Accounts of experiences in the application of artificial neural networks in chemical engineering / David M. Himmelblau in Industrial & engineering chemistry research, Vol. 47 n°16 (Août 2008)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 47 n°16 (Août 2008) . - p. 5782–5796 Titre : Accounts of experiences in the application of artificial neural networks in chemical engineering Type de document : texte imprimé Auteurs : David M. Himmelblau, Auteur Année de publication : 2008 Article en page(s) : p. 5782–5796 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Artificial neural networks Chemical engineering Résumé : Considerable literature describing the use of artificial neural networks (ANNs) has evolved for a diverse range of applications such as fitting experimental data, machine diagnostics, pattern recognition, quality control, signal processing, process modeling, and process control, all topics of interest to chemists and chemical engineers. Because ANNs are nets of simple functions, they can provide satisfactory empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes the characteristics of ANNs including their advantages and disadvantages, focuses on two types of neural networks that have proved in our experience to be effective in practical applications, and presents short examples of four specific applications. In the competitive field of modeling, ANNs have secured a niche that now, after two decades, seems secure. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800076s [article] Accounts of experiences in the application of artificial neural networks in chemical engineering [texte imprimé] / David M. Himmelblau, Auteur . - 2008 . - p. 5782–5796. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°16 (Août 2008) . - p. 5782–5796 Mots-clés : Artificial neural networks Chemical engineering Résumé : Considerable literature describing the use of artificial neural networks (ANNs) has evolved for a diverse range of applications such as fitting experimental data, machine diagnostics, pattern recognition, quality control, signal processing, process modeling, and process control, all topics of interest to chemists and chemical engineers. Because ANNs are nets of simple functions, they can provide satisfactory empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes the characteristics of ANNs including their advantages and disadvantages, focuses on two types of neural networks that have proved in our experience to be effective in practical applications, and presents short examples of four specific applications. In the competitive field of modeling, ANNs have secured a niche that now, after two decades, seems secure. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800076s

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