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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

knopf@lsu.edu

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.

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Figures

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])

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