LiveZilla Live Chat Software

Constellation, le dépôt institutionnel de l'Université du Québec à Chicoutimi

An artificial neural network model for predicting the CO2 reactivity of carbon anodes used in the primary aluminum production

Bhattacharyay Dipankar, Kocaefe Duygu, Kocaefe Yasar S. et Morais Brigitte. (2017). An artificial neural network model for predicting the CO2 reactivity of carbon anodes used in the primary aluminum production. Neural Computing and Applications, 28, (3), p. 553-563.

[img] Doc - Version acceptée
411kB
[img]
Prévisualisation
PDF - Version acceptée
609kB

URL officielle: http://dx.doi.org/doi:10.1007/s00521-015-2093-7

Résumé

Carbon anode is one of the key components for the electrolytic production of aluminum. It is mainly composed of calcined petroleum coke, coal tar pitch, and recycled carbon materials. The impurities in the raw materials, which are mainly by-products of different industries, influence significantly the quality of anodes. Usually, no well-known mathematical relationship exists between the various physical and chemical properties of raw materials and the final anode properties. In such situations, the artificial neural network (ANN) methods can serve as a useful tool to predict anode properties. In this study, published data have been used to show the proficiency of different artificial neural networks using the MATLAB software. The average error between the predicted and experimental values is around 6 %. The artificial neural network was also used to identify the effect of impurities such as, vanadium, iron, sodium, and sulfur on the CO2 reactivity of anodes. ANN also showed the effect of pitch percentage and coke porosity on the CO2 reactivity of anodes. The effect of CO2 and air reactivities of coke on the CO2 reactivity of anode was also studied. The predictions were found to be in good agreement with the results of other studies in the literature.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:28
Numéro:3
Pages:p. 553-563
Version évaluée par les pairs:Oui
Date:2017
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, carbon anode, aluminum, vanadium, iron, CO2 reactivity
Déposé le:24 avr. 2019 00:05
Dernière modification:24 avr. 2019 00:05
Afficher les statistiques de telechargements

Éditer le document (administrateurs uniquement)

Creative Commons LicenseSauf indication contraire, les documents archivés dans Constellation sont rendus disponibles selon les termes de la licence Creative Commons "Paternité, pas d'utilisation commerciale, pas de modification" 2.5 Canada.

Bibliothèque Paul-Émile-Boulet, UQAC
555, boulevard de l'Université
Chicoutimi (Québec)  CANADA G7H 2B1
418 545-5011, poste 5630