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Discrimination between Single Crystals and Agglomerates during the Crystallization Process
Author(s) -
Heisel Stefan,
Rolfes Mareike,
Wohlgemuth Kerstin
Publication year - 2018
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.201700651
Subject(s) - agglomerate , crystallization , materials science , adipic acid , particle (ecology) , crystal (programming language) , biological system , particle size , artificial neural network , process (computing) , particle size distribution , process engineering , chemical engineering , computer science , artificial intelligence , composite material , engineering , operating system , oceanography , biology , programming language , geology
A setup for online measurement of crystallization processes was developed, where particle images were taken and in combination with artificial neural networks (ANNs) were used to convert the particle size distribution into populations of single crystals and agglomerates. For generation of an ANN that discriminates all particle sizes equally well, the training set composition was investigated while proportional similarity was applied for image descriptor selection. Adipic acid/water served as a model system. The effect of the measurement setup on experiments performed for normal cooling crystallization was evaluated. It was found that the major challenge of measuring crystal agglomerates lies in superimposing aggregates that falsify the online measurement results, as offline measurements demonstrated.