Abstract
The reliability of measured data, which can be subject to both systematic and random errors, is of great importance for the monitoring and evaluation of process performance and the determination of control action. This Chapter presents and assesses bias estimation (as a type of systematic error) technique and data reconciliation methods for the detection, estimation and elimination of biases and random errors respectively. It is shown how these methods can be successfully employed within an on-line Integrated System Optimisation and Parameter Estimation (ISOPE) scheme for the determination of the process optimum, despite the existence of model-reality differences and measurement errors. The performance of the resulting scheme is demonstrated by application to a two tank CSTR system.
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References
Abu-el-Zeet, Z.H.: Optimisation Techniques for Advanced Process Supervision and Control. PhD Thesis, City University, London, EC1V 0HB, U.K (2000)
Abu-el-Zeet, Z.H., Becerra, V.M., Roberts, P.D.: Data Reconciliation and Steady-State Detection Applied to a Chemical Process. In: UKACC International Conference (Control 2000), September 4-7, University of Cambridge, Cambridge (2000)
Albuquerque, J.S., Biegler, L.T.: Data Reconciliation and Gross-Error Detection for Dynamic Systems. AIChE Journal 42(10) (1996)
Arora, N., Biegler, L.T., Heyen, G.: Optimization Formulations for Data Reconciliation. Computer Aided Process Engineering (2002)
Bagajewicz, M.J.: Data Reconciliation and Instrumentation Upgrade. Overview and Challenges. In: FOCAPO (Foundations of Computer Aided Process Operations), Coral Springs, FL, USA (2003)
Crowe, C.M.: Data reconciliation-progress and challenges. Journal of Process Control 6(2/3), 89–98 (1996)
Ellis, J.E., Kambhampati, C., Sheng, G., Roberts, P.D.: Approaches to the Optimizing Control Problem. Int. J. of Systems Science 19, 1969–1985 (1988)
Fletcher, R.: Practical Methods of Optimization. Wiley Interscience (1980)
Garcia, C.E., Morari, M.: Optimal Operation of Integrated processing Systems. AICHE Journal 27, 960–968 (1981)
Gelb, A.: Applied Optimal Estimation. MIT Press, Cambridge (1974)
Jang, S.S., Joseph, B., Mukai, H.: Comparison of two approaches to on-line parameter and state estimation of nonlinear systems. Ind. Engng. Chem. Process. Des. Dev. 25, 809–814 (1986)
Kim, I.W., Kang, M.S., Park, S., Edgar, T.F.: Robust Data Reconciliation and Gross Error Detection: The Modified MIMT Using NLP. Computers and Chemical Engineering 21(7), 775–782 (1997)
Liebman, M.J., Edgar, T.F.: Data reconciliation for nonlinear processes, Paper Presented at the AIChE Annual Meeting, Washington, DC (1988)
Madron, F.: Process plant performance: Measurement and data processing for optimization and retrofits. Ellis Horwood, Chichester (1992)
Mah, R.S.H.: Design and Analysis of Process Performance Monitoring Systems. In: Edgar, T.F., Seborg, D.E. (eds.) Chemical Process Control II, Engineering Foundation, New York, p. 525 (1982); Proceedings of the Engineering Foundation Conference, Sea Island, Georgia, January 18-23 (1981)
Mah, R.S.H.: Chemical Process Structures and Information Flows. ch. 8,9. Butterworths, Stoneham (1990)
Mah, R.S.H., Stanley, G.M., Downing, D.M.: Reconciliation and rectification of process flow and inventory data. Ind. Engng Chem. Process. Des. Dev. 15, 175–183 (1976)
Mah, R.S.H., Tamhane, A.C.: Detection of Gross Errors in Process Data. AIChE Journal 28, 828–830 (1982)
McBrayer, K.F., Edgar, T.F.: Bias Detection and Estimation in Dynamic Data Reconciliation. Journal of Process Control 5, 285–289 (1995)
Mansour, M., Ellis, J.E.: Comparison of Methods for Estimating Real Process Derivatives in On-line Optimization. Applied Mathematical Modelling 27, 275–291 (2003)
Mansour, M., Ellis, J.E.: Methodology of On-line Optimisation applied to a Chemical Reactor. Applied Mathematical Modelling 32, 170–184 (2008)
Narasimhan, S., Mah, R.S.H.: Generalized Likelihood Ratio Method for Gross Error Identification. AIChE Journal 33(9), 1514–1521 (1987)
Narasimhan, S., Jordache, C.: Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data. Gulf Publishing Company (2000)
Roberts, P.D.: An Algorithm for Steady-State System Optimisation and Parameter Estimation. Int. J. of Systems Science 10, 719–734 (1979)
Roberts, P.D., Williams, T.W.C.: On an Algorithm for Combined System Optimisation and Parameter Estimation. Automatica 17, 199–209 (1981)
Rollings, D.K., Cheng, Y., Devanathan, S.: Intelligent Selection of Hypothesis tests to Enhance Gross Error Identification. Comp. and Chem. Eng. 20(5), 517–530 (1996)
Tamhane, A.C., Mah, R.S.H.: Data Reconciliation and Gross Error Detection in Chemical Process Networks. Technometrics 27(4), 409–422 (1985)
Tjoa, I.B., Biegler, L.T.: Simultaneous Strategies for Data Reconciliation and Gross Error Detection of Nonlinear Systems. Computers and Chemical Engineering 15(10), 679–690 (1991)
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Mansour, M. (2013). Data Reconciliation and Bias Estimation in On-Line Optimization. In: Kyamakya, K., Halang, W., Mathis, W., Chedjou, J., Li, Z. (eds) Selected Topics in Nonlinear Dynamics and Theoretical Electrical Engineering. Studies in Computational Intelligence, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37781-5_23
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DOI: https://doi.org/10.1007/978-3-642-37781-5_23
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