The Role of Big Data in Industrial (Bio)Chemical Process Operations

Isuru A. Udugama, Carina L. Gargalo, Yoshiyuki Yamashita, Michael A. Taube, Ahmet Palazoglu, Brent R. Young, Krist V. Gernaey, Murat Kulahci, Christoph Bayer*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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With the emergence of Industry 4.0 and Big Data initiatives there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this manuscript is to provide a perspective of the current status of Big Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this manuscript provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of Statistical Process Monitoring and (Engineering) Process Control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored and a path forward is proposed.
Original languageEnglish
JournalIndustrial and Engineering Chemistry Research
Issue number34
Pages (from-to)15283–15297
Publication statusPublished - 2020


  • Big Data applications
  • Chemical engineering
  • Closed-loop systems
  • Control engineering
  • Production management
  • Statistical analysis


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