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A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression

Membrane fouling is a complicated issue in microfiltration and ultrafiltration. Clearly identifying the dominant fouling mechanisms during the filtration process is of great significance for the phased and targeted control of fouling. To this end, we propose a semi-empirical multiple linear regressi...

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Autores principales: Xu, Hao, Xiao, Kang, Yu, Jinlan, Huang, Bin, Wang, Xiaomao, Liang, Shuai, Wei, Chunhai, Wen, Xianghua, Huang, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465108/
https://www.ncbi.nlm.nih.gov/pubmed/32751292
http://dx.doi.org/10.3390/membranes10080171
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author Xu, Hao
Xiao, Kang
Yu, Jinlan
Huang, Bin
Wang, Xiaomao
Liang, Shuai
Wei, Chunhai
Wen, Xianghua
Huang, Xia
author_facet Xu, Hao
Xiao, Kang
Yu, Jinlan
Huang, Bin
Wang, Xiaomao
Liang, Shuai
Wei, Chunhai
Wen, Xianghua
Huang, Xia
author_sort Xu, Hao
collection PubMed
description Membrane fouling is a complicated issue in microfiltration and ultrafiltration. Clearly identifying the dominant fouling mechanisms during the filtration process is of great significance for the phased and targeted control of fouling. To this end, we propose a semi-empirical multiple linear regression model to describe flux decline, incorporating the five fouling mechanisms (the first and second kinds of standard blocking, complete blocking, intermediate blocking, and cake filtration) based on the additivity of the permeate volume contributed by different coexisting mechanisms. A piecewise fitting protocol was established to distinguish the fouling stages and find the significant mechanisms in each stage. This approach was applied to a case study of a microfiltration membrane filtering a model foulant solution composed of polysaccharide, protein, and humic substances, and the model fitting unequivocally revealed that the dominant fouling mechanism evolved in the sequence of initial adaptation, fast adsorption followed by slow adsorption inside the membrane pores, and the gradual growth of a cake/gel layer on the membrane surface. The results were in good agreement with the permeate properties (total organic carbon, ultraviolet absorbance, and fluorescence) during the filtration process. This modeling approach proves to be simple and reliable for identifying the main fouling mechanisms during membrane filtration with statistical confidence.
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spelling pubmed-74651082020-09-04 A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression Xu, Hao Xiao, Kang Yu, Jinlan Huang, Bin Wang, Xiaomao Liang, Shuai Wei, Chunhai Wen, Xianghua Huang, Xia Membranes (Basel) Article Membrane fouling is a complicated issue in microfiltration and ultrafiltration. Clearly identifying the dominant fouling mechanisms during the filtration process is of great significance for the phased and targeted control of fouling. To this end, we propose a semi-empirical multiple linear regression model to describe flux decline, incorporating the five fouling mechanisms (the first and second kinds of standard blocking, complete blocking, intermediate blocking, and cake filtration) based on the additivity of the permeate volume contributed by different coexisting mechanisms. A piecewise fitting protocol was established to distinguish the fouling stages and find the significant mechanisms in each stage. This approach was applied to a case study of a microfiltration membrane filtering a model foulant solution composed of polysaccharide, protein, and humic substances, and the model fitting unequivocally revealed that the dominant fouling mechanism evolved in the sequence of initial adaptation, fast adsorption followed by slow adsorption inside the membrane pores, and the gradual growth of a cake/gel layer on the membrane surface. The results were in good agreement with the permeate properties (total organic carbon, ultraviolet absorbance, and fluorescence) during the filtration process. This modeling approach proves to be simple and reliable for identifying the main fouling mechanisms during membrane filtration with statistical confidence. MDPI 2020-07-29 /pmc/articles/PMC7465108/ /pubmed/32751292 http://dx.doi.org/10.3390/membranes10080171 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Hao
Xiao, Kang
Yu, Jinlan
Huang, Bin
Wang, Xiaomao
Liang, Shuai
Wei, Chunhai
Wen, Xianghua
Huang, Xia
A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression
title A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression
title_full A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression
title_fullStr A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression
title_full_unstemmed A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression
title_short A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression
title_sort simple method to identify the dominant fouling mechanisms during membrane filtration based on piecewise multiple linear regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465108/
https://www.ncbi.nlm.nih.gov/pubmed/32751292
http://dx.doi.org/10.3390/membranes10080171
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