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Causal Discovery Based on Observational Data and Process Knowledge in Industrial Processes

  • Liang Cao
    Liang Cao
    Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
    More by Liang Cao
  • Jianping Su
    Jianping Su
    Forest Products Biotechnology/Bioenergy Group, The University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
    More by Jianping Su
  • Yixiu Wang
    Yixiu Wang
    Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
    More by Yixiu Wang
  • Yankai Cao
    Yankai Cao
    Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
    More by Yankai Cao
  • Lim C. Siang
    Lim C. Siang
    Department of Process Control Engineering, Burnaby Refinery, Burnaby, British Columbia V5C 1L7, Canada
    More by Lim C. Siang
  • Jin Li
    Jin Li
    Department of Process Control Engineering, Burnaby Refinery, Burnaby, British Columbia V5C 1L7, Canada
    More by Jin Li
  • Jack Nicholas Saddler
    Jack Nicholas Saddler
    Forest Products Biotechnology/Bioenergy Group, The University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
  • , and 
  • Bhushan Gopaluni*
    Bhushan Gopaluni
    Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
    *Email: [email protected]
Cite this: Ind. Eng. Chem. Res. 2022, 61, 38, 14272–14283
Publication Date (Web):September 14, 2022
https://doi.org/10.1021/acs.iecr.2c01326
Copyright © 2022 American Chemical Society

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    Abstract

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    Causal discovery approaches are gaining popularity in industrial processes. Existing causal discovery algorithms can indeed find some important causal relationships from industrial data, but, at the same time, the algorithms may also give some incorrect causal relationships. In order to deal with this problem, we give four kinds of process knowledge definitions according to the special characteristics of complex industrial processes. Causal discovery algorithms will yield more accurate results and deeper insights if the process knowledge is properly addressed. Based on commercial-scale fluid catalytic cracker unit data, we validate the effectiveness of the proposed methods with some state-of-the-art causal discovery algorithms.

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    Cited By

    This article is cited by 1 publications.

    1. Tatsuki Saito, Koichi Fujiwara. Causal analysis of nitrogen oxides emissions process in coal-fired power plant with LiNGAM. Frontiers in Analytical Science 2023, 3 https://doi.org/10.3389/frans.2023.1045324

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