Open AccessNeural network model for the on-line monitoring of a crystallization process
Author(s) -
Roberto Guardani,
Rosalina Sumie Onimaru,
Fernanda Carvalho de Abreu e Crespo
Publication year - 2001
Publication title -
brazilian journal of chemical engineering/brazilian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.313
H-Index - 52
eISSN - 1678-4383
pISSN - 0104-6632
DOI - 10.1590/s0104-66322001000300006
Subject(s) - supersaturation , crystallization , precipitation , artificial neural network , biological system , suspension (topology) , process (computing) , process engineering , materials science , computer science , chemical engineering , artificial intelligence , engineering , mathematics , physics , thermodynamics , meteorology , homotopy , biology , pure mathematics , operating system
This paper presents the results of the application of a recently developed technique, based on Neural Networks (NN), in the recognition of angular distribution patterns of light scattered by particles in suspension, for the purpose of estimating concentration and crystal size distribution (CSD) in a precipitation process based on the addition of antisolvent (a model system consisting of sodium chloride, water and ethanol). In the first step, in NN model was fitted, using particles with different size distributions and concentrations. Then the model was used to monitor the process, thus enabling a fast and reliable estimation of supersaturation and CSD. Such information, which is difficult to obtain by any other means, can be used in the study of fundamental aspects of crystallization and precipitation processes