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

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• 作者：Dipankar Bhattacharyay ; Duygu Kocaefe ; Yasar Kocaefe…
• 关键词：Artificial neural network ; Carbon anode ; Aluminum ; Vanadium ; Iron ; CO2 reactivity
• 刊名：Neural Computing and Applications
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：28
• 期：3
• 页码：553-563
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Visi
• 出版者：Springer London
• ISSN：1433-3058
• 卷排序：28

文摘

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.