<?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>1815-5901</journal-id>
<journal-title><![CDATA[Ingeniería Energética]]></journal-title>
<abbrev-journal-title><![CDATA[Energética]]></abbrev-journal-title>
<issn>1815-5901</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59012020000100006</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Red neuronal con entrenamiento dinámico para simulación de un sistema de intercambiadores de calor]]></article-title>
<article-title xml:lang="en"><![CDATA[Neural network with dynamic training for simulation of a heat exchangers system]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez Escalona]]></surname>
<given-names><![CDATA[Andres Adrian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Góngora Leyva]]></surname>
<given-names><![CDATA[Ever]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Camaraza Medina]]></surname>
<given-names><![CDATA[Yanan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Superior Minero Metalúrgico  ]]></institution>
<addr-line><![CDATA[Moa Holguín]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Central Marta Abreu de las Villa  ]]></institution>
<addr-line><![CDATA[ Villa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<volume>41</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59012020000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59012020000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59012020000100006&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Esta investigación propuso un modelo de red neuronal artificial con entrenamiento dinámico para predecir las temperaturas de salida de ambos fluidos en un sistema de intercambiadores de calor para monoetanolamina, realizando el entrenamiento, validación y pruebas con 31 680 juegos de datos obtenidos a través del método de experimentación pasiva. Con el perceptrón multicapa 4-3-2 se lograron correlaciones superiores al 98,76 %, y se corroboró que el entrenamiento dinámico proporciona respuestas más precisas que el entrenamiento único. Aplicando el primer enfoque se calcularon errores absolutos promedio de 0,419 y 0,372 K (para las variables temperatura de salida de la amina rica y de la amina pobre, respectivamente), comparados con 2,214 y 1,181 K para el segundo. Tales desviaciones no tienen una implicación significativa en el proceso tecnológico examinado, por lo que el modelo se considera apropiado para simular el desempeño de los intercambiadores de calor objeto de estudio.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This research proposed an artificial neural network model with dynamic training in order to predict both fluids outlet temperatures on a monoethanolamine heat exchangers system, doing the training, validation and testing with 31 680 data points gathered through the passive experimentation method. The 4-3-2 multilayer perceptron achieved correlations above 98.76 %, and it was corroborated that dynamic training strategy provided more accurate results than single training. Mean absolute errors of 0.419 and 0.372 K were obtained when applying the first approach (for rich amine and lean amine outlet temperatures as output variables, respectively), as compared to 2.214 and 1.181 K when implementing the second one. Since calculated deviations have no significant implication on the technological process under analysis, proposed model is considered appropriate for simulation of the studied heat exchangers performance.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[intercambiador de calor]]></kwd>
<kwd lng="es"><![CDATA[monoetanolamina]]></kwd>
<kwd lng="es"><![CDATA[red neuronal artificial]]></kwd>
<kwd lng="es"><![CDATA[modelación]]></kwd>
<kwd lng="es"><![CDATA[entrenamiento]]></kwd>
<kwd lng="en"><![CDATA[heat exchange]]></kwd>
<kwd lng="en"><![CDATA[monoethanolamine]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[modeling]]></kwd>
<kwd lng="en"><![CDATA[training]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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