Designing and Optimizing a Neural Network for the Modeling of a Fluidized-Bed Drying Process
A wet granular solid material (alperujo, a waste from the olive mills) was dried using a fluidized-bed dryer (FBD) system. The drying curves, data of moisture vs time, were fitted to an exponential equation and then interpolated and used as learning data for an artificial neural network (ANN). The t...
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| Veröffentlicht in: | Industrial & engineering chemistry research 2002-05, Vol.41 (9), p.2262-2269 |
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| creator | Castellanos, José A Palancar, María C Aragón, José M |
| description | A wet granular solid material (alperujo, a waste from the olive mills) was dried using a fluidized-bed dryer (FBD) system. The drying curves, data of moisture vs time, were fitted to an exponential equation and then interpolated and used as learning data for an artificial neural network (ANN). The target is to predict the moisture of the solid from operating conditions data. The ANN has three layers, with four inputs, four hidden neurons, and one output. Several criteria are given to improve the ANN training, e.g., selecting the data sets (number of data and order in which they are shown to the network), tuning the learning coefficient (set at 1.5), and optimizing the sigmoid function (two adjustable parameters, α and β, set at 3 and 9). The optimized ANN can predict the evolution of the moisture of the solid with a model error of ±1.57%. |
| doi_str_mv | 10.1021/ie000950t |
| format | Article |
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The drying curves, data of moisture vs time, were fitted to an exponential equation and then interpolated and used as learning data for an artificial neural network (ANN). The target is to predict the moisture of the solid from operating conditions data. The ANN has three layers, with four inputs, four hidden neurons, and one output. Several criteria are given to improve the ANN training, e.g., selecting the data sets (number of data and order in which they are shown to the network), tuning the learning coefficient (set at 1.5), and optimizing the sigmoid function (two adjustable parameters, α and β, set at 3 and 9). 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Eng. Chem. Res</addtitle><description>A wet granular solid material (alperujo, a waste from the olive mills) was dried using a fluidized-bed dryer (FBD) system. The drying curves, data of moisture vs time, were fitted to an exponential equation and then interpolated and used as learning data for an artificial neural network (ANN). The target is to predict the moisture of the solid from operating conditions data. The ANN has three layers, with four inputs, four hidden neurons, and one output. Several criteria are given to improve the ANN training, e.g., selecting the data sets (number of data and order in which they are shown to the network), tuning the learning coefficient (set at 1.5), and optimizing the sigmoid function (two adjustable parameters, α and β, set at 3 and 9). The optimized ANN can predict the evolution of the moisture of the solid with a model error of ±1.57%.</description><subject>Agriculture, rearing and food industries wastes</subject><subject>Applied sciences</subject><subject>Biological and medical sciences</subject><subject>Devices using thermal energy</subject><subject>Dryers</subject><subject>Energy</subject><subject>Energy. Thermal use of fuels</subject><subject>Exact sciences and technology</subject><subject>Food industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Pollution</subject><subject>Use and upgrading of agricultural and food by-products. 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Thermal use of fuels</topic><topic>Exact sciences and technology</topic><topic>Food industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Pollution</topic><topic>Use and upgrading of agricultural and food by-products. Biotechnology</topic><topic>Wastes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Castellanos, José A</creatorcontrib><creatorcontrib>Palancar, María C</creatorcontrib><creatorcontrib>Aragón, José M</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Industrial & engineering chemistry research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Castellanos, José A</au><au>Palancar, María C</au><au>Aragón, José M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Designing and Optimizing a Neural Network for the Modeling of a Fluidized-Bed Drying Process</atitle><jtitle>Industrial & engineering chemistry research</jtitle><addtitle>Ind. Eng. Chem. Res</addtitle><date>2002-05-01</date><risdate>2002</risdate><volume>41</volume><issue>9</issue><spage>2262</spage><epage>2269</epage><pages>2262-2269</pages><issn>0888-5885</issn><eissn>1520-5045</eissn><coden>IECRED</coden><abstract>A wet granular solid material (alperujo, a waste from the olive mills) was dried using a fluidized-bed dryer (FBD) system. The drying curves, data of moisture vs time, were fitted to an exponential equation and then interpolated and used as learning data for an artificial neural network (ANN). The target is to predict the moisture of the solid from operating conditions data. The ANN has three layers, with four inputs, four hidden neurons, and one output. Several criteria are given to improve the ANN training, e.g., selecting the data sets (number of data and order in which they are shown to the network), tuning the learning coefficient (set at 1.5), and optimizing the sigmoid function (two adjustable parameters, α and β, set at 3 and 9). 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| language | eng |
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| source | ACS Publications |
| subjects | Agriculture, rearing and food industries wastes Applied sciences Biological and medical sciences Devices using thermal energy Dryers Energy Energy. Thermal use of fuels Exact sciences and technology Food industries Fundamental and applied biological sciences. Psychology Pollution Use and upgrading of agricultural and food by-products. Biotechnology Wastes |
| title | Designing and Optimizing a Neural Network for the Modeling of a Fluidized-Bed Drying Process |
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