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A rapid performance diagnosis of seawater reverse osmosis membranes: simulation approach

Authors
Lee, Young GeunKim, Do YeonKim, Yu ChangLee, Yun SeokJung, Da HeePark, MinkyuPark, Sang-JinLee, SanghoYang, Dae RyookKim, Joon Ha
Issue Date
3월-2010
Publisher
DESALINATION PUBL
Keywords
Reverse osmosis membrane; Membrane resistance model; Temperature correction factor; Pressure correction factor
Citation
DESALINATION AND WATER TREATMENT, v.15, no.1-3, pp.11 - 19
Indexed
SCIE
SCOPUS
Journal Title
DESALINATION AND WATER TREATMENT
Volume
15
Number
1-3
Start Page
11
End Page
19
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/116852
DOI
10.5004/dwt.2010.1661
ISSN
1944-3994
Abstract
Recovery and salt rejection rate, which are directly related to the membrane properties and operating conditions (i.e., feed flow rate, pressure, temperature, and TDS concentration), are two main indicators for evaluating the performance of a reverse osmosis (RO) membrane process. A simple and rapid test with minimum data is inevitably requested to diagnose membrane performance at a certain operating conditions, because experiments at all operating conditions are inefficient and consumable work in the view of cost as well as man-hour. In this study, permeate flow rate and TDS concentration of three kinds of commercial RO membranes were carefully examined under various operating conditions [i.e., feed pressure (45-65 kgf/cm(2)) and temperature (5-30 degrees C)]. Based on the experimental data, membrane resistance models including temperature and pressure correction factors were developed for the rapid diagnosis of SWRO membrane performances. As a result, the models developed in this study have good agreements between observed and simulated data. Based on the procedure in this study, the performance of any type of RO membranes can be rapidly examined by simple model parameter inputs. Furthermore, the developed diagnosis tool for performance test of SWRO membranes can be practically applied to build database of membrane performance for designing the SWRO process with minimum data as well as to reduce the cost and effort for data acquisition.
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