Performance estimation and optimization of an adiabatic H2O-Libr absorption system using artificial neural networks
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Universidad Nacional Autónoma de México
2019
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| _version_ | 1876425642645389312 |
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| author | Geydy Luz Gutiérrez-Urueta |
| author_facet | Geydy Luz Gutiérrez-Urueta |
| contents | Performance estimation and optimization of an adiabatic H2O-Libr absorption system using artificial neural networks Geydy Luz Gutiérrez-Urueta Darío Colorado José Alfredo Hernández Pedro Rodríguez-Aumente Wilfrido Rivera Ingeniería water optimization lithium bromide absorption systems Adiabatic absorption The search for alternatives to curb climate change and its devastating consequences for today's society, leads to research environmentally friendly climate systems. To optimize or control them, artificial neural networks (ANN) is considered an effective option. Adiabatic absorption is based on separate design for heat and mass transfer process in order to reduce the size of equipment. This study deals with the application of ANN on the experimental results of a single effect water-lithium bromide adiabatic absorption facility and its optimization using an inverse ANN. Transient and steady state data were used to obtain three empirical models. The models developed correspond to the coefficient of performance (COP), cooling power and generation power of the facility. Steady state statistics consists of 219 experimental points obtained at different operating conditions. These data were used to train and test the steady state and transient ANN models. For transient statistics, 1445 values were considered for a period. In the validation data set, the results showed that simulations and the experimental data were in good agreement with an R > 0.98 for both steady state and transient models. A model for COP, based on the principle of accessibility of data, was developed including temperatures for the external fluid circuits with good results. The inverse neural model applied to transient data demonstrated satisfactory results as well, making possible the optimization of the facility. These results illustrate the adequacy in using an ANN with transient data in absorption systems, making it especially attractive for solar cooling applications. 2019 artículo científico 1405-7743 https://www.redalyc.org/articulo.oa?id=40465050007 https://www.redalyc.org/journal/404/40465050007/ https://www.redalyc.org/journal/404/40465050007/html/ https://www.redalyc.org/journal/404/40465050007/40465050007.epub https://www.redalyc.org/journal/404/40465050007/movil 10.22201/fi.25940732e.2019.20n1.007 en http://www.redalyc.org/revista.oa?id=404 Ingeniería. Investigación y Tecnología application/pdf Universidad Nacional Autónoma de México Ingeniería. Investigación y Tecnología (México) Num.1 Vol.XX |
| format | Artículo científico |
| id | redalyc_40465050007 |
| institution | Redalyc |
| language | en |
| publishDate | 2019 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Performance estimation and optimization of an adiabatic H2O-Libr absorption system using artificial neural networks Geydy Luz Gutiérrez-Urueta Ingeniería water optimization lithium bromide absorption systems Adiabatic absorption Performance estimation and optimization of an adiabatic H2O-Libr absorption system using artificial neural networks Geydy Luz Gutiérrez-Urueta Darío Colorado José Alfredo Hernández Pedro Rodríguez-Aumente Wilfrido Rivera Ingeniería water optimization lithium bromide absorption systems Adiabatic absorption The search for alternatives to curb climate change and its devastating consequences for today's society, leads to research environmentally friendly climate systems. To optimize or control them, artificial neural networks (ANN) is considered an effective option. Adiabatic absorption is based on separate design for heat and mass transfer process in order to reduce the size of equipment. This study deals with the application of ANN on the experimental results of a single effect water-lithium bromide adiabatic absorption facility and its optimization using an inverse ANN. Transient and steady state data were used to obtain three empirical models. The models developed correspond to the coefficient of performance (COP), cooling power and generation power of the facility. Steady state statistics consists of 219 experimental points obtained at different operating conditions. These data were used to train and test the steady state and transient ANN models. For transient statistics, 1445 values were considered for a period. In the validation data set, the results showed that simulations and the experimental data were in good agreement with an R > 0.98 for both steady state and transient models. A model for COP, based on the principle of accessibility of data, was developed including temperatures for the external fluid circuits with good results. The inverse neural model applied to transient data demonstrated satisfactory results as well, making possible the optimization of the facility. These results illustrate the adequacy in using an ANN with transient data in absorption systems, making it especially attractive for solar cooling applications. 2019 artículo científico 1405-7743 https://www.redalyc.org/articulo.oa?id=40465050007 https://www.redalyc.org/journal/404/40465050007/ https://www.redalyc.org/journal/404/40465050007/html/ https://www.redalyc.org/journal/404/40465050007/40465050007.epub https://www.redalyc.org/journal/404/40465050007/movil 10.22201/fi.25940732e.2019.20n1.007 en http://www.redalyc.org/revista.oa?id=404 Ingeniería. Investigación y Tecnología application/pdf Universidad Nacional Autónoma de México Ingeniería. Investigación y Tecnología (México) Num.1 Vol.XX |
| title | Performance estimation and optimization of an adiabatic H2O-Libr absorption system using artificial neural networks |
| topic | Ingeniería water optimization lithium bromide absorption systems Adiabatic absorption |
| url | https://www.redalyc.org/articulo.oa?id=40465050007 https://www.redalyc.org/journal/404/40465050007/ https://www.redalyc.org/journal/404/40465050007/html/ https://www.redalyc.org/journal/404/40465050007/40465050007.epub https://www.redalyc.org/journal/404/40465050007/movil |