Desarrollo de un Sensor Virtual basado en Modelo NARMAX y Máquina de Vectores de Soporte para Molienda Semiautógena
Development of a Software Sensor based on a NARMAX-Support Vector Machine Model for Semi-Autogenous Grinding
a Departamento de Ingeniería Informática, Universidad de Santiago de Chile, USACH, Av. Ecuador 3659, Santiago, Chile
b Departamento de Ingeniería Eléctrica, Universidad de la Frontera, UFRO, Av. Francisco Salazar 01146, Temuco, Chile
c Departamento de Ingeniería Química, Universidad de Santiago de Chile, USACH, Av. Ecuador 3659, Santiago, Chile
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array:1 [ 0 => array:2 [ "paginaInicial" => "109" "paginaFinal" => "116" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Gonzalo Acuña, Millaray Curilem, Francisco Cubillos" "autores" => array:3 [ 0 => array:4 [ "nombre" => "Gonzalo" "apellidos" => "Acuña" "email" => array:1 [ 0 => "gonzalo.acuna@usach.cl" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">¿</span>" "identificador" => "cor0005" ] ] ] 1 => array:4 [ "nombre" => "Millaray" "apellidos" => "Curilem" "email" => array:1 [ 0 => "millaray.curilem@ufrontera.cl" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 2 => array:4 [ "nombre" => "Francisco" "apellidos" => "Cubillos" "email" => array:1 [ 0 => "francisco.cubillos@usach.cl" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Departamento de Ingeniería Informática, Universidad de Santiago de Chile, USACH, Av. Ecuador 3659, Santiago, Chile" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Departamento de Ingeniería Eléctrica, Universidad de la Frontera, UFRO, Av. Francisco Salazar 01146, Temuco, Chile" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Departamento de Ingeniería Química, Universidad de Santiago de Chile, USACH, Av. Ecuador 3659, Santiago, Chile" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Autor para correspondencia." ] ] ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Development of a Software Sensor based on a NARMAX-Support Vector Machine Model for Semi-Autogenous Grinding" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Referencias no citadas</span><p id="par0005" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#bib0005">Acuña and Curilem, 2009</a>, <a class="elsevierStyleCrossRef" href="#bib0010">Canu et al., 2005</a>, <a class="elsevierStyleCrossRef" href="#bib0015">Chai et al., 2005</a>, <a class="elsevierStyleCrossRef" href="#bib0020">Curilem et al., 2011</a>, <a class="elsevierStyleCrossRef" href="#bib0025">Frohlich and Zell, 2005</a>, <a class="elsevierStyleCrossRef" href="#bib0030">Gao and Joo Er, 2005</a>, <a class="elsevierStyleCrossRef" href="#bib0035">Gonzaga et al., 2009</a>, <a class="elsevierStyleCrossRef" href="#bib0040">Guo et al., 2008</a>, <a class="elsevierStyleCrossRef" href="#bib0045">Hornstein and Parlitz, 2004</a>, <a class="elsevierStyleCrossRef" href="#bib0050">Leontaritis and Billings, 1985</a>, <a class="elsevierStyleCrossRef" href="#bib0055">Ljung, 1987</a>, <a class="elsevierStyleCrossRef" href="#bib0060">Magne et al., 1997</a>, <a class="elsevierStyleCrossRef" href="#bib0065">Martínez-Ramón et al., 2006</a>, <a class="elsevierStyleCrossRef" href="#bib0070">Norgaard, 2003</a>, <a class="elsevierStyleCrossRef" href="#bib0075">Salazar et al., 2009</a>, <a class="elsevierStyleCrossRef" href="#bib0080">Sapankevych and Sankar, 2009</a>, <a class="elsevierStyleCrossRef" href="#bib0085">Schölkopf et al., 2000</a>, <a class="elsevierStyleCrossRef" href="#bib0090">Suárez and Gómez, 2011</a>, <a class="elsevierStyleCrossRef" href="#bib0095">Suykens et al., 2002</a>, <a class="elsevierStyleCrossRef" href="#bib0100">Vapnik, 1995</a>, <a class="elsevierStyleCrossRef" href="#bib0105">Werbos, 1990</a>, <a class="elsevierStyleCrossRef" href="#bib0110">Wolmarans and Morgan, 2009</a>, <a class="elsevierStyleCrossRef" href="#bib0115">Woolley et al., 2010</a> and <a class="elsevierStyleCrossRef" href="#bib0120">Yan et al., 2004</a>.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:6 [ 0 => array:2 [ "identificador" => "xres302424" "titulo" => "Resumen" ] 1 => array:2 [ "identificador" => "xpalclavsec285446" "titulo" => "Palabras clave" ] 2 => array:2 [ "identificador" => "xres302425" "titulo" => "Abstract" ] 3 => array:2 [ "identificador" => "xpalclavsec285445" "titulo" => "Keywords" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Referencias no citadas" ] 5 => array:1 [ "titulo" => "Referencias" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2011-11-16" "fechaAceptado" => "2013-09-18" "PalabrasClave" => array:2 [ "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec285446" "palabras" => array:6 [ 0 => "Redes Neuronales Artificiales" 1 => "Máquinas de Vectores de Soporte" 2 => "NARX" 3 => "NARMAX" 4 => "Proceso de Molienda" 5 => "Sensor Virtual." ] ] ] "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec285445" "palabras" => array:6 [ 0 => "Artificial Neural Network" 1 => "Support Vector Machine" 2 => "NARX" 3 => "NARMAX" 4 => "Grinding Process" 5 => "Software Sensor" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">La estimación de estados, en procesos complejos como el proceso de molienda semiautógena (SAG) en la minería del cobre, es una tarea difícil debido a las dificultades para medir directamente ciertas variables relevantes en línea y tiempo real. En este trabajo se amplía una comparación, iniciada en trabajos anteriores de estos mismos autores, entre modelos dinámicos NARX y NARMAX construidos con el uso de Redes Neuronales Artificiales (RNA) y Máquinas de Vectores de Soporte (SVM), cuando actúan como estimadores de una de las variables de estado más importantes para la operación de molienda SAG. Para lograr esta comparación se propone una metodología simple y original para desarrollar modelos NARMAX confeccionados con SVM. Los resultados muestran la potencia predictiva de los modelos NARMAX, que incorporan los errores de predicción en tiempos anteriores para predecir la evolución futura del proceso y la ventaja de aquellos elaborados mediante SVM por sobre los confeccionados con RNA. NARMAX-SVM presenta un MSE significativamente inferior al de todos los otros modelos. En términos del proceso de molienda, se proporciona una herramienta útil para la estimación en línea y tiempo real de una variable que permite controlar y optimizar el proceso y que no puede ser medida mediante instrumentos fácilmente disponibles.</p>" ] "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">State estimation in complex processes such as the semi- autogenous grinding process (SAG) in copper mining is an important and difficult task due to difficulties for real-time and on-line measuring of some relevant process variables. This paper extends a comparison, initiated in previous work of the same authors, between NARX and NARMAX dynamic models built using Artificial Neural Networks (ANN) and Support Vector Machines (SVM), when acting as estimators of one of the most important state variables for SAG milling operation. To accomplish this comparison we propose a simple and original methodology to develop NARMAX models with SVM. The results show that SVM-NARMAX models outperform SVM- NARX models because they incorporate previous prediction errors in order to improve prediction of the future evolution of the process. Advantages of SVM over those RNA models are also highlighted. NARMAX-SVM has a significantly lower MSE than all other models. In terms of the milling process, it provides a useful tool for estimating important state variables that are not easily available on-line and in real time thus aiding control and monitoring of the process.</p>" ] ] "bibliografia" => array:2 [ "titulo" => "Referencias" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:24 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "Acuña and Curilem, 2009" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Comparison of neural networks and support vector machine dynamic models for state estimation in semiautogeneous mills," "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "G. Acuña" 1 => "M. 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| año/Mes | Html | Total | |
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