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Power plant condenser performance forecasting using a non-fully connected artificial neural network

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  • Prieto, M.M
  • Montañés, E
  • Menéndez, O

Abstract

This paper presents a model that uses non-fully connected Feedforward Artificial Neural Networks (FANNs) for the forecasting of a seawater-refrigerated power plant condenser performance using the heat transfer rate (Q̇), the heat transfer coefficient (U) and the cleanliness factor (FC). The model developed includes FANNs that take into account the previous temporal values of the most important variables for obtaining the condenser performance, in order to forecast the next temporal value, as well as FANNs that relate the forecasted values with the corresponding condenser performance values Q̇, U and FC. In FANN architectures, the physical relationships between variables were taken into account. To analyze the model's performance, different ways of grouping data were used: high tide and low tide, right side water box and left side water box of the condenser and time step (daily and every three days). The errors in the test stage for Q̇, U and FC were acceptable, being less than 0.5% for Q̇, around 4% for U and around 2% for FC. The errors in the forecasting stage for U and FC increased with respect to the test stage.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:26:y:2001:i:1:p:65-79
    DOI: 10.1016/S0360-5442(00)00046-3
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    Cited by:

    1. 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.
    2. 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.
    3. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    4. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
    5. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2009. "Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks," Applied Energy, Elsevier, vol. 86(9), pages 1442-1449, September.
    6. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    7. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.

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