Advances and opportunities in machine learning for process data analytics

S. Joe QIN*, Leo H. CHIANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

221 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)465-473
Number of pages9
JournalComputers and Chemical Engineering
Early online date24 Apr 2019
Publication statusPublished - 12 Jul 2019
Externally publishedYes


  • Artificial intelligence
  • Industrial operations
  • Industry 4.0
  • Latent variable methods
  • Machine learning
  • Neural networks
  • Process data analytics


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