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Application of the artificial neural network (ANN) in predicting anode properties

Bhattacharyay Dipankar, Kocaefe Duygu, Kocaefe Yasar S., Morais Brigitte et Gagnon Marc. (2013). Application of the artificial neural network (ANN) in predicting anode properties. Light Metals, p. 1189-1194.

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Carbon anodes are a major part of the cost of primary aluminum production. The focus of the industry is to minimize the consumption of anodes by improving their quality. Therefore, the determination of the impact of quality of raw materials as well as process parameters on baked anode properties is important. The plants have a large data base which, upon appropriate analysis, could help maintain or improve the anode quality. However, it is complex and difficult to analyze these data using conventional methods. The artificial neural network (ANN) is a mathematical tool that can handle such complex data. In this work, Matlab software was used to develop a number of ANN models. Using published data, linear multi-variable analysis and ANN were applied to assess the advantages of custom multilayered feed-forward ANN. Results are presented which show a number of industrial applications.

Type de document:Article publié dans une revue avec comité d'évaluation
Pages:p. 1189-1194
Version évaluée par les pairs:Oui
Sujets:Sciences naturelles et génie > Génie > Génie des matériaux et génie métallurgique
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Mots-clés:feed-forward, back-propagation, regression, linear multi-variable analysis, artificial neural network
Déposé le:06 mai 2019 19:36
Dernière modification:06 mai 2019 19:36
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