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Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse

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  • Hernández, J.A.
  • Bassam, A.
  • Siqueiros, J.
  • Juárez-Romero, D.

Abstract

Artificial neural network inverse (ANNi) is applied to calculate the optimal operating conditions on the coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling. An artificial neural network (ANN) model is developed to predict the COP which was increased with energy recycling. This ANN model takes into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two pressures and LiBr+H2O concentrations. For the network, a feedforward with one hidden layer, a Levenberg–Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validation data set, simulations and experimental data test were in good agreement (R>0.99). This ANN model can be used to predict the COP when the input variables (operating conditions) are well known. However, to control the COP in the system, we developed a strategy to estimate the optimal input variables when a COP is required from ANNi. An optimization method (the Nelder–Mead simplex method) is used to fit the unknown input variable resulted from the ANNi. This methodology can be applied to control on-line the performance of the system.

Suggested Citation

  • Hernández, J.A. & Bassam, A. & Siqueiros, J. & Juárez-Romero, D., 2009. "Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse," Renewable Energy, Elsevier, vol. 34(4), pages 1084-1091.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:4:p:1084-1091
    DOI: 10.1016/j.renene.2008.07.004
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    References listed on IDEAS

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    1. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Yücesu, Serdar, 2005. "Performance parameters of an ejector-absorption heat transformer," Applied Energy, Elsevier, vol. 80(3), pages 273-289, March.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    3. Sözen, Adnan & Özalp, Mehmet, 2005. "Solar-driven ejector-absorption cooling system," Applied Energy, Elsevier, vol. 80(1), pages 97-113, January.
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    1. Parham, Kiyan & Khamooshi, Mehrdad & Tematio, Daniel Boris Kenfack & Yari, Mortaza & Atikol, Uğur, 2014. "Absorption heat transformers – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 430-452.
    2. 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.
    3. 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.
    4. Álvarez, María E. & Hernández, José A. & Bourouis, Mahmoud, 2016. "Modelling the performance parameters of a horizontal falling film absorber with aqueous (lithium, potassium, sodium) nitrate solution using artificial neural networks," Energy, Elsevier, vol. 102(C), pages 313-323.
    5. 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.
    6. Colorado, D. & Hernández, J.A. & El Hamzaoui, Y. & Bassam, A. & Siqueiros, J. & Andaverde, J., 2011. "Error propagation on COP prediction by artificial neural network in a water purification system integrated to an absorption heat transformer," Renewable Energy, Elsevier, vol. 36(5), pages 1315-1322.

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