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Energy efficiency assessment and improvement in energy intensive systems through thermoeconomic diagnosis of the operation

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  • Usón, Sergio
  • Valero, Antonio
  • Correas, Luis

Abstract

Advanced monitoring techniques can play a key role in improving energy efficiency of operating energy intensive systems. In particular, thermoeconomic diagnosis aims at the determination of fuel consumption variation, the identification of causes of its increment from design conditions and the quantification of the effect of each one of these causes. A thermoeconomic diagnosis system installed in a coal-fired power plant has been used to analyse its operation during a time span of more than 6 years, quantifying the effects of variations in components (degradation, repairing and substitution), fuel quality, ambient conditions and operation strategy. The diagnosis method proposed (quantitative causality analysis) provides a precision of ±3% in addressing the source of inefficiency for about 70% of the cases.

Suggested Citation

  • Usón, Sergio & Valero, Antonio & Correas, Luis, 2010. "Energy efficiency assessment and improvement in energy intensive systems through thermoeconomic diagnosis of the operation," Applied Energy, Elsevier, vol. 87(6), pages 1989-1995, June.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:6:p:1989-1995
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    References listed on IDEAS

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    Cited by:

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    2. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants," Applied Energy, Elsevier, vol. 134(C), pages 270-282.
    3. Blanco, Jesús M. & Vazquez, L. & Peña, F., 2012. "Investigation on a new methodology for thermal power plant assessment through live diagnosis monitoring of selected process parameters; application to a case study," Energy, Elsevier, vol. 42(1), pages 170-180.
    4. Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Data reconciliation for the overall thermal system of a steam turbine power plant," Applied Energy, Elsevier, vol. 165(C), pages 1037-1051.
    5. Orozco, Dimas José Rúa & Venturini, Osvaldo José & Escobar Palacio, José Carlos & del Olmo, Oscar Almazán, 2017. "A new methodology of thermodynamic diagnosis, using the thermoeconomic method together with an artificial neural network (ANN): A case study of an externally fired gas turbine (EFGT)," Energy, Elsevier, vol. 123(C), pages 20-35.
    6. Usón, Sergio & Valero, Antonio, 2011. "Thermoeconomic diagnosis for improving the operation of energy intensive systems: Comparison of methods," Applied Energy, Elsevier, vol. 88(3), pages 699-711, March.
    7. Blanco, J.M. & Vazquez, L. & Peña, F. & Diaz, D., 2013. "New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants," Applied Energy, Elsevier, vol. 101(C), pages 589-599.
    8. Honma, Satoshi & Hu, Jin-Li, 2014. "Industry-level total-factor energy efficiency in developed countries: A Japan-centered analysis," Applied Energy, Elsevier, vol. 119(C), pages 67-78.

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