Causal Discovery Based on Observational Data and Process Knowledge in Industrial Processes-Reference-Cited by-同舟云学术

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

Published:2022-09-14 Issue:38 Volume:61 Page:14272-14283
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Cao Liang¹,Su Jianping²,Wang Yixiu¹,Cao Yankai¹^ORCID,Siang Lim C.³^ORCID,Li Jin³,Saddler Jack Nicholas²^ORCID,Gopaluni Bhushan¹^ORCID

Affiliation:

1. Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada

2. Forest Products Biotechnology/Bioenergy Group, The University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

3. Department of Process Control Engineering, Burnaby Refinery, Burnaby, British Columbia V5C 1L7, Canada

Funder

Mitacs

Parkland Corporation

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c01326

Reference30 articles.

1. A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

2. Online Distributed Process Monitoring and Alarm Analysis Using Novel Canonical Variate Analysis with Multicorrelation Blocks and Enhanced Contribution Plot

3. Using Machine Learning To Predict Suitable Conditions for Organic Reactions

4. Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA

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