<?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>0121-7488</journal-id>
<journal-title><![CDATA[Ciencia en Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Ciencia en Desarrollo]]></abbrev-journal-title>
<issn>0121-7488</issn>
<publisher>
<publisher-name><![CDATA[Universidad Pedagógica y Tecnológica de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-74882021000200049</article-id>
<article-id pub-id-type="doi">10.19053/01217488.v12.n2.2021.13417</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A Predictive Model for the Identification of the Volume Fraction in Two-Phase Flow]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelo predictivo para la identificación de la fracción volumétrica en flujo bifásico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruiz-Diaz]]></surname>
<given-names><![CDATA[C. M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández-Cely]]></surname>
<given-names><![CDATA[M. M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Estrada]]></surname>
<given-names><![CDATA[O. A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Industrial de Santander  ]]></institution>
<addr-line><![CDATA[Bucaramanga ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,São Carlos School of Engineering  ]]></institution>
<addr-line><![CDATA[São Carlos ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Industrial de Santander  ]]></institution>
<addr-line><![CDATA[Bucaramanga ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>12</volume>
<numero>2</numero>
<fpage>49</fpage>
<lpage>55</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-74882021000200049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-74882021000200049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-74882021000200049&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This work presents the use of artificial intelligence in multiphase flows, implementing a multilayer perceptron artificial neural network with back-propagation, and using the sigmoid tangent activation function, to generate a predictive model capable of obtaining the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe of 12 m. The artificial neural network is developed using an input layer, formed by the pressure differential in the line and the superficial velocities of the working fluids, also, it has two hidden layers and an outlet layer, which is made up of the volumetric fractions of the fluids. The best-performing predictive model shows a mean percentage absolute error of 3.07 % and a coefficient of determination R2 of 0.985 using 15 neurons in the two hidden layers of the neural network. The 56 experimental data used in the study were obtained in the laboratory LEMIEESC-USP (Brazil).]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este trabajo presenta el uso de inteligencia artificial en flujos multifásicos, implementando una red neuronal artificial de perceptrón multicapa con retropropagación, y utilizando la función de activación tangente sigmoidea, para generar un modelo predictivo capaz de obtener la fracción volumétrica de un flujo bifásico compuesto por agua y aceite mineral en una tubería horizontal de 12 m. La red neuronal artificial se desarrolla a partir de una capa de entrada, formada por el diferencial de presión en la línea y las velocidades superficiales de los fluidos de trabajo, además, tiene dos capas ocultas y una capa de salida, que está formada por las fracciones volumétricas de los fluidos. El modelo predictivo de mejor rendimiento muestra un error medio porcentual absoluto del 3,07 % y un coeficiente de determinación R 2 de 0,985 utilizando 15 neuronas en las dos capas ocultas de la red neuronal. Los 56 datos experimentales utilizados en el estudio se obtuvieron en el laboratorio LEMI EESC-USP (Brasil).]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[multiphase flow]]></kwd>
<kwd lng="en"><![CDATA[volumetric fraction]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[differential pressure]]></kwd>
<kwd lng="en"><![CDATA[surface speed]]></kwd>
<kwd lng="es"><![CDATA[flujo multifásico]]></kwd>
<kwd lng="es"><![CDATA[fracción volumétrica]]></kwd>
<kwd lng="es"><![CDATA[red neuronal artificial]]></kwd>
<kwd lng="es"><![CDATA[presión diferencial]]></kwd>
<kwd lng="es"><![CDATA[velocidad superficial]]></kwd>
</kwd-group>
</article-meta>
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