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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Bibliographic Details
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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Summary:	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.