Neural Network model for the on-line monitoring of a crystallization process

This paper presents the results of the application of a recently developed technique, based on Neural Networks (NN), in the recognition of angular dis tribution patterns of light scattered by particles in suspension, for the p urpose 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 s ize distributions and concentrations. Then the model was used to monitor th e 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 pre cipitation processes.