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Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey

Cited 18 time in Web of Science Cited 22 time in Scopus
Authors

Gopaluni, R. Bhushan; Tulsyan, Aditya; Chachuat, Benoit; Huang, Biao; Lee, Jong Min; Amjad, Faraz; Damarla, Seshu Kumar; Kim, Jong Woo; Lawrence, Nathan P.

Issue Date
2020-07
Publisher
IFAC Secretariat
Citation
IFAC-PapersOnLine, Vol.53 No.2, pp.225-236
Abstract
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry. Copyright (C) 2020 The Authors.
ISSN
2405-8963
URI
https://hdl.handle.net/10371/186490
DOI
https://doi.org/10.1016/j.ifacol.2020.12.126
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