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Modelling of the batch sucrose crystallization kinetics using artificial neural networks / K. Vasanth Kumar in Industrial & engineering chemistry research, Vol. 47 n°14 (Juillet 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°14 (Juillet 2008) . - p. 4917–4923 Titre : Modelling of the batch sucrose crystallization kinetics using artificial neural networks : comparison with conventional regression analysis Type de document : texte imprimé Auteurs : K. Vasanth Kumar, Auteur ; P. Martins, Auteur ; F. Rocha, Auteur Année de publication : 2008 Article en page(s) : p. 4917–4923 Langues : Anglais (eng) Mots-clés : Sucrose;  Artificial  neural  network;  Correlation Résumé : A three-layer feed-forward artificial neural network (ANN) was constructed and tested to analyze the crystal growth rate of sucrose under different operating conditions. The operating variables studied were used as inputs to predict the corresponding crystal growth rate. The operating variables studied include the supersaturation, temperature, agitation speed, and seed crystal diameter. The constructed ANN was determined to be precise in modeling the crystal growth rate for any operating conditions. The constructed network was also found to be precise in predicting the crystal growth rate for the new input data, which are kept unaware of the trained neural network, showing its applicability to determine the growth rate for any operating conditions of interest. The ANN-predicted crystal growth rates were compared to those from the conventional nonlinear regression analysis. The ANN was observed to be more accurate in predicting the crystal growth rate, irrespective of the operating conditions studied. The correlation coefficients between the experimentally determined crystal growth rate and the crystal growth rates determined by the ANN and multiple nonlinear regression (MNLR) were determined to be 0.999 and 0.748, respectively. The correlation coefficient between the experimentally determined crystal growth rates and the crystal growth rates determined by the ANN for new inputs was observed to be >0.98. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie701706v [article] Modelling of the batch sucrose crystallization kinetics using artificial neural networks : comparison with conventional regression analysis [texte imprimé] / K. Vasanth Kumar, Auteur ; P. Martins, Auteur ; F. Rocha, Auteur . - 2008 . - p. 4917–4923. Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°14 (Juillet 2008) . - p. 4917–4923 Mots-clés : Sucrose;  Artificial  neural  network;  Correlation Résumé : A three-layer feed-forward artificial neural network (ANN) was constructed and tested to analyze the crystal growth rate of sucrose under different operating conditions. The operating variables studied were used as inputs to predict the corresponding crystal growth rate. The operating variables studied include the supersaturation, temperature, agitation speed, and seed crystal diameter. The constructed ANN was determined to be precise in modeling the crystal growth rate for any operating conditions. The constructed network was also found to be precise in predicting the crystal growth rate for the new input data, which are kept unaware of the trained neural network, showing its applicability to determine the growth rate for any operating conditions of interest. The ANN-predicted crystal growth rates were compared to those from the conventional nonlinear regression analysis. The ANN was observed to be more accurate in predicting the crystal growth rate, irrespective of the operating conditions studied. The correlation coefficients between the experimentally determined crystal growth rate and the crystal growth rates determined by the ANN and multiple nonlinear regression (MNLR) were determined to be 0.999 and 0.748, respectively. The correlation coefficient between the experimentally determined crystal growth rates and the crystal growth rates determined by the ANN for new inputs was observed to be >0.98. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie701706v

Using an online image analysis technique to characterize sucrose crystal morphology during a crystallization run / A. Ferreira in Industrial & engineering chemistry research, Vol. 50 N° 11 (Juin 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 11 (Juin 2011) . - pp. 6990-7002 Titre : Using an online image analysis technique to characterize sucrose crystal morphology during a crystallization run Type de document : texte imprimé Auteurs : A. Ferreira, Auteur ; N. Faria, Auteur ; F. Rocha, Auteur Année de publication : 2011 Article en page(s) : pp. 6990-7002 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Crystallization  Crystal  morphology  Image  analysis Résumé : The morphological forms and habits of crystals and agglomeration are important properties on crystallization processes. Online techniques for realtime measurement of these properties are mandatory for a better comprehension of crystal growth phenomenon. The present paper presents and describes a new online method to determine the complexity level of a crystal or a population of crystals during a crystallization process. An image analysis technique is combined with discriminant factorial analysis leading to results that allow the computation of the complexity of crystals through the parameter agglomeration degree of crystals. With this methodology, it has been possible to distinguish online and automatically among three different classes of crystals according to their complexity. It further describes the application of such methodology on the study of CaCl2, D-fructose, and D-glucose influence on the crystallization of sucrose, namely, on crystal size, morphology, and complexity. The effect of supersaturation, growth rate, and impurity concentration on the type, amount, and complexity level of the agglomerates was determined at different temperatures. The combination of image analysis and kinetic results allowed to understand better the crystallization phenomena in the presence and absence of impurities. The image analysis results suggest the possible application of this tool for process control, optimizing, by this way, laboratory and industrial crystallizers. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24199918 [article] Using an online image analysis technique to characterize sucrose crystal morphology during a crystallization run [texte imprimé] / A. Ferreira, Auteur ; N. Faria, Auteur ; F. Rocha, Auteur . - 2011 . - pp. 6990-7002. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 11 (Juin 2011) . - pp. 6990-7002 Mots-clés : Crystallization  Crystal  morphology  Image  analysis Résumé : The morphological forms and habits of crystals and agglomeration are important properties on crystallization processes. Online techniques for realtime measurement of these properties are mandatory for a better comprehension of crystal growth phenomenon. The present paper presents and describes a new online method to determine the complexity level of a crystal or a population of crystals during a crystallization process. An image analysis technique is combined with discriminant factorial analysis leading to results that allow the computation of the complexity of crystals through the parameter agglomeration degree of crystals. With this methodology, it has been possible to distinguish online and automatically among three different classes of crystals according to their complexity. It further describes the application of such methodology on the study of CaCl2, D-fructose, and D-glucose influence on the crystallization of sucrose, namely, on crystal size, morphology, and complexity. The effect of supersaturation, growth rate, and impurity concentration on the type, amount, and complexity level of the agglomerates was determined at different temperatures. The combination of image analysis and kinetic results allowed to understand better the crystallization phenomena in the presence and absence of impurities. The image analysis results suggest the possible application of this tool for process control, optimizing, by this way, laboratory and industrial crystallizers. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24199918