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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