Causal Discovery Based on Observational Data and Process Knowledge in Industrial Processes
Liang Cao, Jianping Su, Yixiu Wang, Yankai Cao, Lim C. Siang, Jin Li, Jack Saddler, R. Bhushan Gopaluni
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 unit data, we validate the effectiveness of the proposed methods with some state-of-the-art causal discovery algorithms.