Optimization by genetic algorithm of an ice crystallization for a new hybrid desalination process

This work concerns optimal design for a new developed freeze seawater desalination pretreatment process by multi-objective genetic algorithm used by the GA solver of MALTAB's Global optimization toolbox. Genetic algorithms proved themselves as a powerful and robust tool for multiobjective optimization. This study is based on the use of the model developed and simulated for the ice crystallization process within a Scraped Surface Heat Exchanger (SSHE) in our previous works [1], [2]. The objectives function considered in this work are related to both of process productivity and energy consumption. Multi-objective optimization methodology allows taking these two objectives directly and provide search of optimal solution with respect to all of them. The obtained results are in agreement with the analysis of response surface trends in term of ice volume fraction previously investigated [3]. Thus, this allows determining design operating conditions resulting in the best freeze seawater desalination pretreatment process performance. So the optimal design operating point of the new hybrid seawater desalination system combining freezing and reverse osmosis is obtained.