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Comparison of linear multivariable, partial least square regression, and artificial neural network analyses to study the effect of different parameters on anode properties

Bhattacharyay Dipankar, Kocaefe Duygu, Kocaefe Yasar S. et Morais Brigitte. (2015). Comparison of linear multivariable, partial least square regression, and artificial neural network analyses to study the effect of different parameters on anode properties. Light Metals, p. 1129-1134.

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URL officielle: https://doi.org/10.1002/9781119093435.ch189

Résumé

Carbon anodes constitute a substantial part of the cost during the electrolytic production of aluminum. The industry tries to minimize the consumption of anodes by improving their quality. Therefore, a clear understanding of the impact of the quality of raw materials as well as process parameters on anode properties is important. The plants have a large collection of data, which is complex and difficult to analyze using conventional methods. In this article, linear multivariable (LMA), partial least square regression (PLS), and artificial neural network (ANN) analyses are presented and compared as tools to predict the influence of different parameters on anode properties. Published laboratory data have been processed using Matlab software to carry out the analyses. The results clearly show that ANN is the best tool for prediction purposes. Unlike other methods, ANN can handle nonlinear complex relations even if a well-defined relationship is not available.

Type de document:Article publié dans une revue avec comité d'évaluation
Pages:p. 1129-1134
Version évaluée par les pairs:Oui
Date:2015
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:artificial neural network, linear multivariable analysis, partial least-square analysis, carbon anodes, data analysis
Déposé le:02 mai 2019 16:49
Dernière modification:02 mai 2019 16:49
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