Articles producció científica> Enginyeria Mecànica

Modelling the performance parameters of a horizontal falling film absorber with aqueous (lithium, potassium, sodium) nitrate solution using artificial neural networks

  • Dades identificatives

    Identificador: imarina:5129736
    Autors:
    Alvarez, Maria E.Hernandez, Jose A.Bourouis, Mahmoud
    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.
  • Altres:

    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
  • Paraules clau:

    Building and Construction,Civil and Structural Engineering,Electrical and Electronic Engineering,Energy & Fuels,Energy (Miscellaneous),Energy Engineering and Power Technology,Engineering, Chemical,Fuel Technology,Industrial and Manufacturing Engineering,Management, Monitoring, Policy and Law,Mechanical Engineering,Modeling and Simulation,P
    Triple-effect absorption cooling cycle
    Performance parameters
    Horizontal falling film absorber
    Falling film absorber
    Artificial neural network
    Aqueous nitrate solutions
    Aqueous nitrate solution
    Alkitrate
    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
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