Abstract
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 language | English |
|---|---|
| Pages (from-to) | 294-307 |
| Number of pages | 13 |
| Journal | Control Engineering Practice |
| Volume | 16 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Mar 2008 |
Keywords
- Condensate fractionation
- Condition monitoring
- Model predictive control
- Multivariate statistical process control
- Principal component analysis
- Recursive PCA and multiblock PCA
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Dive into the research topics of 'Monitoring a complex refining process using multivariate statistics'. Together they form a unique fingerprint.Impacts
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Financial and environmental benefits through the development and transfer of control and monitoring technology in the process industries
Lennox, B. (Participant), Marjanovic, O. (Participant), (Participant) & (Participant)
Impact: Policy, Economic, Environmental