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A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

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  • Rossi, Francesco
  • Velázquez, David

Abstract

A methodology is proposed to assess the savings achieved from the implementation of ECMs (energy conservation measures) in industrial plants with application for a combined cycle cogeneration plant. The analysis begins with the study of the operation of the system and a qualitative assessment of the changes resulting from the ECMs in the plant. The control volume is subsequently selected with the objective of minimising and focusing the efforts exclusively on the areas of the system actually influenced by the ECMs. An ANN (artificial neural networks) approach is proposed for the modelling stage and it is used to mimic the production and consumption of the system during the post-retrofit period in its configuration prior to the changes. Special attention is given to the selection of the variables used as predictors in the developed models, as well as to the determination of the relative influence of the inputs on the final models. Finally, the savings are calculated, and a critical analysis of the results is presented based on the comparison with the predictions obtained from the preliminary qualitative assessment.

Suggested Citation

  • Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:528-544
    DOI: 10.1016/j.energy.2015.06.016
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    as
    1. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    2. Yap, Wai Kean & Karri, Vishy, 2015. "An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques," Renewable Energy, Elsevier, vol. 78(C), pages 42-50.
    3. Rashidi, M.M. & Galanis, N. & Nazari, F. & Basiri Parsa, A. & Shamekhi, L., 2011. "Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network," Energy, Elsevier, vol. 36(9), pages 5728-5740.
    4. Olanrewaju, O.A & Jimoh, A.A, 2014. "Review of energy models to the development of an efficient industrial energy model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 661-671.
    5. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    6. Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    7. Suresh, M.V.J.J. & Reddy, K.S. & Kolar, Ajit Kumar, 2011. "ANN-GA based optimization of a high ash coal-fired supercritical power plant," Applied Energy, Elsevier, vol. 88(12), pages 4867-4873.
    8. Dalgleish, A.Z & Grobler, L.J, 2003. "Measurement and verification of a motor sequencing controller on a conveyor belt," Energy, Elsevier, vol. 28(9), pages 913-927.
    9. Cortés, O. & Urquiza, G. & Hernández, J.A., 2009. "Optimization of operating conditions for compressor performance by means of neural network inverse," Applied Energy, Elsevier, vol. 86(11), pages 2487-2493, November.
    10. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    11. Joly, R. B. & Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2004. "Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine," Applied Energy, Elsevier, vol. 78(4), pages 397-418, August.
    12. Bekat, Tugce & Erdogan, Muharrem & Inal, Fikret & Genc, Ayten, 2012. "Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks," Energy, Elsevier, vol. 45(1), pages 882-887.
    13. Mariani, F. & Grimaldi, C.N. & Battistoni, M., 2014. "Diesel engine NOx emissions control: An advanced method for the O2 evaluation in the intake flow," Applied Energy, Elsevier, vol. 113(C), pages 576-588.
    14. Jiang, Lian Lian & Nayanasiri, D.R. & Maskell, Douglas L. & Vilathgamuwa, D.M., 2015. "A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics," Renewable Energy, Elsevier, vol. 76(C), pages 53-65.
    15. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    16. Prieto, M.M & Montañés, E & Menéndez, O, 2001. "Power plant condenser performance forecasting using a non-fully connected artificial neural network," Energy, Elsevier, vol. 26(1), pages 65-79.
    17. Zhou, Hao & Cen, Kefa & Fan, Jianren, 2004. "Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks," Energy, Elsevier, vol. 29(1), pages 167-183.
    18. Kelly Kissock, J. & Eger, Carl, 2008. "Measuring industrial energy savings," Applied Energy, Elsevier, vol. 85(5), pages 347-361, May.
    19. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    20. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    21. Taghavifar, Hamid & Mardani, Aref, 2014. "Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices," Energy, Elsevier, vol. 68(C), pages 651-657.
    22. Wang, Jiangfeng & Sun, Zhixin & Dai, Yiping & Ma, Shaolin, 2010. "Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network," Applied Energy, Elsevier, vol. 87(4), pages 1317-1324, April.
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