Fault Tolerant Control for a Dearomatisation Process

Abstract In this paper, a fault tolerant control (FTC) for a dearomatisation process in the presence of faults in online product quality analysers is presented. The FTC consists of a fault detection system (FDI) and a logic for triggering predefined FTC actions. FDI is achieved by combining several process data driven approaches for detecting faults in online quality analysers. The FTC exploits the diagnostic information in adapting a quality controller (MPC) to the faulty situation by manipulating tuning parameters of the MPC to produce both proactive and reactive strategies. The proposed FTC was implemented, tested offline and validated onsite at the Naantali oil refinery. The successful testing and plant validation results are presented and discussed.

[1]  Sirkka-Liisa Jämsä-Jounela,et al.  Offline testing of the FTC-strategy for dearomatization process of the Naantali refinary , 2007 .

[2]  P. Balle,et al.  Integrated control, diagnosis and reconfiguration of a heat exchanger , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[3]  M. Sourander,et al.  FTC STRATEGIES IN MODEL PREDICTIVE CONTROL OF A DEAROMATISATION PROCESS , 2006 .

[4]  R. Bro Multiway calibration. Multilinear PLS , 1996 .

[5]  S. Narasimhan,et al.  A Supervisory Approach to Fault-Tolerant Control of Linear Multivariable Systems , 2002 .

[6]  Marios M. Polycarpou,et al.  Automated fault detection and accommodation: a learning systems approach , 1995, IEEE Trans. Syst. Man Cybern..

[7]  Per A. Hassel,et al.  Nonlinear partial least squares , 2003 .

[8]  B. De Moor,et al.  A unifying theorem for three subspace system identification algorithms , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[9]  S. Joe Qin,et al.  Sensor validation and process fault diagnosis for FCC units under MPC feedback , 2001 .

[10]  Rolf Isermann,et al.  Integrated control, diagnosis and reconfiguration of a heat exchanger , 1998 .

[11]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[12]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[13]  Bhupinder S. Dayal,et al.  Recursive exponentially weighted PLS and its applications to adaptive control and prediction , 1997 .

[14]  Sirkka-Liisa Jämsä-Jounela,et al.  A process monitoring system based on the Kohonen self-organizing maps , 2003 .

[15]  Tiina M. Komulainen,et al.  Fault detection and isolation of an on-line analyzer for an ethylene cracking process , 2008 .

[16]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[17]  H. E. Rauch,et al.  Autonomous control reconfiguration , 1995 .

[18]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[19]  Thomas J. McAvoy,et al.  Nonlinear PLS Modeling Using Neural Networks , 1992 .

[20]  Tiina M. Komulainen,et al.  An online application of dynamic PLS to a dearomatization process , 2004, Comput. Chem. Eng..

[21]  S. Wold,et al.  Nonlinear PLS modeling , 1989 .

[22]  Didier Theilliol,et al.  Fault-tolerant control in dynamic systems: application to a winding machine , 2000 .

[23]  Thomas E Marlin,et al.  Process Control , 1995 .

[24]  C. Yoo,et al.  Nonlinear PLS modeling with fuzzy inference system , 2002 .

[25]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[26]  D. Massart,et al.  Application of Radial Basis Functions — Partial Least Squares to non-linear pattern recognition problems: diagnosis of process faults , 1996 .

[27]  Jan M. Maciejowski,et al.  Modelling and predictive control: Enabling technologies for reconfiguration , 1999 .

[28]  U. Kruger,et al.  Dynamic Principal Component Analysis Using Subspace Model Identification , 2005, ICIC.

[29]  S. Wold,et al.  The kernel algorithm for PLS , 1993 .

[30]  Magali R. G. Meireles,et al.  A comprehensive review for industrial applicability of artificial neural networks , 2003, IEEE Trans. Ind. Electron..

[31]  J. E. Jackson A User's Guide to Principal Components , 1991 .

[32]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[33]  Theodora Kourti,et al.  Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .

[34]  Bart De Moor,et al.  A unifying theorem for three subspace system identification algorithms , 1995, Autom..

[35]  Uwe Kruger,et al.  Recursive partial least squares algorithms for monitoring complex industrial processes , 2003 .

[36]  D. Hinkley Inference about the change-point from cumulative sum tests , 1971 .

[37]  K. Helland,et al.  Recursive algorithm for partial least squares regression , 1992 .

[38]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[39]  S. Qin,et al.  Partial least squares regression for recursive system identification , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[40]  S. Qin Recursive PLS algorithms for adaptive data modeling , 1998 .

[41]  Edward C. Malthouse,et al.  Nonlinear partial least squares , 1997 .

[42]  S. Wold,et al.  The multivariate calibration problem in chemistry solved by the PLS method , 1983 .

[43]  S. Wold Nonlinear partial least squares modelling II. Spline inner relation , 1992 .