PAPER (2022)

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

Industrial & Engineering Chemistry Research,

, , , , , , ,

[PDF]

Causal Discovery based on Observational Data and Process Knowledge in Industrial Processes by Liang Cao, Jianping Su, Yixiu Wang, Yankai Cao, Lim C. Siang, Jin Li, Jack Nicholas Saddler, and Bhushan Gopaluni
Causal Discovery based on Observational Data and Process Knowledge in Industrial Processes by Liang Cao, Jianping Su, Yixiu Wang, Yankai Cao, Lim C. Siang, Jin Li, Jack Nicholas Saddler, and Bhushan Gopaluni

Click to enlarge image.

Abstract

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 (FCC) unit data, we validate the effectiveness of the proposed methods with some state-of-the-art causal discovery algorithms.

Read or Download: PDF

Can't find a paper? Create a GitHub issue to request a preprint.


DAIS Lab Publications

Read More: