Neural Model for the Prediction of Volume Losses in the Aging Process of Rums

The rum aging process shows volume losses, called wastage. The numerical operation variables: product, boardwalk, horizontal and vertical positions, date, volume, alcoholic degree, temperature, humidity and aging time, recorded in databases, contain valuable information to study the process. MATLAB...

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Main Authors:	García-Castellanos, Beatriz, Pérez-Ones, Osney, Zumalacárregui-de-Cárdenas, Lourdes, Blanco-Carvajal, Idania, López-de-la-Maza, Luis Eduardo
Format:	Online
Language:	eng spa
Published:	Universidad Pedagógica y Tecnológica de Colombia 2020
Subjects:	rums aging volume losses modeling artificial neural networks MATLAB rones añejamiento mermas modelación redes neuronales artificiales
Online Access:	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10514
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author	García-Castellanos, Beatriz Pérez-Ones, Osney Zumalacárregui-de-Cárdenas, Lourdes Blanco-Carvajal, Idania López-de-la-Maza, Luis Eduardo
author_facet	García-Castellanos, Beatriz Pérez-Ones, Osney Zumalacárregui-de-Cárdenas, Lourdes Blanco-Carvajal, Idania López-de-la-Maza, Luis Eduardo
author_sort	García-Castellanos, Beatriz
collection	OJS
description	The rum aging process shows volume losses, called wastage. The numerical operation variables: product, boardwalk, horizontal and vertical positions, date, volume, alcoholic degree, temperature, humidity and aging time, recorded in databases, contain valuable information to study the process. MATLAB 2017 software was used to estimate volume losses. In the modeling of the rum aging process, the multilayer perceptron neuronal network with one and two hidden layers was used, varying the number of neurons in these between 4 and 10. The Levenberg-Marquadt (LM) and Bayesian training algorithms were compared (Bay) The increase in 6 consecutive iterations of the validation error and 1,000 as the maximum number of training cycles were the criteria used to stop the training. The input variables to the network were: numerical month, volume, temperature, humidity, initial alcoholic degree and aging time, while the output variable was wastage. 546 pairs of input/output data were processed. The statistical Friedman and Wilcoxon tests were performed to select the best neural architecture according to the mean square error (MSE) criteria. The selected topology has a 6-4-4-1 structure, with an MSE of 2.1∙10-3 and a correlation factor (R) with experimental data of 0.9898. The neural network obtained was used to simulate thirteen initial aging conditions that were not used for training and validation, detecting a coefficient of determination (R2) of 0.9961.
format	Online
id	oai:oai.revistas.uptc.edu.co:article-10514
institution	Revista Facultad de Ingeniería
language	eng spa
publishDate	2020
publisher	Universidad Pedagógica y Tecnológica de Colombia
record_format	ojs
spelling	oai:oai.revistas.uptc.edu.co:article-105142021-07-13T02:24:24Z Neural Model for the Prediction of Volume Losses in the Aging Process of Rums Modelo neuronal para la predicción de mermas en el proceso de añejamiento de rones García-Castellanos, Beatriz Pérez-Ones, Osney Zumalacárregui-de-Cárdenas, Lourdes Blanco-Carvajal, Idania López-de-la-Maza, Luis Eduardo rums aging volume losses modeling artificial neural networks MATLAB rones añejamiento mermas modelación redes neuronales artificiales The rum aging process shows volume losses, called wastage. The numerical operation variables: product, boardwalk, horizontal and vertical positions, date, volume, alcoholic degree, temperature, humidity and aging time, recorded in databases, contain valuable information to study the process. MATLAB 2017 software was used to estimate volume losses. In the modeling of the rum aging process, the multilayer perceptron neuronal network with one and two hidden layers was used, varying the number of neurons in these between 4 and 10. The Levenberg-Marquadt (LM) and Bayesian training algorithms were compared (Bay) The increase in 6 consecutive iterations of the validation error and 1,000 as the maximum number of training cycles were the criteria used to stop the training. The input variables to the network were: numerical month, volume, temperature, humidity, initial alcoholic degree and aging time, while the output variable was wastage. 546 pairs of input/output data were processed. The statistical Friedman and Wilcoxon tests were performed to select the best neural architecture according to the mean square error (MSE) criteria. The selected topology has a 6-4-4-1 structure, with an MSE of 2.1∙10-3 and a correlation factor (R) with experimental data of 0.9898. The neural network obtained was used to simulate thirteen initial aging conditions that were not used for training and validation, detecting a coefficient of determination (R2) of 0.9961. El proceso de añejamiento de ron experimenta pérdidas de volumen, denominadas mermas. Las variables numéricas de operación: producto, rambla, posiciones horizontal y vertical, fecha, volumen, grado alcohólico, temperatura, humedad y tiempo de añejamiento, registradas en bases de datos, contienen información valiosa para estudiar el proceso. Se utilizó el software MATLAB 2017 para estimar las pérdidas en volumen. En la modelación del proceso de añejamiento de ron se utilizó la red neuronal perceptrón multicapa con una y dos capas ocultas, variándose el número de neuronas en estas entre 4 y 10. Se compararon los algoritmos de entrenamiento Levenberg-Marquadt (L-M) y Bayesiano (Bay). El incremento en 6 iteraciones consecutivas del error de validación y 1 000 como número máximo de ciclo de entrenamiento fueron los criterios utilizados para detener el entrenamiento.  Las variables de entrada a la red fueron: mes numérico, volumen, temperatura, humedad, grado alcohólico inicial y tiempo de añejamiento, mientras que la variable de salida fue mermas. Se procesaron 546 pares de datos de entrada/salida. Se realizaron las pruebas estadísticas de Friedman y Wilcoxon para la selección de la arquitectura neuronal de mejor comportamiento de acuerdo al criterio del error cuadrático medio (MSE). La topología seleccionada presenta la estructura 6-4-4-1, con un MSE de 2.1∙10-3 y un factor de correlación (R) con los datos experimentales de 0.9981. La red neuronal obtenida se empleó para la simulación de trece condiciones iniciales de añejamiento que no fueron empleadas para el entrenamiento y la validación, detectándose un coeficiente de determinación (R2) de 0.9961. Universidad Pedagógica y Tecnológica de Colombia 2020-02-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf application/pdf application/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10514 10.19053/01211129.v29.n54.2020.10514 Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e10514 Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e10514 2357-5328 0121-1129 eng spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10514/8833 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10514/8834 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10514/9173 N.A. N.A. Copyright (c) 2020 Beatriz García-Castellanos, Osney Pérez-Ones, Ph. D., Lourdes Zumalacárregui-de-Cárdenas, Ph. D., Idania Blanco-Carvajal, M.Sc., Luis Eduardo López-de-la-Maza
spellingShingle	rums aging volume losses modeling artificial neural networks MATLAB rones añejamiento mermas modelación redes neuronales artificiales García-Castellanos, Beatriz Pérez-Ones, Osney Zumalacárregui-de-Cárdenas, Lourdes Blanco-Carvajal, Idania López-de-la-Maza, Luis Eduardo Neural Model for the Prediction of Volume Losses in the Aging Process of Rums
title	Neural Model for the Prediction of Volume Losses in the Aging Process of Rums
title_alt	Modelo neuronal para la predicción de mermas en el proceso de añejamiento de rones
title_full	Neural Model for the Prediction of Volume Losses in the Aging Process of Rums
title_fullStr	Neural Model for the Prediction of Volume Losses in the Aging Process of Rums
title_full_unstemmed	Neural Model for the Prediction of Volume Losses in the Aging Process of Rums
title_short	Neural Model for the Prediction of Volume Losses in the Aging Process of Rums
title_sort	neural model for the prediction of volume losses in the aging process of rums
topic	rums aging volume losses modeling artificial neural networks MATLAB rones añejamiento mermas modelación redes neuronales artificiales
topic_facet	rums aging volume losses modeling artificial neural networks MATLAB rones añejamiento mermas modelación redes neuronales artificiales
url	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10514
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