Advances and opportunities in machine learning for process data analytics

In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory over the last 20 years and commercial successes by leading big data companies. Then we discuss the characteristics of process manufacturing...

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Veröffentlicht in:Computers & chemical engineering Jg. 126; S. 465 - 473
Hauptverfasser: Qin, S. Joe, Chiang, Leo H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 12.07.2019
Schlagworte:
Artificial intelligence
Industrial operations
Industry 4.0
Latent variable methods
Machine learning
Neural networks
Process data analytics
Neural networks
Machine learning
Industrial operations
Process data analytics
Industry 4.0
Artificial intelligence
Latent variable methods
ISSN:0098-1354
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Zusammenfassung:In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory over the last 20 years and commercial successes by leading big data companies. Then we discuss the characteristics of process manufacturing systems and briefly review the data analytics research and development in the last three decades. We give three attributes for process data analytics to make machine learning techniques applicable in the process industries. Next we provide a perspective on the currently active topics in machine learning that could be opportunities for process data analytics research and development. Finally we address the importance of a data analytics culture. Issues discussed range from technology development to workforce education and from government initiatives to curriculum enhancement.
ISSN:0098-1354
DOI:10.1016/j.compchemeng.2019.04.003