Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
Machine Learning
2022-09-23 v1 Systems and Control
Systems and Control
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.
Keywords
Cite
@article{arxiv.2209.11123,
title = {Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey},
author = {R. Bhushan Gopaluni and Aditya Tulsyan and Benoit Chachuat and Biao Huang and Jong Min Lee and Faraz Amjad and Seshu Kumar Damarla and Jong Woo Kim and Nathan P. Lawrence},
journal= {arXiv preprint arXiv:2209.11123},
year = {2022}
}
Comments
IFAC World Congress 2020