<?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>2007-2422</journal-id>
<journal-title><![CDATA[Tecnología y ciencias del agua]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnol. cienc. agua]]></abbrev-journal-title>
<issn>2007-2422</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Tecnología del Agua, Coordinación de Comunicación, Participación e Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-24222020000600339</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2020-06-08</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Prediciendo la dosis de sulfato de aluminio en el tratamiento de aguas]]></article-title>
<article-title xml:lang="en"><![CDATA[Predicting the aluminum sulfate dosage in water treatment]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morales]]></surname>
<given-names><![CDATA[Ana María]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez-Caballero]]></surname>
<given-names><![CDATA[Gustavo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Barajas-Meneses]]></surname>
<given-names><![CDATA[Martha]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Industrial de Santander Escuela de Ingeniería Química ]]></institution>
<addr-line><![CDATA[Bucaramanga ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Industrial de Santander Escuela de Ingeniería Química ]]></institution>
<addr-line><![CDATA[Bucaramanga ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Industrial de Santander Escuela de Ingeniería Química ]]></institution>
<addr-line><![CDATA[Bucaramanga ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>11</volume>
<numero>6</numero>
<fpage>339</fpage>
<lpage>367</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222020000600339&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222020000600339&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222020000600339&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El presente estudio muestra las estrategias usadas para mejorar el proceso de clarificación en la planta de agua desmineralizada en la planta GENSA S. A. E. S. P., de Termopaipa, localizada en Boyacá, Colombia. Se emplearon datos experimentales obtenidos a partir de la prueba de jarras para construir un modelo basado en redes neuronales. Las variables independientes fueron pH, turbiedad, conductividad eléctrica y color del agua cruda, junto con la dosis de polímero floculante. La variable de salida del modelo fue la dosis del coagulante. Se escogió el modelo de una red neuronal de tres capas, el cual fue validado para encontrar 10 neuronas en la capa oculta. La herramienta para entrenar la red neuronal fue optimización no lineal. El cálculo de chi cuadrada utilizado para la evaluación del modelo demostró ser eficiente en un 90% de nivel de confianza.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The present study shows the strategies used to improve the treatment of clarification of demineralized water in GENSA S. A. E. S. P., Planta Termopaipa, located in Boyacá, Colombia. Experimental data obtained from jar tests were used to build a model based on neuronal nets. The independent variables were pH, turbidity, electrical conductivity, and color of the raw water along with flocculent dosage. The output variable was the Aluminum Sulfate dosage. A three-layer neural network was chosen as a prediction approach. The model was validated to find ten neurons in the hidden layer. Nonlinear optimization was the tool used to train the neural network. The chi- square value was used to test the model and showed that the model is efficient at 90% confidence level.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[coagulación]]></kwd>
<kwd lng="es"><![CDATA[correlación]]></kwd>
<kwd lng="es"><![CDATA[tratamiento de agua]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="en"><![CDATA[Coagulation]]></kwd>
<kwd lng="en"><![CDATA[correlation]]></kwd>
<kwd lng="en"><![CDATA[cross-validation]]></kwd>
<kwd lng="en"><![CDATA[neural net]]></kwd>
<kwd lng="en"><![CDATA[water treatment]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Altowayti]]></surname>
<given-names><![CDATA[W. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Algaifi]]></surname>
<given-names><![CDATA[H. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Bakar]]></surname>
<given-names><![CDATA[S. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Shahir]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The adsorptive removal of As (III) using biomass of arsenic resistan Bacillus thuringiensis strain WS3: Characteristics and modelling studies]]></article-title>
<source><![CDATA[Ecotoxicology and Environmental Safety]]></source>
<year>2019</year>
<volume>172</volume>
<page-range>176-85</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Anders]]></surname>
<given-names><![CDATA[U.]]></given-names>
</name>
<name>
<surname><![CDATA[Korn]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Model selection in neural networks]]></article-title>
<source><![CDATA[Neural Networks]]></source>
<year>1999</year>
<volume>12</volume>
<page-range>309-23</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bhatnagar]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Minocha]]></surname>
<given-names><![CDATA[A. K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Conventional and non-conventional adsorbents for removal of pollutants from water - A review]]></article-title>
<source><![CDATA[Indian Journal of Chemical Technology]]></source>
<year>2006</year>
<volume>13</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>203-17</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hadzima-Nyarko]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Nyarko]]></surname>
<given-names><![CDATA[E. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Ademovic]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Modelling the influence of waste rubber on compressive strength of concrete by artificial neural networks]]></article-title>
<source><![CDATA[Materials]]></source>
<year>2019</year>
<volume>12</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>561</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jayaweera]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Azis]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Reliability of principal component analysis and Pearson correlation coefficient, for application in artificial neural network model development, for water treatment plants]]></article-title>
<source><![CDATA[IOP Conference Series: Materials Science and Engineering]]></source>
<year>2018</year>
<publisher-name><![CDATA[Kuala Lampur, Malasya]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kennedy]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Gandomi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Miller]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal]]></article-title>
<source><![CDATA[Journal of Environmental Chemical Engineering]]></source>
<year>2015</year>
<volume>3</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>2829-38</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Machado-Infante]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ramírez-Caballero]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Barajas-Meneses]]></surname>
<given-names><![CDATA[M. J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Study of the adsorption capacity of Fe(II) dissolved in water by using a mineral rich in Manganese Dioxide (MnO2) from Colombia]]></article-title>
<source><![CDATA[DYNA]]></source>
<year>2016</year>
<volume>83</volume>
<numero>196</numero>
<issue>196</issue>
<page-range>223-8</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mason]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Perreault]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Collinearity, power, and interpretation of multiple regression analysis]]></article-title>
<source><![CDATA[Journal of Marketing Research]]></source>
<year>1991</year>
<volume>3</volume>
<page-range>268-80</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Michael]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Trinogga]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Cebrian-Piqueras]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Trait correlation network analysis identifies biomass allocation traits and stem specific length as hub traits in herbaceous perennial plants]]></article-title>
<source><![CDATA[Journal of Ecology]]></source>
<year>2019</year>
</nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mojiri]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Kazeroon]]></surname>
<given-names><![CDATA[R. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Gholami]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Cross-linked magnetic chitosan/activated biochar for removal of emerging micropollutants from water: Optimization by the artificial neural network]]></article-title>
<source><![CDATA[Water]]></source>
<year>2019</year>
<volume>11</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>551</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Olusola]]></surname>
<given-names><![CDATA[O. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Olurotimi]]></surname>
<given-names><![CDATA[A. D.]]></given-names>
</name>
<name>
<surname><![CDATA[Adekilekun]]></surname>
<given-names><![CDATA[J. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Adetayo]]></surname>
<given-names><![CDATA[A. J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Kinetics and neuro-fuzzy soft computing modelling of river turbid water coag-flocculation using mango (Mangifera indica) kernel coagulant]]></article-title>
<source><![CDATA[Chemical Engineering Communications]]></source>
<year>2019</year>
<volume>206</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>254-67</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rao]]></surname>
<given-names><![CDATA[S. S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Engineering optimization: Theory and practice]]></source>
<year>2009</year>
<edition>5</edition>
<publisher-loc><![CDATA[Hoboken, USA ]]></publisher-loc>
<publisher-name><![CDATA[Wiley]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
