Design of Instrumentation Sensor Networks for Non-Linear Dynamic Processes Using Extended Kalman Filter

Document Type: Research Article

Authors

1 Department of Automation and Instrumentation, Petroleum University of Technology, Tehran, I.R. IRAN

2 Instituto de sistemas e Robótica (ISR), Instituto Superior Técnico (IST), Technical University of Lisbon (UTL), Lisbon, PORTUGAL

Abstract

This paper presents a methodology for design of instrumentation sensor networks in non-linear chemical plants. The method utilizes a robust extended Kalman filter approach to provide an efficient dynamic data reconciliation. A weighted objective function has been introduced to enable the designer to incorporate each individual process variable with its own operational importance. To enhance the evaluation accuracy of the weighted objective function, a true relative standard deviation measure has been employed in the presented formulation. A Genetic Algorithm (GA) has been used to solve the resulting constrained optimization problem due to cost-optimal and performance-optimal design objectives. The proposed method has been tested on a non-linear continuous-stirred tank reactor (CSTR) benchmark plant, illustrating its effective design capabilities.

Keywords


[1] Vaclavek, V. and Loucka, M., Selection of Measure-ments Necessary to Achieve Multicomponent Mass Balances in Chemical Plant, Chem. Eng. Sc., 31, 1199, (1976).
[2] Ali, Y. and Narasimhan, S., Sensor Network Design for Maximizing Reliability of Linear Processes, AIChE J., 39, 820, (1993).
[3] Ali,  Y.  and  Narasimhan,  S., Redundant Sensor Network for Linear Processes, AIChE J., 41, 2237, (1995).
[4] Sen, S., Narasimhan, S. and Deb, K., Sensor Network Design of Linear Processes Using Genetic Algorithms, Comput. Chem. Eng., 22, 385, (1998).
[5] Madron, F., “Process Plant Performance Measurement and Data Processing for Optimization and Retrofits”, Chichester, England: EllisHorwood, (1992).
[6] Bagajewicz, M.J., Design and Retrofit of Sensors Networks in Process Plants, AIChE J., 43(9), 2300, (1997).
[7] Bagajewicz, M.J., “Process Plant Instrumentation Design and Upgrade”, Technomic Publishing Company, (2000).
[8] Bagajewicz, M.J. and Cabrera, E.,A New MILP Formulation for Instrumentation Network Design and Upgrade, AIChE J., 48(10), 2271, (2001).
[9] Chmielewski, D., Palmer, T. and Manousiouthakis, V.,On the Theory of Optimal Sensor Placement, AIChE J., 48(5), 1001, (2002).
[10] Carnero,  M.,  Hernandez,  J.,  Sanchez,  M. and Bandoni, A., An Evolutionary Approach for the Design of Nonredundant Sensor Networks, Ind. Eng. Chem. Res., 40 (23), 5578, (2001).
[11] Gerkens, C. and Heyen, G., Sensor Network Design Using Genetic Algorithm,Proceedings of 11th IFAC Symposium on Automation in Mining, Mineral and Metal Processing, Nancy, France 8-10 September, (2004a).
[12] Carnero,  M.,  Hernandez,  J., Sanchez,  M.  and Bandoni, A., On the Solution of the Instrumentation Selection Problem, Ind. Eng. Chem. Res.44 (2), 358-367, (2005).
[13] Musulin, E., Benqlilou, C., Bagajewicz, M.J. and Puigjaner, L., Instrumentation Design Based on Optimal Kalman Filtering, J. Process Control, 15, 629 (2005).
[14] Welch,  G. and  Bishop,  G.,  An   Introduction to the Kalman Filter, Department of Computer Science, Chapel Hill, NC, 27599-3175, http://www.cs.unc.edu /~{welch, gb}, (2001).
[15] Maybeck, P.S., “Stochastic Models, Estimation and Control”, New York: Academic, vols. I and II, (1982).
[16] Genetic  Algorithm  Toolbox  [on-line],  Available from: http:// www.shef.ac.uk/ uni/ projects/ gaipp/ gatoolbox/, (2003).
[17] Bhushan, M. and Rengaswamy, R.,Design of Sensor Location Based on Various Fault Diagnosis Observability and Reliability Criteria, Comput. Chem. Eng., 24, 735, (2000a).
[18] Bagajewicz, M.J. and Fuxman, A., Instrumentation Network Design and Upgrade for Process Monitoring and Fault Detection, AIChE J., 50(8), 1870, (2004).