Neural network simulation for non-MSMPR crystallization

Z. Sha*, M. Louhi-Kultanen, S. Palosaari

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

A neural network model has been developed for the simulation of steady state 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 have been chosen for the simulation of different chemicals. The particle size distribution of the product is expressed by the Rosin-Rammler equation. Different operating modes and deviations in crystal size distribution caused by the suspension being imperfectly mixed are presented by different values of modified Rosin-Rammler number. The ranges of variables in the neural network have been chosen based on data for industrial crystallizers. The dominant size of particle, and the productivity of the crystallizer can be predicted with input information. Thus, this neural network can be used for most chemicals and for different kinds of operating conditions. The results predicted with the neural network have been verified by solving the fundamental equations and by comparison with experimental data.

Original languageEnglish
Pages (from-to)101-107
Number of pages7
JournalChemical Engineering Journal
Volume81
Issue number1-3
DOIs
Publication statusPublished - 1 Jan 2001
MoE publication typeA1 Journal article-refereed

Keywords

  • Crystallization
  • Design
  • Neural network
  • Simulation

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