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

TL;DR

This survey reviews recent advances in applying modern machine learning techniques to large-scale nonlinear monitoring and control problems in the process industry, highlighting opportunities enabled by increased data and computational power.

Contribution

It compiles and discusses recent developments in machine learning applications for industrial process monitoring and control, emphasizing the integration of new tools in this domain.

Findings

Enhanced process monitoring accuracy

Improved control strategies using ML

Potential for real-time industrial applications

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.