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

Zhiqiang GeState Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaZhihuan SongState Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, ChinaSteven X. DingInstitute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg, GermanyBiao HuangDepartment of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada
2017en
ABI

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.

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Cited by 20 references
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