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Quality Prediction of Strip Steel Based on Windows-Mean MPLS

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Abstract

A windows-mean multi-way partial-least squares (MPLS) method was proposed to build the strip steel quality prediction model for continuous annealing process. The proposed method can deal with the problem of large time lag in hardness measurement of the strip steel and the difficulties in building mechanism models. Based on the average trajectories of the process variable information, the proposed method was used to realize the strip steel quality prediction. Comparing with the traditional MPLS, the proposed method can eliminate the abundant and redundant process information and reduce the number of the model input variables. Moreover, it can avoid the uneven-length modeling data problem of the traditional MPLS method. The simulation result demonstrates the performance and advantages of the proposed method for predicting the strip steel hardness.

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References

  1. XUAN Mei-can, XU Yao-huan, HAN **g-tao. Cold Rolling Production Technics of Wide Strip Steel in Bao Steel [M]. Harbin: Heilongliang Science and Technology Press, 1997 (in Chinese).

    Google Scholar 

  2. CHEN Shu-guang, LIU **, TIAN Bao-hong. Prediction of Annealing Hardness of C3602 Leaded Brass Based on Grey Theory [J]. Journal of Henan University of Science and Technology: Natural Science, 2005, 26(2): 8 (in Chinese).

    Google Scholar 

  3. LIU Bao, WU Gang, SONG Guang-ming. Forecast Model of Hardness Based on the BP Neural Network [J]. Heat Treatment Technology and Equipment, 2006, 27(3): 37 (in Chinese).

    Google Scholar 

  4. MacGregor J F, Jaeckle C, Kiparissides C. Process Monitoring and Diagnosis by Multiblock PLS Methods [J]. AIChE Journal, 1994, 40(5): 826.

    Article  Google Scholar 

  5. Piovoso M J, Kosanovich K A. Application of Multivariate Statistical Methods to Process Monitoring and Controller Design [J]. International Journal of Control, 1994, 59(3): 743.

    Article  MathSciNet  Google Scholar 

  6. Nomikos P, MacGregor J F. Multivariate SPC Charts for Monitoring Batch Processes [J]. Technometrics, 1995, 37(1): 41.

    Article  Google Scholar 

  7. ZHAO Chun-hui, WANG Fu-li, MAO Zhi-zhong, et al. Quality Prediction Based on Phase-Specific Average Trajectory for Batch Processes [J]. AIChE Journal, 2008, 54(3): 693.

    Article  Google Scholar 

  8. XIAO Dong, PAN **ao-li, MAO Zhi-zhong, et al. Step Mean Value Staged MPLS Based Predictive Model for Shell Quality [J]. Control and Decision, 2008, 23(4): 431 (in Chinese).

    Google Scholar 

  9. Nomikos P, MacGregor J F. Multi-Way Partial Least Squares in Monitoring Batch Process [J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 97.

    Article  Google Scholar 

  10. ZHAO Chun-hui, WANG Fu-li, MAO Zhi-zhong, et al. Improved Batch Process Monitoring and Quality Prediction Based on Multi-Phase Statistical Analysis [J]. Industrial and Engineering Chemistry Research, 2008, 47(3): 835.

    Article  Google Scholar 

  11. Ferrer A, Aguado D, Vidal P S, et al. PLS: A Versatile Tool for Industrial Process Improvement and Optimization [J]. Applied Stochastic Models in Business and Industry, 2008, 24 (6): 551.

    Article  MathSciNet  Google Scholar 

  12. Helland I S. On the Structure of Partial Least Squares Regression [J]. Communications in statistics, 1988, 17(2): 581.

    Article  MathSciNet  Google Scholar 

  13. Kourti T. Multivariate Dynamic Data Modeling for Analysis and Statistical Process Control of Batch Processes, Start-Ups and Grade Transitions [J]. Journal of Chemometrics, 2003, 17(1): 93.

    Article  Google Scholar 

  14. Lu N, Gao F, Yang Y, et al. PCA-Based Modeling and On-Line Monitoring Strategy for Uneven-Length Batch Processes [J]. Industrial and Engineering Chemistry Research, 2004, 43(13): 3343.

    Article  Google Scholar 

  15. WANG hui-wen. Partial Least-Squares Regression-Linear and Nonlinear Methods [M]. Bei**g: National Defence Industry Press, 2006 (in Chinese).

    Google Scholar 

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Correspondence to Yu-qing Chang.

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Foundation Item: Item Sponsored by National Natural Science Foundation of China (60774068) ; National Basic Research Program of China (2009CB320601)

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Chang, Yq., Wang, Jf., Tan, S. et al. Quality Prediction of Strip Steel Based on Windows-Mean MPLS. J. Iron Steel Res. Int. 17, 28–33 (2010). https://doi.org/10.1016/S1006-706X(10)60152-5

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