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Crystallization process optimization using artificial neural networks

✍ Scribed by Prof. Dr. Ir. Alexandru Woinaroschy; Lect. Ir. Raluca Isopescu; Prof. Dr. Ir. Laurentiu Filipescu

Publisher
John Wiley and Sons
Year
1994
Tongue
English
Weight
280 KB
Volume
17
Category
Article
ISSN
0930-7516

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✦ Synopsis

This paper presents a new procedure for optimization of continuous mixed suspensionmixed product removal (MSMPR) crystallizing systems. Owing to the difficulties of theoretical modelling, simulation of the MSMPR crystallization process is based on the use of artificial neural networks (ANN). The optimization criterion is a compound objective function corresponding to an intended mean crystal size dimension and a minimal dispersion. The presence of multiple local minima has called for investigation by several optimization techniques. Ultimately, Luus' and Jaakola's random adaptive method proved to be most effective. The results obtained lend support to the general procedure proposed.

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