Sensor validation and process fault diagnosis for FCC units under MPC feedback


*Corresponding author for this work

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

64 Citations (Scopus)


Model predictive control (MPC) has been widely applied in industry, especially in the refining industry. As all feedback controllers require correct sensor measurements, unreliable sensors can cause the MPC controller to move the process in an erroneous manner. Data validation of sensor measurements is a prerequisite in applying advanced control, particularly multivariable control which depends on many sensors. However, little research work is available on how feedback controllers like MPC complicate the task of sensor validation and process fault diagnosis. In theory, a controller can transfer the effect of a sensor fault in a controlled variable to the manipulated variables. In this paper, principal component analysis (PCA) is applied to detect, identify and reconstruct faulty sensors in a simulated FCC unit. A base PCA model is generated by perturbing the process throughout the operating region. Performance of MPC with and without data validation is compared. The same base PCA model is applied to detect and identify dynamic process faults. We demonstrate that process faults can be detected and diagnosed at an early stage. © 2001 Elsevier Science Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)877-888
Number of pages12
JournalControl Engineering Practice
Issue number8
Early online date24 Jul 2001
Publication statusPublished - Aug 2001
Externally publishedYes

Bibliographical note

This work is supported in part by National Science Foundation under CTS-9814340, Texas Higher Education Coordinating Board, and Pertamina-Indonesia.


  • Gross errors
  • Model predictive control
  • Principal component analysis
  • Process faults
  • Sensor validation


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