Optimization coupling RO desalination unit to renewable energy by genetic algorithms

Renewable energy sources (RES) for powering desalination processes is a promising option especially in remote and arid regions where the use of conventional energy is costly or unavailable. Reverse osmosis (RO) is one of the most suitable desalination processes to be coupled with different RES such...

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Bibliographic Details
Published in:Desalination and water treatment 2013-02, Vol.51 (7-9), p.1416-1428
Main Authors: Ben M’Barek, T., Bourouni, K., Ben Mohamed, K.B.
Format: Article
Language:English
Subjects:
Applied sciences
Batteries
Cost engineering
Desalination
Drinking water and swimming-pool water. Desalination
Exact sciences and technology
Genetic algorithms
Joining
Mathematical models
Modeling
Optimization
Pollution
Renewable energies
Renewable energy
Reverse osmosis
Water treatment and pollution
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Summary:Renewable energy sources (RES) for powering desalination processes is a promising option especially in remote and arid regions where the use of conventional energy is costly or unavailable. Reverse osmosis (RO) is one of the most suitable desalination processes to be coupled with different RES such as solar and wind. If RES/RO systems are optimally designed, some combinations can be cost effective and reliable. However, the design of such systems is complex because of uncertain renewable energy supplies, load demands, and the non-linear characteristics of some components. In such system, different scenarios can be suggested; i.e. combinations of Photovoltaic (PV) panels, type and number of batteries, type and number of turbines, etc. Therefore, it is difficult to determine the optimal configuration with classical techniques. The development of a tool to integrate all parameters involved and compare between the possible scenarios is very important. This paper presents a new model based on the genetic algorithms allowing for coupling small RO unit to RES. A particular interest is focused on the hybrid systems (PV/WIND/Batteries/RO). The objective function to minimize corresponds to the total water cost (capital cost plus operational costs). The feasible solutions (individuals in each generation) are obtained through simulations carried along a complete year.
ISSN:1944-3986
1944-3994
1944-3986
DOI:10.1080/19443994.2012.714855