Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

In this study, four different mathematical models were considered to predict the coagulant dose in view of turbidity removal: response surface methodology (RSM), artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The results showed that...

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Autores principales:	Hichem Tahraoui, Abd-Elmouneïm Belhadj, Nassim Moula, Saliha Bouranene, Abdeltif Amrane
Formato:	article
Lenguaje:	EN HR
Publicado:	Croatian Society of Chemical Engineers 2021
Materias:	coagulation physicochemical analysis response surface methodology artificial neural networks support vector machine adaptive neuro-fuzzy inference system Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a00f6603d85849cd897ddae40c4eb06d
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id	oai:doaj.org-article:a00f6603d85849cd897ddae40c4eb06d
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spelling	oai:doaj.org-article:a00f6603d85849cd897ddae40c4eb06d2021-11-03T23:27:35ZOptimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity10.15255/KUI.2021.0010022-98301334-9090https://doaj.org/article/a00f6603d85849cd897ddae40c4eb06d2021-11-01T00:00:00Zhttp://silverstripe.fkit.hr/kui/assets/Uploads/5-675-691-KUI-11-12-2021.pdfhttps://doaj.org/toc/0022-9830https://doaj.org/toc/1334-9090In this study, four different mathematical models were considered to predict the coagulant dose in view of turbidity removal: response surface methodology (RSM), artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The results showed that all models accurately fitted the experimental data, even if the ANN model was slightly above the other models. The SVM model led to almost similar results as the ANN model; the only difference was in the validation phase, since the correlation coefficient was very high and the statistical indicators were very low for the ANN model compared to the SVM model. However, from an economic point of view, the SVM model was more appropriate than the ANN model, since its number of parameters was 22, i.e., almost half the number of parameters of the ANN model (43 parameters), while the results were almost similar in all the data phase. To reduce the economic costs further, the RSM model can also be used, which remained very useful due to its high coefficients related to the number of parameters – only 13. In addition, the statistical indicators of the RSM model remained acceptable.Hichem TahraouiAbd-Elmouneïm BelhadjNassim MoulaSaliha BouraneneAbdeltif AmraneCroatian Society of Chemical Engineersarticlecoagulationphysicochemical analysisresponse surface methodologyartificial neural networkssupport vector machineadaptive neuro-fuzzy inference systemChemistryQD1-999ENHRKemija u Industriji, Vol 70, Iss 11-12, Pp 675-675 (2021)
institution	DOAJ
collection	DOAJ
language	EN HR
topic	coagulation physicochemical analysis response surface methodology artificial neural networks support vector machine adaptive neuro-fuzzy inference system Chemistry QD1-999
spellingShingle	coagulation physicochemical analysis response surface methodology artificial neural networks support vector machine adaptive neuro-fuzzy inference system Chemistry QD1-999 Hichem Tahraoui Abd-Elmouneïm Belhadj Nassim Moula Saliha Bouranene Abdeltif Amrane Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity
description	In this study, four different mathematical models were considered to predict the coagulant dose in view of turbidity removal: response surface methodology (RSM), artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The results showed that all models accurately fitted the experimental data, even if the ANN model was slightly above the other models. The SVM model led to almost similar results as the ANN model; the only difference was in the validation phase, since the correlation coefficient was very high and the statistical indicators were very low for the ANN model compared to the SVM model. However, from an economic point of view, the SVM model was more appropriate than the ANN model, since its number of parameters was 22, i.e., almost half the number of parameters of the ANN model (43 parameters), while the results were almost similar in all the data phase. To reduce the economic costs further, the RSM model can also be used, which remained very useful due to its high coefficients related to the number of parameters – only 13. In addition, the statistical indicators of the RSM model remained acceptable.
format	article
author	Hichem Tahraoui Abd-Elmouneïm Belhadj Nassim Moula Saliha Bouranene Abdeltif Amrane
author_facet	Hichem Tahraoui Abd-Elmouneïm Belhadj Nassim Moula Saliha Bouranene Abdeltif Amrane
author_sort	Hichem Tahraoui
title	Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity
title_short	Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity
title_full	Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity
title_fullStr	Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity
title_full_unstemmed	Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity
title_sort	optimisation and prediction of the coagulant dose for the elimination of organic micropollutants based on turbidity
publisher	Croatian Society of Chemical Engineers
publishDate	2021
url	https://doaj.org/article/a00f6603d85849cd897ddae40c4eb06d
work_keys_str_mv	AT hichemtahraoui optimisationandpredictionofthecoagulantdosefortheeliminationoforganicmicropollutantsbasedonturbidity AT abdelmouneimbelhadj optimisationandpredictionofthecoagulantdosefortheeliminationoforganicmicropollutantsbasedonturbidity AT nassimmoula optimisationandpredictionofthecoagulantdosefortheeliminationoforganicmicropollutantsbasedonturbidity AT salihabouranene optimisationandpredictionofthecoagulantdosefortheeliminationoforganicmicropollutantsbasedonturbidity AT abdeltifamrane optimisationandpredictionofthecoagulantdosefortheeliminationoforganicmicropollutantsbasedonturbidity
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Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

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