Neural network simulation for non-MSMPR crystallization

A neural network model has been developed for the simulation of steady stat e industrial crystallizers where, in general, the crystal size distribution cannot be described by simple mass and energy balances, i.e. they are non- MSMPR crystallizers. The model is based on fundamental equations of steady state suspension crystallization. The parameters in the nucleation rate hav e been chosen for the simulation of different chemicals. The particle size distribution of the product is expressed by the Rosin-Rammler equation. Dif ferent operating modes and deviations in crystal size distribution caused b y the suspension being imperfectly mixed are presented by different values of modified Rosin-Rammler number. The ranges of variables in the neural net work have been chosen based on data for industrial crystallizers. The domin ant size of particle, and the productivity of the crystallizer can be predi cted with input information. Thus, this neural network can be used for most chemicals and for different kinds of operating conditions. The results pre dicted with the neural network have been verified by solving the fundamenta l equations and by comparison with experimental data. (C) 2001 Elsevier Sci ence B.V. All rights reserved.