Monitoring a complex refining process using multivariate statistics

Ashraf AlGhazzawi, Barry Lennox

    Research output: Contribution to journalArticlepeer-review


    Over the past decade, multivariate statistical process control (MSPC) methods have been proven, in the process industries, to be an effective tool for process monitoring, modelling and fault detection. This paper describes the development of a real-time monitoring solution for a complex petroleum refining process with an installed multivariable model predictive controller. The developed solution was designed to track the time-varying and non-stationary dynamics of the process and for improved isolation capabilities, a multiblock approach was applied. The paper highlights the systematic and generic approach that was followed to develop the monitoring solution and stresses the importance of exploiting the knowledge of experienced plant personnel when developing any such system. © 2007 Elsevier Ltd. All rights reserved.
    Original languageEnglish
    Pages (from-to)294-307
    Number of pages13
    JournalControl Engineering Practice
    Issue number3
    Publication statusPublished - Mar 2008


    • Condensate fractionation
    • Condition monitoring
    • Model predictive control
    • Multivariate statistical process control
    • Principal component analysis
    • Recursive PCA and multiblock PCA


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