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Performance assessment of an NH3/LiNO3 bubble plate absorber applying a semi-empirical model and artificial neural networks

  • Dades identificatives

    Identificador: imarina:8263216
    Autors:
    Amaris CAlvarez MEVallès MBourouis M
    Resum:
    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this study, ammonia vapor absorption with NH3/LiNO3 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.
  • Altres:

    Autor segons l'article: Amaris C; Alvarez ME; Vallès M; Bourouis M
    Departament: Enginyeria Mecànica
    Autor/s de la URV: Bourouis Chebata, Mahmoud / Vallès Rasquera, Joan Manel
    Paraules clau: Water Semi-empirical model Plus lithium-nitrate Plate heat exchanger Nonequilibrium phenomenological theory Nh3/lino3 Mass-transfer Lithium nitrate Heat-transfer Heat and mass transfer correlations Falling film Bubble absorption Bubble absorber Artificial neural networks Ammonia absorption process Ammonia Advanced surfaces
    Resum: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this study, ammonia vapor absorption with NH3/LiNO3 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.
    Àrees temàtiques: Zootecnia / recursos pesqueiros Renewable energy, sustainability and the environment Renewable energy, sustainability and the environm Interdisciplinar General computer science Fuel technology Engineering (miscellaneous) Engenharias iv Engenharias iii Engenharias ii Energy engineering and power technology Energy (miscellaneous) Energy & fuels Electrical and electronic engineering Economia Control and optimization Ciências ambientais Ciências agrárias i Ciência da computação Building and construction Biotecnología Biodiversidade Astronomia / física
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 19961073
    Adreça de correu electrònic de l'autor: mahmoud.bourouis@urv.cat manel.valles@urv.cat
    Identificador de l'autor: 0000-0003-2476-5967 0000-0002-0748-1287
    Data d'alta del registre: 2024-07-27
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/1996-1073/13/17/4313
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Energies. 13 (17):
    Referència de l'ítem segons les normes APA: Amaris C; Alvarez ME; Vallès M; Bourouis M (2020). Performance assessment of an NH3/LiNO3 bubble plate absorber applying a semi-empirical model and artificial neural networks. Energies, 13(17), -. DOI: 10.3390/en13174313
    DOI de l'article: 10.3390/en13174313
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2020
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Control and Optimization,Electrical and Electronic Engineering,Energy & Fuels,Energy (Miscellaneous),Energy Engineering and Power Technology,Engineering (Miscellaneous),Fuel Technology,Renewable Energy, Sustainability and the Environm,Renewable Energy, Sustainability and the Environment
    Water
    Semi-empirical model
    Plus lithium-nitrate
    Plate heat exchanger
    Nonequilibrium phenomenological theory
    Nh3/lino3
    Mass-transfer
    Lithium nitrate
    Heat-transfer
    Heat and mass transfer correlations
    Falling film
    Bubble absorption
    Bubble absorber
    Artificial neural networks
    Ammonia absorption process
    Ammonia
    Advanced surfaces
    Zootecnia / recursos pesqueiros
    Renewable energy, sustainability and the environment
    Renewable energy, sustainability and the environm
    Interdisciplinar
    General computer science
    Fuel technology
    Engineering (miscellaneous)
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Energy engineering and power technology
    Energy (miscellaneous)
    Energy & fuels
    Electrical and electronic engineering
    Economia
    Control and optimization
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Building and construction
    Biotecnología
    Biodiversidade
    Astronomia / física
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