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Diagnosis Method for the Heat Balance State of an Aluminum Reduction Cell Based on Bayesian Network

by Jiaming Zhu * and Jie Li
School of Metallurgy and Environment, Central South University, Changsha 410012, China
*
Author to whom correspondence should be addressed.
Metals 2020, 10(5), 604; https://doi.org/10.3390/met10050604
Received: 29 March 2020 / Revised: 25 April 2020 / Accepted: 5 May 2020 / Published: 7 May 2020
The superheat of an electrolyte is an important indicator of the heat balance state of aluminum reduction cells. In industrial practice, it costs too much to accurately measure the superheat in every cell every day. A common alternative is to calculate the superheat based on additive concentrations in the electrolyte, which has problems of high error and long delay. In this paper, a method to diagnose the heat balance state of an aluminum reduction cell based on Bayesian network is presented, a Bayesian network structure and CPT (conditional probability distribution) were built, and the continuous diagnosis process is presented. This diagnosis method takes important symptoms and factors into account, taking advantage of more useful information instead of only calculated superheat. The application examples show that this method is effective in diagnosing the heat balance state for uncertain and incomplete superheat information. View Full-Text
Keywords: aluminum reduction cell; Bayesian network; heat balance state; superheat aluminum reduction cell; Bayesian network; heat balance state; superheat
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Zhu, J.; Li, J. Diagnosis Method for the Heat Balance State of an Aluminum Reduction Cell Based on Bayesian Network. Metals 2020, 10, 604.

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