<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432019000100007</article-id>
<article-id pub-id-type="doi">10.22201/fi.25940732e.2019.20n1.007</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Performance estimation and optimization of an adiabatic H2O-Libr absorption system using artificial neural networks]]></article-title>
<article-title xml:lang="es"><![CDATA[Estimación de desempeño y optimización de un sistema de absorción adiabático H2O-LiBr usando redes neuronales artificiales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gutiérrez-Urueta]]></surname>
<given-names><![CDATA[Geydy Luz]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Colorado]]></surname>
<given-names><![CDATA[Darío]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández]]></surname>
<given-names><![CDATA[José Alfredo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez-Aumente]]></surname>
<given-names><![CDATA[Pedro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rivera]]></surname>
<given-names><![CDATA[Wilfrido]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de San Luis Potosí Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[San Luis Potosí ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Veracruzana Centro de Investigación en Recursos Energéticos y Sustentables ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma del Estado de Morelos Centro de Investigación en Ingeniería y Ciencias Aplicadas ]]></institution>
<addr-line><![CDATA[Cuernavaca ]]></addr-line>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Carlos III de Madrid Departamento de Ingeniería Térmica y de Fluidos ]]></institution>
<addr-line><![CDATA[Leganés Madrid]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Nacional Autónoma de México Instituto de Energías Renovables ]]></institution>
<addr-line><![CDATA[Morelos ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<volume>20</volume>
<numero>1</numero>
<fpage>0</fpage>
<lpage>0</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432019000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432019000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432019000100007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract 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&gt; 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.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La búsqueda de alternativas para frenar el cambio climático y sus consecuencias devastadoras para la sociedad actual, conduce a la investigación de sistemas climáticos respetuosos con el medio ambiente. Para optimizarlos o controlarlos, las redes neuronales artificiales (ANN) se consideran una opción efectiva. La absorción adiabática se basa en un diseño separado para el proceso de transferencia de calor y masa con el fin de reducir el tamaño del equipo. Este estudio trata la aplicación de redes neuronales artificiales (RNA) sobre los resultados experimentales de un sistema de absorción simple efecto agua- LiBr y su optimización utilizando una red neuronal inversa. Se usaron datos tanto en estado transitorio como estacionario para obtener tres modelos empíricos. Los modelos desarrollados corresponden al coeficiente de rendimiento (COP en inglés), potencia de refrigeración y de generación de la instalación. Las estadísticas de estado estable consisten en 219 puntos experimentales obtenidos en diferentes condiciones de operación. Estos datos se utilizaron para entrenar y probar los modelos de estado estacionario y transitorios de ANN. Para las estadísticas transitorias, se consideraron 1445 valores para un período. En el conjunto de datos de validación, los resultados mostraron que las simulaciones y datos experimentales se ajustan con un R&gt; 0.98 para ambos modelos, transitorio y estable. Se obtuvo un modelo para el COP, con base en la accesibilidad de los datos, incluyendo temperaturas de los circuitos de fluido externos con buenos resultados. El modelo de red neuronal inversa aplicado a los datos transitorios demostró resultados satisfactorios, haciendo posible la optimización de la instalación. Estos resultados ilustran la idoneidad del uso de una RNA con datos transitorios en sistemas de absorción, lo que es especialmente atractivo para aplicaciones de refrigeración solar.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Adiabatic absorption]]></kwd>
<kwd lng="en"><![CDATA[water-lithium bromide]]></kwd>
<kwd lng="en"><![CDATA[absorption systems]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[performance estimation]]></kwd>
<kwd lng="en"><![CDATA[optimization.]]></kwd>
<kwd lng="es"><![CDATA[Absorción adiabática]]></kwd>
<kwd lng="es"><![CDATA[agua-Bromuro de litio]]></kwd>
<kwd lng="es"><![CDATA[sistemas de absorción]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[estimación de desempeño]]></kwd>
<kwd lng="es"><![CDATA[optimización]]></kwd>
</kwd-group>
</article-meta>
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