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Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse

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  • Colorado, D.
  • Hernández, J.A.
  • Rivera, W.
  • Martínez, H.
  • Juárez, D.

Abstract

Analysis based on first and second law of thermodynamics together with direct and artificial neural networks inverse (ANNi) have been used to develop a methodology to decrease the total irreversibility of an experimental single-stage heat transformer. With the proposed methodology it is possible to calculate the optimal input parameters that should be used in order to operate the heat transformer with the lower irreversibilities. Mathematical validation of ANNi was carried out together with the comparison between the total cycle irreversibility (Icycle) obtained thermodynamically and the Icycle determined by using the ANNi. The results showed a mean discrepancy of 0.9% of the Icycle values. The proposed new methodology can be very useful to control on-line the performance of a single-state heat transformer obtaining lower Icycle values.

Suggested Citation

  • Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:4:p:1281-1290
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Wenyu & Wu, Jie & Wang, Jianzhou & Zhao, Weigang & Shen, Lin, 2012. "Performance analysis of four modified approaches for wind speed forecasting," Applied Energy, Elsevier, vol. 99(C), pages 324-333.
    3. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    4. Parrales, Arianna & Colorado, Dario & Huicochea, Armando & Díaz, Juan & Alfredo Hernández, J., 2014. "Void fraction correlations analysis and their influence on heat transfer of helical double-pipe vertical evaporator," Applied Energy, Elsevier, vol. 127(C), pages 156-165.
    5. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    6. Donnellan, Philip & Cronin, Kevin & Byrne, Edmond, 2015. "Recycling waste heat energy using vapour absorption heat transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1290-1304.
    7. Labus, J. & Hernández, J.A. & Bruno, J.C. & Coronas, A., 2012. "Inverse neural network based control strategy for absorption chillers," Renewable Energy, Elsevier, vol. 39(1), pages 471-482.
    8. Donnellan, Philip & Byrne, Edmond & Oliveira, Jorge & Cronin, Kevin, 2014. "First and second law multidimensional analysis of a triple absorption heat transformer (TAHT)," Applied Energy, Elsevier, vol. 113(C), pages 141-151.

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