Process operating performance assessment for magnesium melting process based on adaptive fusion of multi-source heterogeneous information

被引：0
|
作者：
Liu Y. [1 ]
Xiong Y. [1 ]
Chu F. [2 ]
Wang F. [1 ]
机构：
[1] College of Information Science & Engineering, Northeastern University, Shenyang
[2] School of Information and Control Engineering, University of Mining and Technology, Xuzhou
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2023年 / 44卷 / 09期
关键词：
assessment sub-models; attention mechanism; magnesium melting process; multi-source heterogeneous information; process operating performance assessment;
D O I：
10.19650/j.cnki.cjsi.J2311259
中图分类号：
学科分类号：
摘要：
In this article, a new operating performance assessment method based on adaptive fusion of multi-source heterogeneous information (AFMSHI) is proposed for magnesium melting process. First, the data pre-processing is performed for the multi-source heterogeneous information (MSHI) in the process of magnesium melting, and deep learning methods are used to formulate performance assessment sub-models based on different types of information. Secondly, to fully consider the impacts of MSHI on the assessment results under different melting states, the attention mechanism is used to establish an adaptive fusion network for the assessment results of each sub-model. Finally, the fused assessment results are input into a SoftMax classifier, and the magnesium melting process assessment model is formulated. The simulation results show that, comparing with the assessment model established by a single type of information or the existing deep learning MSHI assessment methods, the assessment accuracy of AFMSHI based on simulation platform data and actual production data reached 99. 5% and 98. 44%, respectively, which is higher than the compared methods by comprehensively considering the roles of MSHI. The effectiveness and the superiority of the proposed method are verified. © 2023 Science Press. All rights reserved.
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页码：320 / 335
页数：15
相关论文
共 31 条
  • [1] LI H R, WANG Y W, DENG J CH, Information fusion based abnormal condition levels recognition of smelting in fused magnesium furnace, Journal of Northeastern University Natural Science, 41, 2, pp. 153-157, (2020)
  • [2] BU K Q, LIU Y, WANG F L., Process operation performance assessment based on multisource heterogeneous information with semisupervised transfer generative adversarial network for electrofused magnesium furnace, IEEE Transactions on Industrial Informatics, 19, 5, pp. 6785-6793, (2023)
  • [3] WU ZH W, CHAI T Y, WU Y J., A hybrid prediction model of energy consumption per ton for fused magnesia, Acta Automatica Sinica, 39, 12, pp. 2002-2011, (2013)
  • [4] WU ZH W, FANG ZH, CHAI T Y, Et al., Research on special embedded controller and its control method for fused magnesium furnace, Chinese Journal of Scientific Instrument, 33, 6, pp. 1261-1267, (2012)
  • [5] WU Z W, LIU T F, JIANG Z P, Et al., Nonlinear control tools for fused magnesium furnaces: Design and implementation [ J ], IEEE Transactions on Industrial Electronics, 65, 9, pp. 7248-7257, (2018)
  • [6] DU W Y, ZHOU W., An intelligent fault diagnosis architecture for electrical fused magnesia furnace using sound spectrum submanifold analysis [ J ], IEEE Transactions on Instrumentation & Measurement, 67, 9, pp. 2014-2023, (2018)
  • [7] LI M Y, ZHANG J W, CHAI T Y., Deep learning assisted online multi-step demand forecasting of fused magnesia smelting processes, Proceedings of the 4th International Conference on Industrial Artificial Intelligence (IAI) in Shenyang of China, pp. 1-8, (2022)
  • [8] YUAN P, SUN Y F, LI H., Abnormal condition identification modeling method based on Bayesian network parameters transfer learning for the electro-fused magnesia smelting process, IEEE Access, 7, pp. 149764-149775, (2019)
  • [9] LI H, WANG F L, LI H R., Abnormal condition identification and self-healing control scheme for the electro-fused magnesia smelting process, Acta Automatica Sinica, 46, 7, pp. 1411-1419, (2020)
  • [10] CHANG Y Q, NIU D P, ZOU X Y., Canonical correlation analysis based process operating performance assessment and its application to a gold hydrometallurgy process [ C], Proceedings of the Chinese Automation Congress (CAC) in Jinan of China, pp. 1584-1587, (2017)
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