Modeling of an industrial drying process by artificial neural networks
A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN), precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.
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Brazilian Society of Chemical Engineering
2008
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oai:scielo:S0104-663220080003000092008-09-02Modeling of an industrial drying process by artificial neural networksAssidjo,E.Yao,B.Kisselmina,K.Amané,D. Neural network Grated coconut drying Modeling A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN), precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.info:eu-repo/semantics/openAccessBrazilian Society of Chemical EngineeringBrazilian Journal of Chemical Engineering v.25 n.3 20082008-09-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322008000300009en10.1590/S0104-66322008000300009 |
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| author |
Assidjo,E. Yao,B. Kisselmina,K. Amané,D. |
| spellingShingle |
Assidjo,E. Yao,B. Kisselmina,K. Amané,D. Modeling of an industrial drying process by artificial neural networks |
| author_facet |
Assidjo,E. Yao,B. Kisselmina,K. Amané,D. |
| author_sort |
Assidjo,E. |
| title |
Modeling of an industrial drying process by artificial neural networks |
| title_short |
Modeling of an industrial drying process by artificial neural networks |
| title_full |
Modeling of an industrial drying process by artificial neural networks |
| title_fullStr |
Modeling of an industrial drying process by artificial neural networks |
| title_full_unstemmed |
Modeling of an industrial drying process by artificial neural networks |
| title_sort |
modeling of an industrial drying process by artificial neural networks |
| description |
A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN), precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying. |
| publisher |
Brazilian Society of Chemical Engineering |
| publishDate |
2008 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322008000300009 |
| work_keys_str_mv |
AT assidjoe modelingofanindustrialdryingprocessbyartificialneuralnetworks AT yaob modelingofanindustrialdryingprocessbyartificialneuralnetworks AT kisselminak modelingofanindustrialdryingprocessbyartificialneuralnetworks AT amaned modelingofanindustrialdryingprocessbyartificialneuralnetworks |
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1756411210177183744 |