Autor segons l'article: Alvarez, Maria E.; Hernandez, Jose A.; Bourouis, Mahmoud
Departament: Enginyeria Mecànica
Autor/s de la URV: Bourouis Chebata, Mahmoud
Paraules clau: Triple-effect absorption cooling cycle Performance parameters Horizontal falling film absorber Falling film absorber Artificial neural network Aqueous nitrate solutions Aqueous nitrate solution Alkitrate
Resum: 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: mahmoud.bourouis@urv.cat
Identificador de l'autor: 0000-0003-2476-5967
Data d'alta del registre: 2024-09-07
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S0360544216300640
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Energy. 102 313-323
Referència de l'ítem segons les normes 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
DOI de l'article: 10.1016/j.energy.2016.02.022
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2016
Tipus de publicació: Journal Publications