Multi-objective optimization of seeded batch crystallization processes |
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Authors: | Debasis Sarkar Arthur Jutan |
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Affiliation: | Department of Chemical & Biochemical Engineering, University of Western Ontario, London, Ont., Canada N6A 5B9 |
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Abstract: | The optimization of a batch cooling crystallizer has been traditionally sought with respect to the cooling profile and seeding characteristics that keep supersaturation at an optimum level throughout the operation. Crystallization processes typically have multiple performance objectives and optimization using different objective functions leads to significantly different optimal operating conditions. Thus different temperature profiles and seeding characteristics impose a complex interplay on the crystallizer behavior and there is a trade-off between the performance objectives. Therefore, a multi-objective approach should be adopted for optimization of a batch crystallizer for best process operation. This study presents the solution of various optimal control problems for a seeded batch crystallizer within a multi-objective framework. A well known multi-objective evolutionary algorithm, the elitist Nondominated Sorting Genetic Algorithm, has been adapted here to illustrate the potential for the multi-objective optimization approach. |
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Keywords: | Batch Crystallization Population balance model Dynamic simulation Multi-objective optimization Genetic algorithm |
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