Performance Assessment of an NH 3 /LiNO 3 Bubble Plate Absorber Applying a Semi-Empirical Model and Artificial Neural Networks
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- 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.
- 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.
- Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
- 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.
- 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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Keywords
bubble absorption; plate heat exchanger; advanced surfaces; heat and mass transfer correlations; semi-empirical model; artificial neural networks; ammonia; lithium nitrate;All these keywords.
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