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

Carlos Amaris, Maria E. Alvarez, Manel Vallès and Mahmoud Bourouis
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Carlos Amaris: Department of Mechanical Engineering, Universitat Rovira i Virgili, Av. Països Catalans No. 26, 43007 Tarragona, Spain
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

Energies, 2020, vol. 13, issue 17, 1-20

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.

Keywords: bubble absorption; plate heat exchanger; advanced surfaces; heat and mass transfer correlations; semi-empirical model; artificial neural networks; ammonia; lithium nitrate (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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