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A Machine Learning Method for State Identification of Superheat Degree with Flame Interference

  • Shiwei Zhao
  • Yongfang Xie
  • Weichao YueEmail author
  • Xiaofang Chen
Conference paper
Part of the The Minerals, Metals & Materials Series book series (MMMS)

Abstract

The superheat degree in the process of aluminium electrolysis is an important indicator for judging the condition of the electrolysis cell. In the actual production process, the artificial observation of the fire hole is usually used for judgment and decision of cell condition. However, the decreasing number and frequent flow of experienced technicians make it difficult to guarantee the accuracy of this complex work. Although there exist some methods for state identification of superheat degree, they do not consider flame interference, resulting in decreasing of accuracy. In view of this fact, a method for state identification of superheat degree with flame interference is proposed, and the proposed method is compared with the existing method on 17 aluminium electrolysis cells. The vilification result shows that the proposed method has a better performance than the existing methods. Moreover, it reveals that the proposed method is feasible for identification with flame interference. In addition, it can provide suggestions for the technicians to judge the state of superheat degree.

Keywords

Superheat degree Electrolysis cell Fire hole Flame interference Features extraction 

Notes

Acknowledgements

Supported by National Natural Science Foundation of China (61773405, 61533020, 61751312, 61725306, 61621062), the teachers-students co-innovation project of Central South University (502390003).

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Copyright information

© The Minerals, Metals & Materials Society 2019

Authors and Affiliations

  • Shiwei Zhao
    • 1
  • Yongfang Xie
    • 1
  • Weichao Yue
    • 1
    Email author
  • Xiaofang Chen
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina

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