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Modelling the performance parameters of a horizontal falling film absorber with aqueous (lithium, potassium, sodium) nitrate solution using artificial neural networks

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  • Álvarez, María E.
  • Hernández, José A.
  • Bourouis, Mahmoud

Abstract

An ANN (artificial neural network) model was developed to determine the efficiency parameters of a horizontal falling film absorber at operating conditions of interest for absorption cooling systems. The aqueous nitrate solution LiNO3+KNO3+NaNO3 with salt mass percentages of 53%, 28% and 19%, respectively, was used as a working fluid. The authors created the ANN from the database they had compiled with the results of experiments that they had performed in a set-up designed and built for this purpose. The ANN structure consisted of 6 input variables: inlet solution and cooling water temperatures, cooling water and solution mass flow rates, absorber pressure and inlet solution concentration; 4 output variables which facilitated the assessment of the performance of the absorber: heat and mass transfer coefficients, absorption mass flux and the degree of subcooling of the solution leaving the absorber. The hidden layer contained 9 neurons which were determined by training and test procedures. The results showed that the deviation between the experimental data and the estimated values was well adjusted. This indicated that the ANN model was an effective tool for predicting the efficiency parameters of the absorber. The solution flow rate was also observed to be the most significant operating variable which affected the performance of the absorber.

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  • 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.
    • Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:313-323
    DOI: 10.1016/j.energy.2016.02.022
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    2. Şencan, Arzu & Yakut, Kemal A. & Kalogirou, Soteris A., 2006. "Thermodynamic analysis of absorption systems using artificial neural network," Renewable Energy, Elsevier, vol. 31(1), pages 29-43.
    3. 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.
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    Cited by:

    1. María E. Álvarez & Mahmoud Bourouis, 2021. "Modelling of Coupled Heat and Mass Transfer in a Water-Cooled Falling-Film Absorber Working with an Aqueous Alkaline Nitrate Solution," Energies, MDPI, vol. 14(7), pages 1-23, March.
    2. Sui, Zengguang & Wu, Wei, 2022. "A comprehensive review of membrane-based absorbers/desorbers towards compact and efficient absorption refrigeration systems," Renewable Energy, Elsevier, vol. 201(P1), pages 563-593.
    3. Carlos Amaris & Maria E. Alvarez & Manel Vallès & Mahmoud Bourouis, 2020. "Performance Assessment of an NH 3 /LiNO 3 Bubble Plate Absorber Applying a Semi-Empirical Model and Artificial Neural Networks," Energies, MDPI, vol. 13(17), pages 1-20, August.
    4. Amaris, Carlos & Vallès, Manel & Bourouis, Mahmoud, 2018. "Vapour absorption enhancement using passive techniques for absorption cooling/heating technologies: A review," Applied Energy, Elsevier, vol. 231(C), pages 826-853.

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