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Using generalized advanced data validation and reconciliation in steam power unit energy balancing

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Języki publikacji
EN
Abstrakty
EN
There are advantages to be gained by using a generalized method of data validation and reconciliation in energy conversion processes in terms of decreasing the uncertainty of measurements data. This method was used to complete the validation model of the process (conditional equations of optimization task) including substance and energy conservation principles with additional equations describing energy conversion processes. The methodology developed was used for example for calculations of data reconciliation in the selected steam power unit. The equations of steam flow capacity, adiabatic internal efficiency and equations resulting from the form of an isobaric line on the h-s diagram for a group of turbine stages were applied. Also applied as additional equations in the validation model were: Darcy’s equation of steam pressure drop in the pipeline into heat exchangers and Peclet’s equations of heat transfer and equations of over-cooling of condensate in regenerative heat exchangers. The criterion of an assessment of the decrease of measurements uncertainty in the form of global decrease of measurements variance after measurement data reconciliation is proposed. Derivation of the analyzed coefficient was based on the characteristic property of the measurements variance, coming from the variance-covariance matrix of measurements before and after data reconciliation. The criterion for selection of the mathematical form of additional equations in the validation model in reconciliation calculation was formulated. Professor Jan Szargut introduced and developed the advanced data validation and reconciliation method in Poland for thermodynamic analysis of energy conversion processes. The author of this paper engaged in further research on the development and application of this method in thermodynamic analyses.
Rocznik
Strony
68--84
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
autor
  • Instytut Techniki Cieplnej, Politechnika Śląska, Gliwice
Bibliografia
  • 1. Szargut J, K.Z. (1968) Theory of coordination of material and energy balances in metallurgical processes. Archives of Metallurgy, 123(2): 153.
  • 2. Szargut J, R.E. (1952) Reconciliation of mass balances. Works of the Institute of Metallurgy, 5(4).
  • 3. Szargut J, K.Z. (1967) Reconciling of mass and energy balances in chemical processes. Measurements, Automation, Control, 2(13).
  • 4. Kolenda, Z., Szmyd, J., Slupek, S., and Baez, L.M. (1983) Numerical modelling of heat transfer processes with supplementary data. The Canadian Journal of Chemical Engineering, 61 (5), 627–634.
  • 5. Kolenda Z, A.J.S. (1974) Coordination of Energy Balances in Heat Transfer. Bull. Polish Academy of Science, 6(22).
  • 6. T, S. (1991) The multistage and Multigroup Adjustment of the Measurement Results, Cracow University of Technology, Monograph.
  • 7. JT., S. (1984) Reconciliation Calculus in Thermal Engineering. Polish Academy of Sciences.
  • 8. (1998) Numerical and experimental mathematical modelling of heat and mass transfer processes using unified least squares method. Energy Conversion and Management, 39.
  • 9. Rusinowski, H., Szega, M., Szlek, A., and Wilk, R. (2002) Methods of choosing the optimal parameters for solid fuel combustion in stoker-fired boilers. Energy Conversion and Management, 43 (9-12), 1363–1375.
  • 10. Rusinowski, H., and Szega, M. (2001) The influence of the operational parameters of chamber furnaces on the consumption of the chemical energy of fuels. Energy, 26 (12), 1121–1133.
  • 11. Rusinowski H., S.M., Ziebik A. (1997) Thermal Investigations of Open-Flame Fired Furnaces in Copper Metallurgy with the Application of the Least Squares Adjustment Method. Archives of Metallurgy and Materials, 42(4).
  • 12. Almásy GA, S.T. (1975) Checking and Correction of Measurements on the Basis of Linear System Model. Problems of Control and Information Theory, 4(1).
  • 13. W., H. (1980) Error theory and advanced data validation calculation. De Gruyter Lehrbuch.
  • 14. Mikhail E, A.F. (1976) Observations and least squares. IEP-A, Dun Donnelly Publisher, New York.
  • 15. Narasimhan S, J.C. (2000) Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data. Houston, Texas: Gulf Publishing Company;.
  • 16. S., S. (1975) Application of the data validation calculation for thermal engineering experiments. Vienna University of Technology, Dissertation.
  • 17. AG., S. (1964) Adjustment of measurement results in the chemical industry. Acta IMEKO 10-NL-145.
  • 18. Veverka V, M.F. (1997) Material and Energy Balancing in the Process Industries. From Microscopic Balances to Large Plants.. Computer-Aided Chemical Engineering 7. Elsevier Science B.V.
  • 19. Bagajewicz MJ, T.D.N., Chmielewski DJ (2010) Smart Process Plants: Software and Hardware Solutions for Accurate Data and Profitable Operations. New York: McGraw-Hill Company Inc.
  • 20. MJ., B. (2001) Process Plant Instrumentation: Design and Upgrade. Technomic Publishing Company, Inc. Lancaster, Pennsylvania, USA.
  • 21. Tamhane AC, M.R.S.H. (1985) Data Reconciliation and Gross Error Detection in Chemical Process Networks. Technometrics, 27(4).
  • 22. 1, B.V.G.H.2000 P. VDI-Guidelines 2048. Uncertainties of Measurement During Acceptance Tests on Energy Conversion and Power Plants. Fundamentals.
  • 23. VDI- Guidelines 2048. Uncertainties of Measurement During Acceptance Tests on Energy Conversion and Power Plants. Examples. Duesseldorf: Beuth Verlag GmbH; 2001 Part 2..
  • 24. M., S. (2016) Advanced Data Validation and Reconciliation in Thermal Processes. Polish Academy of Sciences – Branch in Katowice. Commission of Power Engineering. Skalmierski Publishing House.
  • 25. Szargut J, S.J. (1991) Influence of the Preliminary Estimation of Unknown on the Results of Coordination of Material and Energy Balances. Bull. Pol. Acad. Sci., Techn. Series, 39(2).
  • 26. M., S. (2009) Advantages of an Application of the Generalized Method of Data Reconciliation in Thermal Technology. Archives of Thermodynamics, 4(30).
  • 27. M., S. (2015) Using data reconciliation to improve the reliability of the energy evaluation of a gas-andsteam CHP unit. Journal of Power Technologies (Polish Energy Mix 2014), 5(95).
  • 28. M., S. (2009) Application of Data Reconciliation Method for Increase of Measurements Reliability in the Power Unit System of a Steam Power Plant. Monograph No. 193. Silesian University of Technology Publisher, Gliwice.
  • 29. M., S. (2008) Problems of estimating the enthalpy value of the exhaust steam from a condensing turbine in a generalized method of reconciliation of energy balances in a steam power plant. Archives of Energetics, 1(38).
  • 30. M., S. (2007) Problems of gross errors detection in data reconciliation using principal component analysis. Proc. of the 8-th Int. Carpathian Control Conference ICCC’2007. The Slovak Republic.
  • 31. M., S. (2006) Detection of Gross Measurement Errors Using Constraints Test in Data Reconciliation Algorithm and Interval Methods. Proc. of the 7-th Int. Carpathian Control Conference ICCC’2006. Ostrava, Czech Republic.
  • 32. S., P. (1992) Steam and Gas Turbines. Polish Academy of Science, Ossolineum Publishing House, Wrocław-Warszawa-Kraków.
  • 33. M., S. (2018) Extended applications of the advanced data validation and reconciliation method in studies of energy conversion processes. Energy, 161.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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