Data Mining and Analytics in the Process Industry: The Role of Machine Learning

TLDR

Data mining and analytics, powered by machine learning, have been essential for knowledge discovery and decision‑making in the process industry for decades. The paper reviews machine‑learning‑driven data mining and analytics applications in the process industry and outlines future research directions. The review surveys eight unsupervised and ten supervised learning algorithms, along with semi‑supervised methods, to assess their application status in the process industry.

Abstract

Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.