Research Papers: Gas Turbines: Cycle Innovations

Data Reconciliation and Suspect Measurement Identification for Gas Turbine Cogeneration Systems

[+] Author and Article Information
F. Carl Knopf

e-mail: knopf@lsu.edu
Department of Chemical Engineering,
Louisiana State University,
Baton Rouge, LA 70803

Michael R. Erbes

Enginomix, LLC,
Menlo Park, CA 94026

Frantisek Madron

ChemPlant Technology,
400 01 Czech Republic
e-mail: frantisek.madron@chemplant.cz


1Corresponding author.

Contributed by the Cycle Innovations Committee of ASME for publication in the Journal of Engineering for Gas Turbines and Power. Manuscript received September 11, 2012; final manuscript received April 30, 2013; published online August 19, 2013. Assoc. Editor: Allan Volponi.

J. Eng. Gas Turbines Power 135(9), 091701 (Aug 19, 2013) (10 pages) Paper No: GTP-12-1354; doi: 10.1115/1.4024419 History: Received September 11, 2012; Revised April 30, 2013

Data reconciliation is widely used in the chemical process industry to suppress the influence of random errors in process data and help detect gross errors. Data reconciliation is currently seeing increased use in the power industry. Here, we use data from a recently constructed cogeneration system to show the data reconciliation process and the difficulties associated with gross error detection and suspect measurement identification. Problems in gross error detection and suspect measurement identification are often traced to weak variable redundancy, which can be characterized by variable adjustability and threshold value. Proper suspect measurement identification is accomplished using a variable measurement test coupled with the variable adjustability. Cogeneration and power systems provide a unique opportunity to include performance equations in the problem formulation. Gross error detection and suspect measurement identification can be significantly enhanced by increasing variable redundancy through the use of performance equations. Cogeneration system models are nonlinear, but a detailed analysis of gross error detection and suspect measurement identification is based on model linearization. A Monte Carlo study was used to verify results from the linearized models.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.


Lefebvre, A. H., 1995, “The Role of Fuel Preparation in Low-Emission Combustion,” ASME J. Eng. Gas Turbines Power, 117(4), pp. 617–654. [CrossRef]
Kuehn, D. R., and Davidson, H., 1961, “Computer Control II. Mathematics of Control,” Chem. Eng. Prog., 57(6), pp. 44–47.
Mah, R. S. H., 1990, Chemical Process Structure and Information Flows, Butterworths, Stoneham, MA.
Madron, F., 1992, Process Plant Performance: Measurement and Data Processing for Optimization and Retrofits, Ellis Horwood, New York.
Narasimhan, S., and Jordache, C., 2000, Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data, Gulf, Houston, TX.
Romagnoli, J. A., and Sanchez, M. C., 2000, Data Processing and Reconciliation for Chemical Process Operations, Academic, London.
Veverka, V. V., 2012, “Balancing and Data Reconciliation Minibook. Report CPT-189-04,” ChemPlant Technology, accessed January 2013, http://www.chemplant.cz/dwnld.htm
Bagajewicz, M. J., 2010, Smart Process Plants. Software and Hardware Solutions for Accurate Data and Profitable Operations, McGraw-Hill, New York.
Veverka, V. V., and Madron, F., 1997, Material and Energy Balancing in the Process Industries: From Microscopic Balances to Large Plants, Elsevier, Amsterdam.
Gay, R. R., Palmer, C. A., and Erbes, M. R., 2004, Power Plant Performance Monitoring, R-Squared, Woodland, CA.
Lin, T., 2008, “An Adaptive Modeling and Simulation Environment for Combined-Cycle Data Reconciliation and Degradation Estimation,” Ph.D. thesis, Georgia Institute of Technology, Atlanta, GA.
Grönstedt, T. U. J., 2002, “Identifiability in Multi-Point Gas Turbine Parameter Estimation Problems,” Proceedings of the ASME Turbo Expo, Amsterdam, The Netherlands, June 3–6, ASME Paper No. GT2002-30020, pp. 9–17. [CrossRef]
Grodent, M., and Navez, A., 2001, “Engine Physical Diagnosis Using a Robust Parameter Estimation Method,” 37th AIAA/AASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Salt Lake City, UT, July 8–11, AIAA Paper No. 2001-3768, pp. 1–16. [CrossRef]
Peng, D. Y., and Robinson, D. B., 1976, “A New Two-Constant Equation of State,” Ind. Eng. Chem. Fundam., 15, pp. 59–64. [CrossRef]
Elliott, F. G. R., Kurz, E. C., and O'Connell, J. P., 2004, “Fuel System Stability Considerations for Industrial Gas Turbines,” ASME J. Eng. Gas Turbines Power, 126, pp. 119–126. [CrossRef]
Kandula, V. K., Telotte, J. C., and Knopf, F. C., 2013, “Its Not as Easy as it Looks: Revisiting Peng-Robinson Equation of State Convergence Issues for Dew, Bubble and Flash Calculations,” Int. J. Mech. Eng. Educ., (in press).
Huber, M. L., 2007, NIST Thermophysical Properties of Hydrocarbon Mixture Database (SUPERTRAPP) Version 3.2—User's Guide, U.S. Department of Commerce, National Institute of Standards and Technology, Gaithersburg, MD.
Lasdon, L. S., Warren, A. D., Jain, A., and Ratner, M., 1978, “Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming,” ACM Trans. Math Softw., 1(4), pp. 33–50. [CrossRef]
Chen, P., and Andersen, H. G., 2005, “The Implementation of the Data Validation Process in a Gas Turbine Performance Monitoring System,” Proceedings of the ASME Turbo Expo, Reno, NV, June 6–9, Vol. 1, ASME Paper No. GT2005-68429, pp. 609–616. [CrossRef]
VDI, 2000, “Uncertainties of Measurement During Acceptance Tests on Energy-Conversion and Power Plants-Fundamentals,” 2000, VDI–Gesellschaft Energietechnik Guideline No. 2048, Part 1.
Andersen, H. G., and Chen, P., 2005, “A New Calculation Approach to the Energy Balance of a Gas Turbine Including a Study of the Impact of the Uncertainty of Measured Parameters,” Proceedings of the ASME Turbo Expo, Reno, NV, June 6–9, Vol. 5, ASME Paper No. GT2005-68430, pp. 419–425. [CrossRef]
Buckley, R. A., 2006, “Overview of Cogeneration at LSU,” M.S. thesis, Louisiana State University, Baton Rouge, LA.
Mathioudakis, K., 2002, “Analysis of the Effects of Water Injection on the Performance of a Gas Turbine,” ASME J. Eng. Gas Turbines Power, 124, pp. 489–495. [CrossRef]
Walsh, P. P., and Fletcher, P., 2004, Gas Turbine Performance, Blackwell Science, Fairfield, NJ.
Knopf, F. C., 2012, Modeling Analysis and Optimization of Process and Energy Systems, Wiley, Hoboken, NJ.
ChemPlant Technology, 2012, “RECON: Mass, Heat and Momentum Balancing Software With Data Reconciliation,” accessed January 2013, http://www.chemplant.cz/recon.htm
Madron, F., Veverka, V., and Hostalek, M., 2007, “Process Data Validation in Practice: Applications From Chemical, Oil, Mineral and Power Industries,” ChemPlant Technology, Report No. CPT-229-07, accessed January 2013, http://www.chemplant.cz/dwnld.htm


Grahic Jump Location
Fig. 1

Gas turbine system (for description of symbols, see Nomenclature)

Grahic Jump Location
Fig. 2

Gas turbine cogeneration system—turbine system and HRSG

Grahic Jump Location
Fig. 3

Dimensionless threshold value qi,90 as function of the degree of redundancy ν and adji (for α = 0.05 and β = 0.9) (see Ref. [27])




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In