Skip to main content
SpringerLink
Log in

Data Reconciliation and Bias Estimation in On-Line Optimization

  • Chapter
Selected Topics in Nonlinear Dynamics and Theoretical Electrical Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 459))

  • 1101 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abu-el-Zeet, Z.H.: Optimisation Techniques for Advanced Process Supervision and Control. PhD Thesis, City University, London, EC1V 0HB, U.K (2000)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. Albuquerque, J.S., Biegler, L.T.: Data Reconciliation and Gross-Error Detection for Dynamic Systems. AIChE Journal 42(10) (1996)

    Google Scholar 

  4. Arora, N., Biegler, L.T., Heyen, G.: Optimization Formulations for Data Reconciliation. Computer Aided Process Engineering (2002)

    Google Scholar 

  5. Bagajewicz, M.J.: Data Reconciliation and Instrumentation Upgrade. Overview and Challenges. In: FOCAPO (Foundations of Computer Aided Process Operations), Coral Springs, FL, USA (2003)

    Google Scholar 

  6. Crowe, C.M.: Data reconciliation-progress and challenges. Journal of Process Control 6(2/3), 89–98 (1996)

    Article  Google Scholar 

  7. 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)

    Article  MATH  Google Scholar 

  8. Fletcher, R.: Practical Methods of Optimization. Wiley Interscience (1980)

    Google Scholar 

  9. Garcia, C.E., Morari, M.: Optimal Operation of Integrated processing Systems. AICHE Journal 27, 960–968 (1981)

    Article  Google Scholar 

  10. Gelb, A.: Applied Optimal Estimation. MIT Press, Cambridge (1974)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Liebman, M.J., Edgar, T.F.: Data reconciliation for nonlinear processes, Paper Presented at the AIChE Annual Meeting, Washington, DC (1988)

    Google Scholar 

  14. Madron, F.: Process plant performance: Measurement and data processing for optimization and retrofits. Ellis Horwood, Chichester (1992)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. Mah, R.S.H.: Chemical Process Structures and Information Flows. ch. 8,9. Butterworths, Stoneham (1990)

    Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Mah, R.S.H., Tamhane, A.C.: Detection of Gross Errors in Process Data. AIChE Journal 28, 828–830 (1982)

    Article  Google Scholar 

  19. McBrayer, K.F., Edgar, T.F.: Bias Detection and Estimation in Dynamic Data Reconciliation. Journal of Process Control 5, 285–289 (1995)

    Article  Google Scholar 

  20. Mansour, M., Ellis, J.E.: Comparison of Methods for Estimating Real Process Derivatives in On-line Optimization. Applied Mathematical Modelling 27, 275–291 (2003)

    Article  MATH  Google Scholar 

  21. Mansour, M., Ellis, J.E.: Methodology of On-line Optimisation applied to a Chemical Reactor. Applied Mathematical Modelling 32, 170–184 (2008)

    Article  MATH  Google Scholar 

  22. Narasimhan, S., Mah, R.S.H.: Generalized Likelihood Ratio Method for Gross Error Identification. AIChE Journal 33(9), 1514–1521 (1987)

    Article  Google Scholar 

  23. Narasimhan, S., Jordache, C.: Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data. Gulf Publishing Company (2000)

    Google Scholar 

  24. Roberts, P.D.: An Algorithm for Steady-State System Optimisation and Parameter Estimation. Int. J. of Systems Science 10, 719–734 (1979)

    Article  MATH  Google Scholar 

  25. Roberts, P.D., Williams, T.W.C.: On an Algorithm for Combined System Optimisation and Parameter Estimation. Automatica 17, 199–209 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. Tamhane, A.C., Mah, R.S.H.: Data Reconciliation and Gross Error Detection in Chemical Process Networks. Technometrics 27(4), 409–422 (1985)

    Article  Google Scholar 

  28. 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electronics and Computer Science, University of Sciences and Technology, BP. 32, El-Allia, Algiers, Algeria

    Moufid Mansour

Authors
  1. Moufid Mansour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moufid Mansour .

Editor information

Editors and Affiliations

  1. Inst. Intelligente Systemtechnologien, Universität Klagenfurt, Universitätsstr. 65-67, Klagenfurt, 9020, Austria

    Kyandoghere Kyamakya

  2. , Lehrgebiet Informationstechnik, FernUniversität in Hagen, Universitätsstraße 27, Hagen, 58097, Germany

    Wolfgang A. Halang

  3. FB Elektrotechnik, Inst. Theoretische Elektrotechnik, Universität Hannover, Appelstr. 9 A, Hannover, 30167, Germany

    Wolfgang Mathis

  4. , Lakeside Park, Alpen-Adria Universität Klagenfurt, Haus B04, Klagenfurt, 9020, Austria

    Jean Camberlain Chedjou

  5. Fernuniversität Hagen, Hagen, 58084, Germany

    Zhong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

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 459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34560-9_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34560-9_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34559-3

  • Online ISBN: 978-3-642-34560-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation