Sensor validation and process fault diagnosis for FCC units under MPC feedback |
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| Affiliation: | 1. Dpt of Systems Engineering and Automatic Control, University of Valladolid, Doctor Mergelina s/n, 47011 Valladolid, Spain;7. Dpt of Electromechanical Engineering, University of Burgos, Avda. Cantabria s/n, 09006 Burgos;71. Dpt. Ingenieria Quimica y Ambiental, Universidad Tecnica Federico Santa Maria, Santiago, Chile;1. School of Chemical and Biological Engineering, Engineering Development Research Center, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea;2. Clean Energy Research Center, Korea Institute of Science and Technology (KIST), 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea;3. School of Chemical Engineering and Materials Science, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea;1. Department of Mathematics and Industrial Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec H3T 1J4, Canada;2. CanmetENERGY-Natural Resources Canada, 1615 Lionel-Boulet Blvd., P.O. Box 4800, Varennes, Québec J3X 1S6, Canada;3. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32925, Egypt |
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| Abstract: | 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. |
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