Industrial Data Collection

  • Aarón D. Bojarski
  • Carlos Rodrigo Alvarez Medina
  • Mar Pérez–Fortes
  • Pilar Coca
Part of the Green Energy and Technology book series (GREEN)


In this chapter, a general description of data-mining techniques is done in the context of IGCC operation. The different control philosophies applicable to IGCC operation are discussed together with different examples of data reconciliation based on process simulation. The problem of process monitorisation, as an example of data-mining application, is extensively discussed and an approach based on PCA is presented.


Steam Turbine Normal Operation Condition Principal Component Analysis Model Distribute Control System Square Prediction Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Air separation unit


Combined cycle


Cumulative percent variance


Distributed control system


Data reconciliation


Independent component analysis


Multivariate statistical process control


Normal operating condition


Outlet temperature corrected


Principal component


Principal components analysis


Plant information system


Partial least squares


Squared prediction error


  1. 1.
    Tona-Vásquez RV (2006) Estrategias de análisis y exploración de datos como soporte a la operación y supervisión de procesos químicos (in Spanish). PhD thesis, Universitat Politecnica de Catalunya, Barcelona, SpainGoogle Scholar
  2. 2.
    Kuehn D, Davidson H (1961) Computer control at American oil I: mathematics of control. Chem Eng Prog 57(6):44–51Google Scholar
  3. 3.
    Knepper JC, Gorman JW (1980) Statistical analysis of constrained data sets. AIChE J 26:260–264MathSciNetCrossRefGoogle Scholar
  4. 4.
    Tjoa I, Biegler L (1992) Reduced successive quadratic programming strategy for errors-invariables estimation. Comput Chem Eng 16(6):523–533CrossRefGoogle Scholar
  5. 5.
    Romagnoli J, Sanchez M (1999) Data processing and reconciliation for chemical processes operation. Academic Press, New YorkGoogle Scholar
  6. 6.
    Alvarez-Medina CR (2010) Integración de reconciliación de datos y monitoreo en procesos de polimerización (in Spanish). PhD thesis, Universidad Nacional del Sur, UNS, Bahia Blanca, ArgentinaGoogle Scholar
  7. 7.
    Crowe C, García-Campos Y, Hrymak A (1983) Reconciliation of process flow rates by matrix projection part I: linear case. AIChE J 29:881–888CrossRefGoogle Scholar
  8. 8.
    Sanchez M, Romagnoli J (1996) Use of orthogonal transformations in data classification-reconciliation. Comput Chem Eng 20(5):483–493CrossRefGoogle Scholar
  9. 9.
    Zagoruiko AN, Matros YS (2002) Mathematical modelling of Claus reactors undergoing sulfur condensation and evaporation. Chem Eng J 87(1):73–88CrossRefGoogle Scholar
  10. 10.
    Perez-Fortes MM, Bojarski AD, Velo E, Nougués JM, Puigjaner L (2009) Conceptual model and evaluation of generated power and emissions in an IGCC plant. Energy 34:1721–1732CrossRefGoogle Scholar
  11. 11.
    Sequeira SE (2003) Real time evolution (RTE) for on-line optimisation of continuous and semi-continuous chemical processes. PhD thesis, Universitat Politecnica de Catalunya, Barcelona, SpainGoogle Scholar
  12. 12.
    Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann, Massachusets, USAGoogle Scholar
  13. 13.
    Jackson JE (1991) A user’s guide to principal components. John Wiley & Sons, New York, USAMATHCrossRefGoogle Scholar
  14. 14.
    Wold H (1966) Estimation of principal components and related models by iterative least squares. In: Krishnaiah PR (ed) Multivariate analysis. Academic Press, New York, pp 391–420Google Scholar
  15. 15.
    Valle S, Li W, Qin SJ (1999) Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Ind Eng Chem Res 38(11):4389–4401CrossRefGoogle Scholar
  16. 16.
    Hotelling H (1931) The generalization of Student’s ratio. Ann Math Stat 2:360–378MATHCrossRefGoogle Scholar
  17. 17.
    Nomikos P, MacGregor JF (1995) Multivariate SPC charts for monitoring batch processes. Technometrics 37(1):41–59MATHCrossRefGoogle Scholar
  18. 18.
    Westerhuis JA, Gurden SP, Smilde AK (2000) Generalized contribution plots in multivariate statistical process monitoring. Chemom Intell Lab Syst 51:95–114CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Aarón D. Bojarski
    • 1
  • Carlos Rodrigo Alvarez Medina
    • 1
  • Mar Pérez–Fortes
    • 1
  • Pilar Coca
    • 2
  1. 1.ETSEIBUniversitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.ELCOGAS, S.A.CtraPuertollano–(Ciudad Real)Spain

Personalised recommendations