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Dynamic Fault Diagnosis for Semi-Batch Reactor under Closed-Loop Control via Independent Radial Basis Function Neural Network

Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm


In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. Several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature, and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust Fault Detection, Cascade Control, RBF neural networks, Chylla-Haase reactor, independent RBF model, FDI under closed-loop control

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[1] Barton, R. S. and Himmelblau, D. M. (1997) Online prediction of polymer product quality in an industrial reactor using recurrent neural networks, 114 vol.1.
[2] Beyer, M.-A., Grote, W. and Reinig, G. (2008) 'Adaptive exact linearization control of batch polymerization reactors using a Sigma- Point Kalman Filter', Journal of Process Control, 18(7–8), 663-675.
[3] Chen, S., Billings, S. A., Cowan, C. F. N. and Grant, P. M. (1990) 'Nonlinear systems identification using radial basis functions', International Journal of Systems Science, 21(12), 2513-2539.
[4] Chylla, R. W. and Haase, D. R. (1993) 'Temperature control of semibatch polymerization reactors', Computers & Chemical Engineering, 17(3), 257-264.
[5] Deibert, R. and Isermann, R. (1992) 'Examples for fault detection in closed loops', Annual Review in Automatic Programming, 17(0), 235- 240.
[6] Ertiame, A. M., Dingli, Y., Feng, Y. and Gomm, J. B. (2013) Fault detection and isolation for open-loop Chylla-Haase polymerization reactor, 1-6.
[7] Ertiame, A. M., et al. (2014). "Robust fault diagnosis for an exothermic semi-batch polymerization reactor under open-loop." Systems Science & Control Engineering 3(1): 14-23.
[8] Fabrizio Caccavale, Mario Iamarino, Francesco Pierri and Tufano, V. (2011) Control and Monitoring of Chemical Batch Reactors, London: Springer-Verlag London Limited.
[9] Ferrari, R. M. G., Parisini, T. and Polycarpou, M. M. (2008) A robust fault detection and isolation scheme for a class of uncertain input-output discrete-time nonlinear systems, 2804-2809.
[10] Frank, P. M. and Köppen-Seliger, B. (1997) 'Fuzzy logic and neural network applications to fault diagnosis', International Journal of Approximate Reasoning, 16(1), 67-88.
[11] Gertler, J. J. (1988) 'Survey of model-based failure detection and isolation in complex plants', Control Systems Magazine, IEEE, 8(6), 3- 11.
[12] Gomm, J. B., et al. (1996). "Enhancing the non-linear modelling capabilities of MLP neural networks using spread encoding." Fuzzy Sets and Systems 79(1): 113-126.
[13] Gomm, J. B. and Yu, D. L. (2000) 'Selecting radial basis function network centers with recursive orthogonal least squares training', Neural Networks, IEEE Transactions on, 11(2), 306-314.
[14] Graichen, K., Hagenmeyer, V. and Zeitz, M. (2005) Adaptive Feedforward Control with Parameter Estimation for the Chylla-Haase Polymerization Reactor, 3049-3054.
[15] Graichen, K., Hagenmeyer, V. and Zeitz, M. (2006) 'Feedforward control with online parameter estimation applied to the Chylla–Haase reactor benchmark', Journal of Process Control, 16(7), 733-745.
[16] Isermann, R. (1984) 'Process fault detection based on modeling and estimation methods—A survey', Automatica, 20(4), 387-404.
[17] Patton, R. J. and Chen, J. (1992) Robustness in quantitative model-based fault diagnosis, 4/1-417.
[18] Patton, R. J., Chen, J. and Siew, T. M. (1994) Fault diagnosis in nonlinear dynamic systems via neural networks, 1346-1351 vol.2.
[19] Pierri, F., Paviglianiti, G., Caccavale, F. and Mattei, M. (2008) 'Observer-based sensor fault detection and isolation for chemical batch reactors', Engineering Applications of Artificial Intelligence, 21(8), 1204-1216.
[20] Polycarpou, M. M. and Helmicki, A. J. (1995) 'Automated fault detection and accommodation: a learning systems approach', Systems, Man and Cybernetics, IEEE Transactions on, 25(11), 1447-1458.
[21] Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N. and Yin, K. (2003) 'A review of process fault detection and diagnosis: Part III: Process history based methods', Computers & Chemical Engineering, 27(3), 327-346.
[22] Wang, S. W., Yu, D. L., Gomm, J. B., Page, G. F. and Douglas, S. S. (2006) 'Adaptive neural network model based predictive control for air– fuel ratio of SI engines', Engineering Applications of Artificial Intelligence, 19(2), 189-200.
[23] Xiaodong, Z. (2011) 'Sensor Bias Fault Detection and Isolation in a Class of Nonlinear Uncertain Systems Using Adaptive Estimation', Automatic Control, IEEE Transactions on, 56(5), 1220-1226.
[24] Xiaodong, Z., Polycarpou, M. and Parisini, T. (2001) Fault isolation in a class of nonlinear uncertain input-output systems, 1741-1746 vol.2.
[25] Xiaodong, Z., Polycarpou, M. M. and Parisini, T. (2002) 'A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems', Automatic Control, IEEE Transactions on, 47(4), 576-593.
[26] Yu, D. L., Gomm, J. B. and Williams, D. (1999) 'Sensor fault diagnosis in a chemical process via RBF neural networks', Control Engineering Practice, 7(1), 49-55.