A Multi-correlated Time-delay Estimation Method in the Blast Furnace Ironmaking Process Based on Time-series Correlation Matrix

被引：0
|
作者：
Jiang K. [1 ]
Jiang Z.-H. [1 ,2 ]
Xie Y.-F. [1 ]
Pan D. [1 ]
Gui W.-H. [1 ]
机构：
[1] School of Automation, Central South University, Changsha
[2] Peng Cheng Laboratory, Shenzhen
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Blast furnace; double-scale collaborative search strategy; particle swarm algorithm; time-series correlation matrix; variable time-delay estimation;
D O I：
10.16383/j.aas.c220091
中图分类号：
学科分类号：
摘要：
Variable time-delay is brought by transportation and reaction time of materials and the different distributions of smelting units in spatial and temporal, which affects the accuracy and the actual causal relationship of collected data. Therefore, the estimation of variable time-delay information and the reconstruction of data in time series effectively are the key points of subsequent process modeling, optimal control, and performance evaluation. Considering the multiple correlations characteristic of variable time-delay, an estimation method based on a time-series correlation matrix is proposed. First, the time-series correlation matrix is reconstructed in time and space according to the time-delay parameters, where grey correlation analysis is used to quantify the multi-correlated correlation. Then, considering the number of time-series correlation matrices and the time complexity of the exhaustive method, a dynamic multi-swarm particle swarm optimization based on a double-scale collaborative search strategy is proposed to search for the optimal time-delay parameters. The proposed optimization algorithm can consider both global exploration and local exploitation ability and escape local optimal solutions. Finally, the feasibility and effectiveness of the proposed time-delay estimation method are verified by a numerical simulation and an industrial experiment of 2# blast furnace in a steel plant. In addition, a significant improvement can be observed in the performance of the soft-sensor model of silicon content with the reconstruction of data in time series. © 2023 Science Press. All rights reserved.
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页码：329 / 342
页数：13
相关论文
共 25 条
  • [1] Zhou H, Zhang H F, Yang C J., Hybrid model based intelligent optimization of ironmaking process, IEEE Transaction on Industrial Electronics, 67, 3, pp. 2469-2479, (2020)
  • [2] Jiang Zhao-Hui, Xu Chuang, Gui Wei-Hua, Jiang Ke, Prediction method of hot metal silicon content in blast furnace based on optimal smelting condition migration, Acta Automatica Sinica, 48, 1, pp. 207-219, (2021)
  • [3] Li Wen-Peng, Zhou Ping, Blast furnace hot metal quality robust regularization random weight neural network modeling, Acta Automatica Sinica, 46, 4, pp. 721-733, (2020)
  • [4] Chen S H, Gao C H., Linear priors mined and integrated for transparency of blast furnace black-box SVM model, IEEE Transactions on Industrial Informatics, 16, 6, pp. 3862-3870, (2020)
  • [5] Li J P, Hua C C, Yang Y N, Zhang L M, Guan X P., Output space transfer based multi-input multi-output Takagi –Sugeno fuzzy modeling for estimation of molten iron quality in blast furnace, Knowledge-Based Systems, 219, (2021)
  • [6] Saxen H, Gao C H, Gao Z W., Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace: A review, IEEE Transactions on Industrial Informatics, 9, 4, pp. 2213-2225, (2013)
  • [7] Tang X L, Zhuang L, Jiang C J., Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization, Expert Systems With Applications, 36, 9, pp. 11853-11857, (2009)
  • [8] Zhou Ping, Zhang Li, Li Wen-Peng, Dai Peng, Chai Tian-You, Modeling of blast furnace multi-element molten iron quality with random weight neural network based on self-encoding and PCA, Acta Automatica Sinica, 44, 10, pp. 1799-1811, (2018)
  • [9] Zhou P, Li W P, Wang H, Li M J, Chai T Y., Robust online sequential RVFLNs for data modeling of dynamic time-varying systems with application of an ironmaking blast furnace, IEEE Transactions on Cybernetics, 50, 11, pp. 4783-4795, (2019)
  • [10] Pan D, Jiang Z H, Chen Z P, Gui W H, Xie Y F, Yang C H., Temperature measurement and compensation method of blast furnace molten iron based on infrared computer vision, IEEE Transactions on Instrumentation and Measurement, 68, 10, pp. 3576-3588, (2018)
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