Dynamic soft sensor modeling method based on distributed Bayesian hidden Markov regression

被引：0
|
作者：
Shao W. [1 ]
Han W. [1 ]
Song W. [2 ]
Yang Y. [3 ]
Chen C. [2 ]
Zhao D. [1 ]
机构：
[1] College of New Energy, China University of Petroleum, Shandong, Qingdao
[2] SINOPEC Qingdao Refining & Chemical Co., Ltd., Shandong, Qingdao
[3] Technical Detection Center, Shengli Oil Field of SINOPEC, Shandong, Dongying
来源：
Huagong Xuebao/CIESC Journal | 2023年 / 74卷 / 06期
关键词：
Bayesian hidden Markov regression; chemical processes; distributed computing; dynamic modeling; soft sensors;
D O I：
10.11949/0438-1157.20230360
中图分类号：
学科分类号：
摘要：
Real-time prediction of key parameters in the chemical process by using soft sensing technology is of great significance for on-line monitoring, automatic control, and real-time optimization of production process. Therefore, a dynamic soft sensor modeling method based on hidden Markov model is proposed. Firstly, aiming at the problem of low computational efficiency caused by large data scales and insufficient utilization of data due to missing data, a predictive model based on distributed Bayesian hidden Markov regression is proposed. Then, a distributed training method that can obtain accurate posterior distribution is proposed for model training. Finally, the effectiveness of the proposed model is verified by the wax oil hydrogenation process. © 2023 Chemical Industry Press. All rights reserved.
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页码：2495 / 2502
页数：7
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共 31 条
  • [1] Qin M H, Zhu H Q, Li Y G, Et al., Soft-sensor method for ion concentration of electrochemical wastewater treatment based on STA-K-means clustering, CIESC Journal, 70, 9, pp. 3458-3464, (2019)
  • [2] Li Y G, Han W X, Shao W M, Et al., Virtual sensing for dynamic industrial process based on localized linear dynamical system models with time-delay optimization, ISA Transactions, 133, pp. 505-517, (2023)
  • [3] Wen C Y, Zhou P., Sparse robust modeling of effluent quality indices in wastewater treatment process, Acta Automatica Sinica, 48, 6, pp. 1469-1481, (2022)
  • [4] Ge Z Q, Song Z H, Ding S X, Et al., Data mining and analytics in the process industry: the role of machine learning, IEEE Access, 5, pp. 20590-20616, (2017)
  • [5] Zhou L, Shen C K, Wu C, Et al., Deep fusion feature extraction network and its application in chemical process soft sensing, CIESC Journal, 73, 7, pp. 3156-3165, (2022)
  • [6] Sun K, Sui L, Zhang F F, Et al., Dynamic soft sensor algorithm based on nonnegative garrote and long short-term memory neural network, Control Theory & Applications, 40, 1, pp. 83-93, (2023)
  • [7] Khine K L, Nyunt T S., Predictive geospatial analytics using principal component regression, International Journal of Electrical and Computer Engineering (IJECE), 10, 3, pp. 2651-2658, (2020)
  • [8] Liu J X, Sun D S, Chen J H., Comparative study on wavelet functional partial least squares soft sensor for complex batch processes, Chemical Engineering Science, 254, (2022)
  • [9] Shao W M, Ge Z Q, Song Z H., Soft-sensor development for processes with multiple operating modes based on semisupervised Gaussian mixture regression, IEEE Transactions on Control Systems Technology, 27, 5, pp. 2169-2181, (2019)
  • [10] Shao W M, Ge Z Q, Song Z H, Et al., Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines, Control Engineering Practice, 91, (2019)
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