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Performance Assessment of an NH 3 /LiNO 3 Bubble Plate Absorber Applying a Semi-Empirical Model and Artificial Neural Networks

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  • Carlos Amaris

    (Department of Mechanical Engineering, Universitat Rovira i Virgili, Av. Països Catalans No. 26, 43007 Tarragona, Spain
    Department of Energy, Universidad de la Costa, Cl. 58 #55-66, Barranquilla 080002, Colombia)

  • Maria E. Alvarez

    (School of Chemical Engineering, Universidad Metropolitana, Av. Boyacá, Carcacas-Miranda 1073, Venezuela)

  • Manel Vallès

    (Department of Mechanical Engineering, Universitat Rovira i Virgili, Av. Països Catalans No. 26, 43007 Tarragona, Spain)

  • Mahmoud Bourouis

    (Department of Mechanical Engineering, Universitat Rovira i Virgili, Av. Països Catalans No. 26, 43007 Tarragona, Spain)

Abstract

In this study, ammonia vapor absorption with NH 3 /LiNO 3 was assessed using correlations derived from a semi-empirical model, and artificial neural networks (ANNs). The absorption process was studied in an H-type corrugated plate absorber working in bubble mode under the conditions of an absorption chiller machine driven by low-temperature heat sources. The semi-empirical model is based on discretized heat and mass balances, and heat and mass transfer correlations, proposed and developed from experimental data. The ANN model consists of five trained artificial neurons, six inputs (inlet flows and temperatures, solution pressure, and concentration), and three outputs (absorption mass flux, and solution heat and mass transfer coefficients). The semi-empirical model allows estimation of temperatures and concentration along the absorber, in addition to overall heat and mass transfer. Furthermore, the ANN design estimates overall heat and mass transfer without the need for internal details of the absorption phenomenon and thermophysical properties. Results show that the semi-empirical model predicts the absorption mass flux and heat flow with maximum errors of 15.8% and 12.5%, respectively. Maximum errors of the ANN model are 10.8% and 11.3% for the mass flux and thermal load, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4313-:d:401532
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    References listed on IDEAS

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    1. Amaris, Carlos & Bourouis, Mahmoud & Vallès, Manel, 2014. "Passive intensification of the ammonia absorption process with NH3/LiNO3 using carbon nanotubes and advanced surfaces in a tubular bubble absorber," Energy, Elsevier, vol. 68(C), pages 519-528.
    2. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    3. 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.
    4. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    5. 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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