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An Enhanced Method to Assess MPC Performance Based on Multi-Step Slow Feature Analysis

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  • Linyuan Shang

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China)

  • Yanjiang Wang

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China)

  • Xiaogang Deng

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China)

  • Yuping Cao

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China)

  • Ping Wang

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China)

  • Yuhong Wang

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China)

Abstract

Due to the wide application of model predictive control (MPC) in industrial processes, the assessment of MPC performance is essential to ensure product quality and improve energy efficiency. Recently, the slow feature analysis (SFA) algorithm has been successfully applied to assess the performance of MPC. However, the disadvantage of the traditional SFA-based predictable index is that it can only extract one-step predictable information in the monitored variables. In order to better mine the predictable information contained in the monitored variables with large lag, an enhanced method to assess MPC performance based on multi-step SFA (MSSFA) is proposed. Based on the relationship between the slowness of slow features (SFs) and data predictability, an MSSFA model SFA( τ ) is built through extending the temporal derivatives of the SFs from one step to multiple steps to extract multi-step predictable information in the monitored variables, which is used to construct a multi-step predictable index. Then, the predictable information in the SFs is further extracted for enhancing the multi-step predictable index to improve its sensitivity to performance changes. The effectiveness of the proposed method has been verified through two process simulation examples.

Suggested Citation

  • Linyuan Shang & Yanjiang Wang & Xiaogang Deng & Yuping Cao & Ping Wang & Yuhong Wang, 2019. "An Enhanced Method to Assess MPC Performance Based on Multi-Step Slow Feature Analysis," Energies, MDPI, vol. 12(19), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3799-:d:274218
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    References listed on IDEAS

    as
    1. Haoran Zhao & Sen Guo & Huiru Zhao, 2018. "Comprehensive Performance Assessment on Various Battery Energy Storage Systems," Energies, MDPI, vol. 11(10), pages 1-26, October.
    2. Alhussein Albarbar & Abdullah Arar, 2019. "Performance Assessment and Improvement of Central Receivers Used for Solar Thermal Plants," Energies, MDPI, vol. 12(16), pages 1-27, August.
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