12-01
publisher Technoscience Publications
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series Nature Environment and Pollution Technology
spelling doaj-art-7bdd617c62eb49f6853444672a2035452025-01-20T07:13:36ZengTechnoscience PublicationsNature Environment and Pollution Technology0972-62682395-34542024-12-012342273228110.46488/NEPT.2024.v23i04.031Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment WorksDnyaneshwar V. Wadkar, Ganesh C. Chikute, Pravin S. Patil, Pallavi D. Wadkar and Manasi G. ChikuteGenerally, in India, determining the chlorine and coagulant dosage in a WTP depends on the proficiency of operators, which may lead to overdosing or underdosing of coagulants and chlorine. Nevertheless, the determination of both coagulant and chlorine dosages frequently changes as inlet water quality varies which demands extensive laboratory analyses, leading to prolonged experimentation periods in water treatment plants. So objective of the study is to develop the precise relationship between coagulant dose and chlorine dose in a water treatment plant by using an artificial neural network (ANN). As a result, ANN models were developed to predict chlorine dose using coagulant dose by comparing the performance of the number of ANN models. It has been found that radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN) modeling provide better prediction. In RBFNN and GRNN modeling, the spread factor is varied from 0.1 to 15 to establish a stable and accurate model with high predictive accuracy. It is observed that the RBFNN model showed good prediction (R2 = 0.999). The application of a soft computing model for defining doses of coagulant and chlorine that are inextricably linked at a Water treatment plant (WTP) will be highly beneficial for WTP Managers.https://neptjournal.com/upload-images/(31)B-4155.pdfurban plants, xenobiotics, mycorrhiza, heavy metals, pollution
spellingShingle Dnyaneshwar V. Wadkar, Ganesh C. Chikute, Pravin S. Patil, Pallavi D. Wadkar and Manasi G. Chikute
Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works
Nature Environment and Pollution Technology
urban plants, xenobiotics, mycorrhiza, heavy metals, pollution
title Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works
title_full Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works
title_fullStr Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works
title_full_unstemmed Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works
title_short Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works
title_sort effectiveness of different artificial neural network models in establishing the suitable dosages of coagulant and chlorine in water treatment works
topic urban plants, xenobiotics, mycorrhiza, heavy metals, pollution
url https://neptjournal.com/upload-images/(31)B-4155.pdf
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