Study on optimal conditions of flocculation in deinking wastewater treatment

Abstract Flocculation is an important method to treat paper manufacturing wastewater. Coagulants and flocculants added to wastewater facilitate the aggregation and sedimentation of various particles in the wastewater and lead to the formation of floc networks which can be easily removed using physic...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ming Li, Kaitang Hu, Jin Wang
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Deinking wastewater Flocculation Gaussian progress regression Genetic algorithm Hydraulic conditions Support vector regression Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/8623c462ae2b4e0cb1701cab364fe74f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8623c462ae2b4e0cb1701cab364fe74f
record_format	dspace
spelling	oai:doaj.org-article:8623c462ae2b4e0cb1701cab364fe74f2021-11-28T12:14:27ZStudy on optimal conditions of flocculation in deinking wastewater treatment10.1186/s44147-021-00044-61110-19032536-9512https://doaj.org/article/8623c462ae2b4e0cb1701cab364fe74f2021-11-01T00:00:00Zhttps://doi.org/10.1186/s44147-021-00044-6https://doaj.org/toc/1110-1903https://doaj.org/toc/2536-9512Abstract Flocculation is an important method to treat paper manufacturing wastewater. Coagulants and flocculants added to wastewater facilitate the aggregation and sedimentation of various particles in the wastewater and lead to the formation of floc networks which can be easily removed using physical methods. The goal of this paper is to determine the optimal hydraulic conditions using machine learning in order to enable efficient flocculation and improve performance during the treatment of deinking wastewater. Experiments using polymerized aluminum chloride as flocculant to treat deinking wastewater were carried out. Based on the orthogonal array test, 16 different combinations of hydraulic conditions were chosen to investigate the performance of flocculation, which was indicated by the turbidity of the solution after treatment. To develop a model representing the relationship between the hydraulic conditions and the performance of wastewater treatment, the machine learning methods, support vector regression and Gaussian process regression, were compared, whereby the support vector regression method was chosen. According to the fitness function derived from the support vector regression model, a genetic algorithm was applied to evaluate the optimal hydraulic conditions. Based on the optimal conditions determined by the genetic algorithm and real-life experience, a set of hydraulic conditions were implemented experimentally. After treatment under higher stirring speed at 120 rpm for 1 min and lower stirring speed at 20 rpm for 5 min at a temperature of 20 °C, the turbidity of deinking wastewater was measured as 1 NTU. The turbidity reduction was as high as 99.6%, which indicated good performance of the deinking wastewater treatment.Ming LiKaitang HuJin WangSpringerOpenarticleDeinking wastewaterFlocculationGaussian progress regressionGenetic algorithmHydraulic conditionsSupport vector regressionEngineering (General). Civil engineering (General)TA1-2040ENJournal of Engineering and Applied Science, Vol 68, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deinking wastewater Flocculation Gaussian progress regression Genetic algorithm Hydraulic conditions Support vector regression Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Deinking wastewater Flocculation Gaussian progress regression Genetic algorithm Hydraulic conditions Support vector regression Engineering (General). Civil engineering (General) TA1-2040 Ming Li Kaitang Hu Jin Wang Study on optimal conditions of flocculation in deinking wastewater treatment
description	Abstract Flocculation is an important method to treat paper manufacturing wastewater. Coagulants and flocculants added to wastewater facilitate the aggregation and sedimentation of various particles in the wastewater and lead to the formation of floc networks which can be easily removed using physical methods. The goal of this paper is to determine the optimal hydraulic conditions using machine learning in order to enable efficient flocculation and improve performance during the treatment of deinking wastewater. Experiments using polymerized aluminum chloride as flocculant to treat deinking wastewater were carried out. Based on the orthogonal array test, 16 different combinations of hydraulic conditions were chosen to investigate the performance of flocculation, which was indicated by the turbidity of the solution after treatment. To develop a model representing the relationship between the hydraulic conditions and the performance of wastewater treatment, the machine learning methods, support vector regression and Gaussian process regression, were compared, whereby the support vector regression method was chosen. According to the fitness function derived from the support vector regression model, a genetic algorithm was applied to evaluate the optimal hydraulic conditions. Based on the optimal conditions determined by the genetic algorithm and real-life experience, a set of hydraulic conditions were implemented experimentally. After treatment under higher stirring speed at 120 rpm for 1 min and lower stirring speed at 20 rpm for 5 min at a temperature of 20 °C, the turbidity of deinking wastewater was measured as 1 NTU. The turbidity reduction was as high as 99.6%, which indicated good performance of the deinking wastewater treatment.
format	article
author	Ming Li Kaitang Hu Jin Wang
author_facet	Ming Li Kaitang Hu Jin Wang
author_sort	Ming Li
title	Study on optimal conditions of flocculation in deinking wastewater treatment
title_short	Study on optimal conditions of flocculation in deinking wastewater treatment
title_full	Study on optimal conditions of flocculation in deinking wastewater treatment
title_fullStr	Study on optimal conditions of flocculation in deinking wastewater treatment
title_full_unstemmed	Study on optimal conditions of flocculation in deinking wastewater treatment
title_sort	study on optimal conditions of flocculation in deinking wastewater treatment
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/8623c462ae2b4e0cb1701cab364fe74f
work_keys_str_mv	AT mingli studyonoptimalconditionsofflocculationindeinkingwastewatertreatment AT kaitanghu studyonoptimalconditionsofflocculationindeinkingwastewatertreatment AT jinwang studyonoptimalconditionsofflocculationindeinkingwastewatertreatment
_version_	1718408151162683392

Study on optimal conditions of flocculation in deinking wastewater treatment

Ejemplares similares