Evaluation, modelling and control of ultrafiltration membrane water treatment systems / Chew Chun Ming
Ultrafiltration (UF) membrane water treatment systems are swiftly gaining acceptance for large-scale production of potable/drinking water supply in Malaysia. In the first part of this research, extensive efforts have been taken to analyze and evaluate an industrial-scale UF membrane water treatme...
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Format: | Thesis |
Published: |
2017
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Online Access: | http://studentsrepo.um.edu.my/8275/1/All.pdf http://studentsrepo.um.edu.my/8275/6/chun_ming.pdf http://studentsrepo.um.edu.my/8275/ |
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Institution: | Universiti Malaya |
Summary: | Ultrafiltration (UF) membrane water treatment systems are swiftly gaining
acceptance for large-scale production of potable/drinking water supply in Malaysia. In
the first part of this research, extensive efforts have been taken to analyze and evaluate
an industrial-scale UF membrane water treatment plant in Malaysia through detailed
case studies. Analysis of the UF membrane water treatment plant was performed to
highlight the common design and operational issues with suggested solutions obtained
from literature. Subsequently, evaluation and comparison between the UF membrane
water treatment system and the conventional media/sand filtration water treatment
system was conducted. Detailed analyses on commercial, quality and environmental
aspects were examined on both water treatment systems. Capital costs of the UF system
was 5.6% higher while the operational cost was more than three times than the
conventional media/sand filtration water treatment system. Apparent advantages of the
UF system were exhibited through its production of consistent filtrate turbidity of less
than 1 Nephelometric Turbidity Units (NTU) and non-hazardous sludge as by-products.
The sludge from the conventional system contains 58 mg/L of Aluminium residual
originates from the Aluminium Chlorohydrate (ACH) utilized as coagulant in the
process. Considerable efforts were also made to elucidate the key issues of scaling-up
industrial-scale UF membrane water treatment system from data obtained through
laboratory and pilot-scale experiments. Results have indicated that all three UF systems
(laboratory-scale, pilot-scale and industrial-scale) have exhibited similar transmembrane
pressure (TMP) profiles pattern under same operational conditions.
In the second part of this research work, a pilot-scale UF system has been utilized to
gather data for the process modelling. A practical hybrid model which encompassed the
theoretical model of Darcy’s law and artificial neural networks (ANN) predictive model
has been developed. This hybrid model utilizes data from commonly available on-line monitoring analyzers and laboratory analysis data in a typical UF membrane water
treatment plant. Results have indicated close agreement between the simulated model
and experimental data on feed water with turbidity of 10 NTU and 20 NTU
respectively.
In the final part of this research work, an UF experimental system equipped with
supervisory control and data acquisition software has been commissioned to implement
various on-line control systems. The predictive model developed earlier has been
utilized together with ANN controllers to provide an alternative control system for the
dead-end constant flux UF process. Experiments were conducted to compare the results
from both the ANN and conventional set-points control systems. The ANN control
system has exhibited capability to reduce water losses to 4.9 % compared to the
conventional set-points control system of 9.6% while maintaining acceptable potential
membrane fouling propensity for low turbidity of feed water. Main objectives of this
research are to demonstrate the feasible utilization of UF membrane water treatment
systems and viable suggestions to improve its operations. The major contributions of
this research were highlighted through case studies evaluation of the UF membrane
water treatment systems, development of hybrid model for potential membrane fouling
parameters estimation and a proposed alternative ANN process control system to reduce
water losses. |
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