Observational process data analytics using causal inference

Yang, Shu; Bequette, B. Wayne

doi:10.1002/aic.17986

Observational process data analytics using causal inference

Journal Article · Wed Dec 07 00:00:00 EST 2022 · AIChE Journal

DOI:https://doi.org/10.1002/aic.17986· OSTI ID:2418510

^[1]; Bequette, B. Wayne ^[2]

Rensselaer Polytechnic Inst., Troy, NY (United States); OSTI
Rensselaer Polytechnic Inst., Troy, NY (United States)

Voluminous process data are available with the paradigm shift toward smart manufacturing. However, most historical data are observational, containing noncausal correlations due to confounders and mediators. Estimating causal effects from observational data remains a bottleneck in leveraging them for active applications such as optimization and control. Further, this work aims to introduce a causal modeling framework for analyzing observational process data and extracting quantitative causal information. We demonstrate a real-world application in steel manufacturing where causal inference is used to analyze observational production data and improve the steelmaking process. Additionally, we propose a novel formulation for identifying critical process parameters from observational data, where causal inference is combined with variance-based methods to estimate corresponding risks of interventions to the manufacturing system. The proposed methods are compared with statistical ones to illustrate that causally interpreting statistical correlation leads to problematic results, while the provided workflow generates satisfactory strategies for process improvement.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, Los Angeles, CA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0007613

OSTI ID:: 2418510

Alternate ID(s):: OSTI ID: 1905951

Journal Information:: AIChE Journal, Journal Name: AIChE Journal Journal Issue: 4 Vol. 69; ISSN 0001-1541

Publisher:: American Institute of Chemical EngineersCopyright Statement

Country of Publication:: United States

Language:: English

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