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Advanced process control for ultrafiltration membrane water treatment system

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Advanced process control for ultrafiltration membrane water treatment system. / Chew, Chun Ming; Aroua, Mohamed Kheireddine; Hussain, Mohd Azlan.

In: Journal of Cleaner Production, Vol. 179, 01.04.2018, p. 63-80.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Chew CM, Aroua MK, Hussain MA. Advanced process control for ultrafiltration membrane water treatment system. Journal of Cleaner Production. 2018 Apr 1;179:63-80. Epub 2018 Jan 16. doi: 10.1016/j.jclepro.2018.01.075

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Chew, Chun Ming ; Aroua, Mohamed Kheireddine ; Hussain, Mohd Azlan. / Advanced process control for ultrafiltration membrane water treatment system. In: Journal of Cleaner Production. 2018 ; Vol. 179. pp. 63-80.

Bibtex

@article{eae5eab09937404a92292edbc7302b9e,
title = "Advanced process control for ultrafiltration membrane water treatment system",
abstract = "Dead-end ultrafiltration (UF) has been considered as a more energy efficient operation mode compared to cross-flow filtration for the production of drinking/potable water in large-scale water treatment systems. Conventional control systems utilize pre-determined set-points for filtration and backwash durations of the constant flux dead-end UF process. Commonly known potential membrane fouling parameters such as feed water solids concentrations and specific cake resistance during filtration were not taken into considerations in the conventional control systems. In this research, artificial neural networks (ANN) predictive model and controllers were utilized for the process control of the UF process. An UF experimental system has been developed to conduct experiments and compare efficiencies of both the conventional set-points and ANN control systems. The novelty of this study is to utilize commonly available on-line and simple laboratory analysis data to estimate potential membrane fouling parameters and subsequently utilize the ANN control system to reduce water losses. Reduction of water losses were achieved by prolonging filtration duration for feed water with low turbidity using the ANN control system. This advanced control system would be of interest to operators of industrial-scale UF membrane water treatment plants for the reduction of water losses with existing facilities.",
keywords = "Process control, Ultrafiltration, Dead-end, Fouling parameters, Water treatment",
author = "Chew, {Chun Ming} and Aroua, {Mohamed Kheireddine} and Hussain, {Mohd Azlan}",
year = "2018",
month = apr,
day = "1",
doi = "10.1016/j.jclepro.2018.01.075",
language = "English",
volume = "179",
pages = "63--80",
journal = "Journal of Cleaner Production",
issn = "0959-6526",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Advanced process control for ultrafiltration membrane water treatment system

AU - Chew, Chun Ming

AU - Aroua, Mohamed Kheireddine

AU - Hussain, Mohd Azlan

PY - 2018/4/1

Y1 - 2018/4/1

N2 - Dead-end ultrafiltration (UF) has been considered as a more energy efficient operation mode compared to cross-flow filtration for the production of drinking/potable water in large-scale water treatment systems. Conventional control systems utilize pre-determined set-points for filtration and backwash durations of the constant flux dead-end UF process. Commonly known potential membrane fouling parameters such as feed water solids concentrations and specific cake resistance during filtration were not taken into considerations in the conventional control systems. In this research, artificial neural networks (ANN) predictive model and controllers were utilized for the process control of the UF process. An UF experimental system has been developed to conduct experiments and compare efficiencies of both the conventional set-points and ANN control systems. The novelty of this study is to utilize commonly available on-line and simple laboratory analysis data to estimate potential membrane fouling parameters and subsequently utilize the ANN control system to reduce water losses. Reduction of water losses were achieved by prolonging filtration duration for feed water with low turbidity using the ANN control system. This advanced control system would be of interest to operators of industrial-scale UF membrane water treatment plants for the reduction of water losses with existing facilities.

AB - Dead-end ultrafiltration (UF) has been considered as a more energy efficient operation mode compared to cross-flow filtration for the production of drinking/potable water in large-scale water treatment systems. Conventional control systems utilize pre-determined set-points for filtration and backwash durations of the constant flux dead-end UF process. Commonly known potential membrane fouling parameters such as feed water solids concentrations and specific cake resistance during filtration were not taken into considerations in the conventional control systems. In this research, artificial neural networks (ANN) predictive model and controllers were utilized for the process control of the UF process. An UF experimental system has been developed to conduct experiments and compare efficiencies of both the conventional set-points and ANN control systems. The novelty of this study is to utilize commonly available on-line and simple laboratory analysis data to estimate potential membrane fouling parameters and subsequently utilize the ANN control system to reduce water losses. Reduction of water losses were achieved by prolonging filtration duration for feed water with low turbidity using the ANN control system. This advanced control system would be of interest to operators of industrial-scale UF membrane water treatment plants for the reduction of water losses with existing facilities.

KW - Process control

KW - Ultrafiltration

KW - Dead-end

KW - Fouling parameters

KW - Water treatment

U2 - 10.1016/j.jclepro.2018.01.075

DO - 10.1016/j.jclepro.2018.01.075

M3 - Journal article

VL - 179

SP - 63

EP - 80

JO - Journal of Cleaner Production

JF - Journal of Cleaner Production

SN - 0959-6526

ER -