Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks

Dissolved air flotation (DAF) is a physical separation process that uses air microbubbles to remove suspended material dispersed in a liquid phase. Even though DAF is considered a well-established unit operation, modeling it is difficult due to the complexity of the phenomena involved, resulting in...

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Autores principales:	Ana Cláudia Oliveira e Souza, Newton Libanio Ferreira, Flávio Vasconcelos da Silva
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial neural networks dissolved air flotation process modeling water treatment Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/2ea66692b68046c6a82ca8e1d36e245c
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id	oai:doaj.org-article:2ea66692b68046c6a82ca8e1d36e245c
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spelling	oai:doaj.org-article:2ea66692b68046c6a82ca8e1d36e245c2021-11-23T18:57:09ZEmpirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks1606-97491607-079810.2166/ws.2021.152https://doaj.org/article/2ea66692b68046c6a82ca8e1d36e245c2021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3946https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Dissolved air flotation (DAF) is a physical separation process that uses air microbubbles to remove suspended material dispersed in a liquid phase. Even though DAF is considered a well-established unit operation, modeling it is difficult due to the complexity of the phenomena involved, resulting in conceptual models with no practical application. Thereby, the objective of this work was to evaluate empirical modeling efficiency in predicting the turbidity removal dynamic using artificial neural networks applied to a DAF prototype. For the study of the neural network input variables, a two-level, full-factorial design was utilized to verify the statistical significance of the saturation pressure and the saturated water flow rate in relation to the turbidity removal. Using a time-delay recurrent neural network architecture, two empirical models were proposed to simulate the dynamic behavior of the turbidity removal promoted by the DAF prototype. The real-time model provided good predictions with R = 0.9717 and MSE = 1.0482, and the simulation model was also able to predict the process behavior presenting performance criteria equal to R = 0.9475 and MSE = 1.8640. HIGHLIGHTS Development of empirical models capable of efficiently predicting the turbidity removal dynamic behavior of a DAF prototype using artificial neural networks.; The simulation model can predict the TBDR without the need to perform an experimental run with R2 = 0.9475.; The real-time model can be used in applications that demand an online model, and it provided good predictions with R2 = 0.9717.;Ana Cláudia Oliveira e SouzaNewton Libanio FerreiraFlávio Vasconcelos da SilvaIWA Publishingarticleartificial neural networksdissolved air flotationprocess modelingwater treatmentWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3946-3959 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks dissolved air flotation process modeling water treatment Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	artificial neural networks dissolved air flotation process modeling water treatment Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Ana Cláudia Oliveira e Souza Newton Libanio Ferreira Flávio Vasconcelos da Silva Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks
description	Dissolved air flotation (DAF) is a physical separation process that uses air microbubbles to remove suspended material dispersed in a liquid phase. Even though DAF is considered a well-established unit operation, modeling it is difficult due to the complexity of the phenomena involved, resulting in conceptual models with no practical application. Thereby, the objective of this work was to evaluate empirical modeling efficiency in predicting the turbidity removal dynamic using artificial neural networks applied to a DAF prototype. For the study of the neural network input variables, a two-level, full-factorial design was utilized to verify the statistical significance of the saturation pressure and the saturated water flow rate in relation to the turbidity removal. Using a time-delay recurrent neural network architecture, two empirical models were proposed to simulate the dynamic behavior of the turbidity removal promoted by the DAF prototype. The real-time model provided good predictions with R = 0.9717 and MSE = 1.0482, and the simulation model was also able to predict the process behavior presenting performance criteria equal to R = 0.9475 and MSE = 1.8640. HIGHLIGHTS Development of empirical models capable of efficiently predicting the turbidity removal dynamic behavior of a DAF prototype using artificial neural networks.; The simulation model can predict the TBDR without the need to perform an experimental run with R2 = 0.9475.; The real-time model can be used in applications that demand an online model, and it provided good predictions with R2 = 0.9717.;
format	article
author	Ana Cláudia Oliveira e Souza Newton Libanio Ferreira Flávio Vasconcelos da Silva
author_facet	Ana Cláudia Oliveira e Souza Newton Libanio Ferreira Flávio Vasconcelos da Silva
author_sort	Ana Cláudia Oliveira e Souza
title	Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks
title_short	Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks
title_full	Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks
title_fullStr	Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks
title_full_unstemmed	Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks
title_sort	empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/2ea66692b68046c6a82ca8e1d36e245c
work_keys_str_mv	AT anaclaudiaoliveiraesouza empiricalmodelingofturbidityremovalinadissolvedairflotationsystemapplicationofartificialneuralnetworks AT newtonlibanioferreira empiricalmodelingofturbidityremovalinadissolvedairflotationsystemapplicationofartificialneuralnetworks AT flaviovasconcelosdasilva empiricalmodelingofturbidityremovalinadissolvedairflotationsystemapplicationofartificialneuralnetworks
_version_	1718416142962262016

Empirical modeling of turbidity removal in a dissolved air flotation system: application of artificial neural networks

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