Principal Component Analysis Based Data Reconciliation for a Steam Metering Circuit

  • C. Reddy Varshith
  • J. Reddy Rishika
  • S. Ganesh
  • R. JeyanthiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


Data reconciliation (DR) is playing an important role in reducing random errors usually occurred in measured data. Principal component analysis (PCA), on the other end, deals with the reduction of dimensions when there is large number of variables involved in a complex process. In this paper, we bring these two techniques together to deal with random errors in measured data of a steam metering circuit. The results prove that PCA-based DR is effective in dealing with random errors than DR alone. The study is also extended to work on a partially measured system where only partial information of the system is known.


PCA DR Steam metering circuit 


  1. 1.
    Romagnoli, J., Sanchez, M.: Data Processing and Reconciliation for Chemical Process Operation. Chemometric Monitoring: Product Quality Assessment, Process Fault Detection, and Applications (2000)Google Scholar
  2. 2.
    Narasimhan, S., Jordache, C.: The Importance of Data Reconciliation and Gross Error Detection, pp. 1–31. Gulf Professional Publishing, Burlington (1999)CrossRefGoogle Scholar
  3. 3.
    Yoon, S., MacGregor, J.F.: Fault diagnosis with multivariate statistical models part I: using steady state fault signatures. J. Process Control 11(4), 387–400 (2001)CrossRefGoogle Scholar
  4. 4.
    Yang, Y., Ten, R., Jao, L.: A study of gross error detection and data reconciliation in process industries. Comput. Chem. Eng. 19(Supplement 1), 217–222 (1995)CrossRefGoogle Scholar
  5. 5.
    Narasimhan, S., Bhatt, N.: Deconstructing principal component analysis using a data reconciliation perspective. Comput. Chem. Eng. 77, 74–84 (2015)CrossRefGoogle Scholar
  6. 6.
    Narasimhan, S., Shah, S.L.: Model identification and error covariance matrix estimation from noisy data using PCA. Control Eng. Pract. 16(1), 146–155 (2008)CrossRefGoogle Scholar
  7. 7.
    Moore, B.: Principal component analysis in linear systems: controllability, observability, and model reduction. IEEE Trans. Autom. Control 26(1), 0018–9286 (1981)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Wentzell, P.D., Andrews, D.T., Hamilton, D.C., Faber, K., Kowalski, B.R.: Maximum likelihood principal component analysis. Chemometrics 11, 339–366 (1997)CrossRefGoogle Scholar
  9. 9.
    Andersen, A.H., Gash, D.M., Avison, M.J.: Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. Magn. Reson. Imaging 17(6), 795–815 (1999)CrossRefGoogle Scholar
  10. 10.
    Rao, C.R.: The use and interpretation of principal component analysis in applied research. Sankhyā Indian J. Stat. Ser. A (1961–2002) 26(4), 329–358 (1964)Google Scholar
  11. 11.
    Chan, N.N., Mak, T.K.: Estimation in multivariate errors-in-variables models. Linear Algebra Appl. 70, 197–207 (1985)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hiremath, N., Kumar, S.N., Narayanan, N.S.S., Jeyanthi, R.: A study of dealing serially correlated data in GED techniques. In: International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Kollam, 2017, pp. 1–6Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • C. Reddy Varshith
    • 1
  • J. Reddy Rishika
    • 1
  • S. Ganesh
    • 1
  • R. Jeyanthi
    • 1
    Email author
  1. 1.Department of Electronics and Communication EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamBengaluruIndia

Personalised recommendations