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Design Optimization of Reverse Osmosis Water Desalination Systems via Genetic Algorithms

[+] Author Affiliations
Karim Hamza, Kazuhiro Saitou

University of Michigan, Ann Arbor, MI

Mohammed Shalaby

General Electric-Global Research, Niskayuna, NY

Ashraf O. Nassef

American University in Cairo, Cairo, Egypt

Mohamed F. Aly

Cairo University, Cairo, Egypt

Paper No. DETC2010-28489, pp. 655-664; 10 pages
  • ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 36th Design Automation Conference, Parts A and B
  • Montreal, Quebec, Canada, August 15–18, 2010
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4409-0 | eISBN: 978-0-7918-3881-5
  • Copyright © 2010 by ASME


This paper explores the application of genetic algorithms (GA) for optimal design of reverse osmosis (RO) water desalination systems. While RO desalination is among the most cost and energy efficient methods for water desalination, optimal design of such systems is rarely an easy task. In these systems, salty water is made to flow at high pressure through vessels that contain semi-permeable membrane modules. The membranes can allow water to flow through, but prohibit the passage of salt ions. When the pressure is sufficiently high, water molecules will flow through the membranes leaving the salt ions behind and are collected in a fresh water stream. Typical system design variables for RO systems include the number and layout of the vessels and membrane modules, as well as the operating pressure and flow rate. This paper explores models for single and two-stage RO pressure vessel configurations. The number and layout of the vessels and membrane modules are regarded as discrete variables, while the operating pressures and flow rate are regarded as continuous variables. GA is applied to optimize the models for minimum overall cost of unit produced fresh water. Case studies are considered for four different water salinity concentration levels. In each of the studies, three different types of crossover are explored in the GA. While all the studied crossover types yielded satisfactory results, the crossover types that attempt to exploit design variable continuity performed slightly better, even for the discrete variables of this problem.

Copyright © 2010 by ASME



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