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

IEEE Access · 2017 · Vol. 5 · pp. 20590–20616
Zhiqiang GeZhihuan SongSteven X. DingBiao Huang

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.

Fault Detection and Control SystemsAdvanced Statistical Process MonitoringMineral Processing and GrindingAnalyticsComputer scienceProcess (computing)Data scienceData analysisMachine learningKnowledge extractionSoftware analyticsProcess miningArtificial intelligence

Funding

  • Alexander von Humboldt-Stiftung
  • National Natural Science Foundation of China
Citations
876
FWCI
77.58
field-weighted impact
References
335
Percentile
100%
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Citations per year
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