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Crystallization process optimization using artificial neural networks
Author(s) -
Woinaroschy Alexandru,
Isopescu Raluca,
Filipescu Laurentiu
Publication year - 1994
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.270170408
Subject(s) - maxima and minima , artificial neural network , process (computing) , dimension (graph theory) , crystallization , process optimization , mathematical optimization , computer science , dispersion (optics) , crystal structure prediction , mathematics , artificial intelligence , engineering , crystal structure , chemistry , physics , mathematical analysis , optics , chemical engineering , pure mathematics , environmental engineering , crystallography , operating system
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.