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Methodology of advanced data validation and reconciliation application in industrial thermal processes

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  • Szega, Marcin

Abstract

Data validation and reconciliation is a technique that uses process information and mathematical methods in order to correct measurements in thermal industrial processes. On the need to measurements data validation and reconciliation (DVR) in order to use them for further calculations is indicated in the Association of German Engineers guidelines – VDI 2048. Moreover, the necessity of removing measurement errors, in the international standard for the integration of enterprise and control systems ISA-95 is recommended. The paper presents in a comprehensive manner methodology of advanced DVR application in computer systems supporting supervising of the industrial thermal processes. The necessity of preliminary measurement data validation has been indicated. The problems of detection and identification of measurements with a gross error were presented. A method that enables the detection of steady and unsteady states in thermal processes has been shown. The statistical tests for control the assumed accuracy of measurements after advanced DVR calculations have been displayed. A comprehensive analysis of the usefulness of these statistical tests used in the detection and identification of gross measurement errors in the advanced DVR method was carried out. The presented computational example illustrating the use of statistical methods for the detection and identification of gross measurement errors clearly shows that the use of these methods does not solve the problem in the case of advanced DVR tasks with the non-linear form of conditional equations. In this case, the additional methods presented in the article must be used. An overall methodology of applying the generalized advanced DVR method in distributed control systems of thermal processes in the industry was developed. Based on that, a block diagram of the worked-out methodology of the application mentioned DVR method in the industrial thermal processes has been elaborated. The developed overall methodology and aggregated block diagram enable an integrated approach to the generalized advanced DVR method in industrial thermal processes.

Suggested Citation

  • Szega, Marcin, 2020. "Methodology of advanced data validation and reconciliation application in industrial thermal processes," Energy, Elsevier, vol. 198(C).
  • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304333
    DOI: 10.1016/j.energy.2020.117326
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    References listed on IDEAS

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    1. Szega, Marcin, 2017. "An improvement of measurements reliability in thermal processes by application of the advanced data reconciliation method with the use of fuzzy uncertainties of measurements," Energy, Elsevier, vol. 141(C), pages 2490-2498.
    2. Szega, Marcin, 2018. "Extended applications of the advanced data validation and reconciliation method in studies of energy conversion processes," Energy, Elsevier, vol. 161(C), pages 156-171.
    3. Szega, Marcin & Czyż, Tomasz, 2019. "Problems of calculation the energy efficiency of a dual-fuel steam boiler fired with industrial waste gases," Energy, Elsevier, vol. 178(C), pages 134-144.
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    Cited by:

    1. Yu, Jianxi & Han, Wenquan & Chen, Kang & Liu, Pei & Li, Zheng, 2022. "Gross error detection in steam turbine measurements based on data reconciliation of inequality constraints," Energy, Elsevier, vol. 253(C).
    2. Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
    3. Tatarczuk, Adam & Szega, Marcin & Zuwała, Jarosław, 2023. "Thermodynamic analysis of a post-combustion carbon dioxide capture process in a pilot plant equipped with a heat integrated stripper," Energy, Elsevier, vol. 278(PA).
    4. Oleg Todorov & Kari Alanne & Markku Virtanen & Risto Kosonen, 2021. "A Novel Data Management Methodology and Case Study for Monitoring and Performance Analysis of Large-Scale Ground Source Heat Pump (GSHP) and Borehole Thermal Energy Storage (BTES) System," Energies, MDPI, vol. 14(6), pages 1-25, March.
    5. Szega, Marcin & Żymełka, Piotr & Janda, Tomasz, 2022. "Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation," Energy, Elsevier, vol. 239(PE).
    6. Michał Kozioł & Joachim Kozioł, 2021. "Application of Data Validation and Reconciliation to Improve Measurement Results in the Determination Process of Emission Characteristics in Co-Combustion of Sewage Sludge with Coal," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    7. Eslick, John C. & Zamarripa, Miguel A. & Ma, Jinliang & Wang, Maojian & Bhattacharya, Indrajit & Rychener, Brian & Pinkston, Philip & Bhattacharyya, Debangsu & Zitney, Stephen E. & Burgard, Anthony P., 2022. "Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application," Applied Energy, Elsevier, vol. 319(C).

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