NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS

ABSTRACT Background: The production quality of rice is highly depended on the drying process as drying weakens the rice kernel. A look at the production process of rice in the industry was taken. The drying of rice influences the storage capacity of the grain, the energy consumption, the final mas...

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Autores principales:	José Aldemar MUÑOZ, Carlos Arturo SÁNCHEZ, Helmer MUÑOZ
Formato:	article
Lenguaje:	EN
Publicado:	Universidad de Antioquia
Materias:	drying rice modeling neural networks nonlinear Food processing and manufacture TP368-456 Pharmaceutical industry HD9665-9675
Acceso en línea:	https://doaj.org/article/9e7719e951314284856db20f2d472056
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spelling	oai:doaj.org-article:9e7719e951314284856db20f2d4720562021-12-02T09:23:47ZNONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS0121-400410.17533/udea.vitae.v25n3a02https://doaj.org/article/9e7719e951314284856db20f2d472056http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-40042018000300120&lng=en&tlng=enhttps://doaj.org/toc/0121-4004ABSTRACT Background: The production quality of rice is highly depended on the drying process as drying weakens the rice kernel. A look at the production process of rice in the industry was taken. The drying of rice influences the storage capacity of the grain, the energy consumption, the final mass of the grain and the percentage of whole grains at the end of the process. Objective: The main objective was to analyse the drying of rice by making an artificial neural network to model and simulate it. Methods: The modeling of a rice drying process using neural networks was presented. These models are suitable to be used in combination with model-based control strategies in order to improve the drying process. The implementation, preprocessing and data retrieval for the design of an artificial neural system was analyzed. Controlling the drying factors is of major importance. Feedforward and dynamic neural networks were compared based on their performance. Results: It was concluded that when some part of the dataset is given as training, even with one dataset, a back-propagation network simulates very well the other parts of the drying curve. It can be said that the approximations done by the networks to obtain a nonlinear model of the rice drying process are quiet good. Conclusions: Firstly, because of the too little data available for training, the networks were not as good as expected. More data is needed to realy have a powerfull network capable of approximated very well the drying curve. Secondly, a backpropagation network can be a good solution for modelling and for use in a controller if more data is available, in contrast a linear network gave bad results. thirdly, a network with little number of layers is the best option. A perfect mapping from the input to the output is impossible due the differences in each test and the imperfect sensors.José Aldemar MUÑOZCarlos Arturo SÁNCHEZHelmer MUÑOZUniversidad de Antioquiaarticledryingricemodelingneural networksnonlinearFood processing and manufactureTP368-456Pharmaceutical industryHD9665-9675ENVitae, Vol 25, Iss 3, Pp 120-127
institution	DOAJ
collection	DOAJ
language	EN
topic	drying rice modeling neural networks nonlinear Food processing and manufacture TP368-456 Pharmaceutical industry HD9665-9675
spellingShingle	drying rice modeling neural networks nonlinear Food processing and manufacture TP368-456 Pharmaceutical industry HD9665-9675 José Aldemar MUÑOZ Carlos Arturo SÁNCHEZ Helmer MUÑOZ NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS
description	ABSTRACT Background: The production quality of rice is highly depended on the drying process as drying weakens the rice kernel. A look at the production process of rice in the industry was taken. The drying of rice influences the storage capacity of the grain, the energy consumption, the final mass of the grain and the percentage of whole grains at the end of the process. Objective: The main objective was to analyse the drying of rice by making an artificial neural network to model and simulate it. Methods: The modeling of a rice drying process using neural networks was presented. These models are suitable to be used in combination with model-based control strategies in order to improve the drying process. The implementation, preprocessing and data retrieval for the design of an artificial neural system was analyzed. Controlling the drying factors is of major importance. Feedforward and dynamic neural networks were compared based on their performance. Results: It was concluded that when some part of the dataset is given as training, even with one dataset, a back-propagation network simulates very well the other parts of the drying curve. It can be said that the approximations done by the networks to obtain a nonlinear model of the rice drying process are quiet good. Conclusions: Firstly, because of the too little data available for training, the networks were not as good as expected. More data is needed to realy have a powerfull network capable of approximated very well the drying curve. Secondly, a backpropagation network can be a good solution for modelling and for use in a controller if more data is available, in contrast a linear network gave bad results. thirdly, a network with little number of layers is the best option. A perfect mapping from the input to the output is impossible due the differences in each test and the imperfect sensors.
format	article
author	José Aldemar MUÑOZ Carlos Arturo SÁNCHEZ Helmer MUÑOZ
author_facet	José Aldemar MUÑOZ Carlos Arturo SÁNCHEZ Helmer MUÑOZ
author_sort	José Aldemar MUÑOZ
title	NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS
title_short	NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS
title_full	NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS
title_fullStr	NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS
title_full_unstemmed	NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS
title_sort	nonlinear model of arice drying process using neural networks
publisher	Universidad de Antioquia
url	https://doaj.org/article/9e7719e951314284856db20f2d472056
work_keys_str_mv	AT josealdemarmunoz nonlinearmodelofaricedryingprocessusingneuralnetworks AT carlosarturosanchez nonlinearmodelofaricedryingprocessusingneuralnetworks AT helmermunoz nonlinearmodelofaricedryingprocessusingneuralnetworks
_version_	1718398136750178304

NONLINEAR MODEL OF ARICE DRYING PROCESS USING NEURAL NETWORKS

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