Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
R. Bhushan Gopaluni
Aditya Tulsyan
Benoît Chachuat
Biao Huang
J. M. Lee
Faraz Amjad
S. Damarla
Jong Woo Kim
Nathan P. Lawrence

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.
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