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Multiobjective optimization of semibatch reactive crystallization processes
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DOI：10.1002/aic.11142.png)
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Abstract
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The determination of the optimal feed profiles for a reactive crystallizer is an important dynamic optimization problem, as the feed profiles offer a significant control over the quality of the product crystals. Crystallization processes typically have multiple performance objectives and optimization using different objective functions leads to significantly different optimal operating conditions. Therefore, a multiobjective approach is more appropriate for optimization of these processes. The potential for multiobjective optimization approach is demonstrated for semibatch reactive crystallization processes. The multiobjective approach usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. The Pareto-optimal solutions can help the designer visualize the trade-offs between different objectives, and select an appropriate operating condition for the process. A well known multiobjective evolutionary algorithm, the elitist nondominated sorting genetic algorithm, has been adapted to illustrate the potential for the multiobjective optimization approach. (c) 2007 American Institute of Chemical Engineers.
Keywords:
semibatch
reactive crystallization
precipitation
dynamic simulation
multiobjective
optimization
genetic algorithm
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