Machine learning for industrial sensing and control: A survey and practical perspective

no code implementations • 24 Jan 2024 • Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoit Chachuat, Jong Min Lee, Biao Huang, R. Bhushan Gopaluni

Soft sensing contains a wealth of industrial applications of statistical and machine learning methods.

Estimation of minimum miscibility pressure (MMP) in impure/pure N2 based enhanced oil recovery process: A comparative study of statistical and machine learning algorithms

no code implementations • 15 Apr 2023 • Xiuli Zhu, Seshu Kumar Damarla, Biao Huang

Most of the predictive models developed in this study exhibited superior performance over correlation and predictive models reported in literature.

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

no code implementations • 22 Sep 2022 • R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence

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.