Traditional techniques employed by control engineers require a significant update in order to handle the increasing complexity of modern processes. Conveniently, advances in statistical machine learning and distributed computation have led to an abundance of techniques suitable for advanced analysis. In this tutorial we introduce data analytics techniques and discuss their theory and application to chemical processes. Although the focus is more on theory, the applications will be explored more widely in a follow-up journal paper. The ultimate goal is to familiarize control engineers with how these techniques are used to extract valuable knowledge from raw data, which can then be utilized to make smarter process control decisions.
Recommended citation: Tsai, Y., Lu, Q., Rippon, L., Lim, S., Tulsyan, A., & Gopaluni, B. (2018). “Pattern and knowledge extraction using process data analytics: A tutorial.” IFAC-PapersOnLine. 51(18), pp. 13-18.