Yes / Reverse osmosis (RO) has found extensive applications in industry as an efficient separation process in comparison with thermal process. In this study, a one-dimensional distributed model based on a wastewater treatment spiral-wound RO system is developed to simulate the transport phenomena of solute and water through the membrane and describe the variation of operating parameters along the x-axis of membrane. The distributed model is tested against experimental data available in the literature derived from a chlorophenol rejection system implemented on a pilot-scale cross-flow RO filtration system with an individual spiral-wound membrane at different operating conditions. The proposed model is then used to carry out an optimisation study using a genetic algorithm (GA). The GA is developed to solve a formulated optimisation problem involving two objective functions of RO wastewater system performance. The model code is written in MATLAB, and the optimisation problem is solved using an optimisation platform written in C++. The objective function is to maximize the solute rejection at different cases of feed concentration and minimize the operating pressure to improve economic aspects. The operating feed flow rate, pressure and temperature are considered as decision variables. The optimisation problem is subjected to a number of upper and lower limits of decision variables, as recommended by the module’s manufacturer, and the constraint of the pressure loss along the membrane length to be within the allowable value. The algorithm developed has yielded a low optimisation execution time and resulted in improved unit performance based on a set of optimal operating conditions.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/11341 |
| Date | 19 January 2017 |
| Creators | Al-Obaidi, Mudhar A.A.R., Li, Jian-Ping, Kara-Zaitri, Chakib, Mujtaba, Iqbal M. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted Manuscript |
| Rights | © 2017 Elsevier B. V. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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