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

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Abstract

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.

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Acknowledgments

The technical and financial support of Aluminerie Alouette Inc. as well as the financial support of the National Science and Engineering Research Council of Canada (NSERC), Développement économique Sept-Îles, the University of Québec at Chicoutimi (UQAC), and the Foundation of the University of Québec at Chicoutimi (FUQAC) are greatly appreciated.

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Authors and Affiliations

  1. UQAC/AAI Research Chair on Carbon and REGAL Aluminum Research Center, Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC, Canada

    Dipankar Bhattacharyay, Duygu Kocaefe & Yasar Kocaefe

  2. Aluminerie Alouette Inc., Sept-Îles, QC, Canada

    Brigitte Morais

Authors
  1. Dipankar Bhattacharyay
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  2. Duygu Kocaefe
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  3. Yasar Kocaefe
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  4. Brigitte Morais
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Correspondence to Duygu Kocaefe.

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Bhattacharyay, D., Kocaefe, D., Kocaefe, Y. et al. An artificial neural network model for predicting the CO2 reactivity of carbon anodes used in the primary aluminum production. Neural Comput & Applic 28, 553–563 (2017). https://doi.org/10.1007/s00521-015-2093-7

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  • DOI: https://doi.org/10.1007/s00521-015-2093-7

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