Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -