Author, as appears in the article.: Alvarez, Maria E.; Hernandez, Jose A.; Bourouis, Mahmoud
Department: Enginyeria Mecànica
URV's Author/s: Bourouis Chebata, Mahmoud
Keywords: Triple-effect absorption cooling cycle Performance parameters Horizontal falling film absorber Falling film absorber Artificial neural network Aqueous nitrate solutions Aqueous nitrate solution Alkitrate
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.
Thematic Areas: Thermodynamics Renewable energy, sustainability and the environment Química Pollution Modeling and simulation Medicina iii Medicina ii Mechanical engineering Materiais Management, monitoring, policy and law Interdisciplinar Industrial and manufacturing engineering Geografía Geociências General energy Fuel technology Engineering, chemical Engenharias iv Engenharias iii Engenharias ii Engenharias i Energy engineering and power technology Energy (miscellaneous) Energy (all) Energy & fuels Electrical and electronic engineering Economia Civil and structural engineering Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Building and construction Biotecnología Biodiversidade Administração pública e de empresas, ciências contábeis e turismo
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: mahmoud.bourouis@urv.cat
Author identifier: 0000-0003-2476-5967
Record's date: 2024-09-07
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S0360544216300640
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Energy. 102 313-323
APA: Alvarez, Maria E.; Hernandez, Jose 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, 102(), 313-323. DOI: 10.1016/j.energy.2016.02.022
Article's DOI: 10.1016/j.energy.2016.02.022
Entity: Universitat Rovira i Virgili
Journal publication year: 2016
Publication Type: Journal Publications