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Abstract This article presents an experimental application of fault detection, isolation, and estimation in a chemical reactor system, introducing a functional observer‐based approach without the need for linear approximation. The residual signal generators, functioning as disturbance‐decoupled functional observers, provide fault size estimates and enable fault isolation through multiple generators operating independently. The experimental study focuses on the 3‐Picoline oxidation process, deriving a discrete‐time model, and constructing specific residual generators for coolant inlet temperature and feed concentration faults. Fault diagnosis employs Fast Fourier Transform (FFT) filtering and Generalized Likelihood Ratio (GLR), facilitating on‐the‐fly detection during the experiment. The effectiveness of fault detection, disturbance decoupling, and estimation is experimentally validated.
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Model‐based fault diagnosis in closed‐loop safety‐critical chemical reactors: An experimental study
Abstract This experimental study investigates fault detection and estimation in a continuous stirred‐tank reactor (CSTR) system under closed‐loop feedback control, including an analysis of different manipulative inputs for temperature regulation. A novel fault diagnosis approach is proposed, combining residual signal analysis andT2statistic for real‐time fault detection and size estimation. The closed‐loop system demonstrated robust setpoint tracking and fault tolerance across a range of fault magnitudes. Residual signals serve as direct estimators of fault size, critical for adaptive control, while theT2statistic enhances reliability by identifying deviations from normal behavior with fault‐confidence thresholds. As a step towards fault‐tolerant control, the proposed methodology lays the groundwork for advanced control strategies that can ensure safe and efficient operation of chemical reactor systems.
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- Award ID(s):
- 2133810
- PAR ID:
- 10636191
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 71
- Issue:
- 9
- ISSN:
- 0001-1541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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