Drying Kinetics Prediction of Solid Waste Using Semi-Empirical and Artificial Neural Network Models
The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a fu...
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Veröffentlicht in: | Chemical engineering & technology 2013-07, Vol.36 (7), p.1193-1201 |
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description | The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well, using artificial neural network (ANN) models and four well‐known drying kinetics correlations commonly applied to biological materials. According to the statistical analysis employed, the simulations showed good results for the ANN, and the Overhults model provided optimum agreement with experimental data among all other models evaluated. Empirical correlations between the Overhults model parameters and the drying operational conditions using nonlinear regression techniques were determined.
The kinetics of the drying process of solid citrus wastes in a thin‐layer fixed‐bed dryer under different operational conditions is investigated. By means of artificial neural networks and four well‐known drying kinetics correlations, the problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well. |
doi_str_mv | 10.1002/ceat.201200593 |
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The kinetics of the drying process of solid citrus wastes in a thin‐layer fixed‐bed dryer under different operational conditions is investigated. By means of artificial neural networks and four well‐known drying kinetics correlations, the problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well.</description><identifier>ISSN: 0930-7516</identifier><identifier>EISSN: 1521-4125</identifier><identifier>DOI: 10.1002/ceat.201200593</identifier><language>eng</language><publisher>Weinheim: WILEY-VCH Verlag</publisher><subject>Artificial neural networks ; Drying kinetics ; Fixed-bed dryer ; Thin-layer drying</subject><ispartof>Chemical engineering & technology, 2013-07, Vol.36 (7), p.1193-1201</ispartof><rights>Copyright © 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3643-501610e539a853d29193c2dcb0dfa5591453f40bb0d99b37c86d1c832e939b973</citedby><cites>FETCH-LOGICAL-c3643-501610e539a853d29193c2dcb0dfa5591453f40bb0d99b37c86d1c832e939b973</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fceat.201200593$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fceat.201200593$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,27929,27930,45579,45580</link.rule.ids></links><search><creatorcontrib>Perazzini, H.</creatorcontrib><creatorcontrib>Freire, F. B.</creatorcontrib><creatorcontrib>Freire, J. T.</creatorcontrib><title>Drying Kinetics Prediction of Solid Waste Using Semi-Empirical and Artificial Neural Network Models</title><title>Chemical engineering & technology</title><addtitle>Chem. Eng. Technol</addtitle><description>The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well, using artificial neural network (ANN) models and four well‐known drying kinetics correlations commonly applied to biological materials. According to the statistical analysis employed, the simulations showed good results for the ANN, and the Overhults model provided optimum agreement with experimental data among all other models evaluated. Empirical correlations between the Overhults model parameters and the drying operational conditions using nonlinear regression techniques were determined.
The kinetics of the drying process of solid citrus wastes in a thin‐layer fixed‐bed dryer under different operational conditions is investigated. By means of artificial neural networks and four well‐known drying kinetics correlations, the problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well.</description><subject>Artificial neural networks</subject><subject>Drying kinetics</subject><subject>Fixed-bed dryer</subject><subject>Thin-layer drying</subject><issn>0930-7516</issn><issn>1521-4125</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkE9PwjAchhujiYhePfcLDH9t1209EgT8A2gChMRL07WdqYyNtDPIt3eIId48vXmT93kPD0K3BHoEgN5pq5oeBUIBuGBnqEM4JVFMKD9HHRAMopST5BJdhfABAKQtHaTv_d5V7_jZVbZxOuBXb43TjasrXBd4XpfO4JUKjcXLcBjO7cZFw83WeadViVVlcN83rnDatXVmP_1PNLvar_G0NrYM1-iiUGWwN7_ZRcvRcDF4iCYv48dBfxJplsQs4kASApYzoTLODBVEME2NzsEUinNBYs6KGPK2C5GzVGeJITpj1AomcpGyLuodf7WvQ_C2kFvvNsrvJQF5UCQPiuRJUQuII7Bzpd3_s5aDYX_xl42OrGvdfJ1Y5dcySVnK5Wo2lqOn-WCarYh8Y9-vV3nT</recordid><startdate>201307</startdate><enddate>201307</enddate><creator>Perazzini, H.</creator><creator>Freire, F. B.</creator><creator>Freire, J. T.</creator><general>WILEY-VCH Verlag</general><general>WILEY‐VCH Verlag</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201307</creationdate><title>Drying Kinetics Prediction of Solid Waste Using Semi-Empirical and Artificial Neural Network Models</title><author>Perazzini, H. ; Freire, F. B. ; Freire, J. T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3643-501610e539a853d29193c2dcb0dfa5591453f40bb0d99b37c86d1c832e939b973</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Artificial neural networks</topic><topic>Drying kinetics</topic><topic>Fixed-bed dryer</topic><topic>Thin-layer drying</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Perazzini, H.</creatorcontrib><creatorcontrib>Freire, F. B.</creatorcontrib><creatorcontrib>Freire, J. T.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><jtitle>Chemical engineering & technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Perazzini, H.</au><au>Freire, F. B.</au><au>Freire, J. T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drying Kinetics Prediction of Solid Waste Using Semi-Empirical and Artificial Neural Network Models</atitle><jtitle>Chemical engineering & technology</jtitle><addtitle>Chem. Eng. Technol</addtitle><date>2013-07</date><risdate>2013</risdate><volume>36</volume><issue>7</issue><spage>1193</spage><epage>1201</epage><pages>1193-1201</pages><issn>0930-7516</issn><eissn>1521-4125</eissn><abstract>The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well, using artificial neural network (ANN) models and four well‐known drying kinetics correlations commonly applied to biological materials. According to the statistical analysis employed, the simulations showed good results for the ANN, and the Overhults model provided optimum agreement with experimental data among all other models evaluated. Empirical correlations between the Overhults model parameters and the drying operational conditions using nonlinear regression techniques were determined.
The kinetics of the drying process of solid citrus wastes in a thin‐layer fixed‐bed dryer under different operational conditions is investigated. By means of artificial neural networks and four well‐known drying kinetics correlations, the problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well.</abstract><cop>Weinheim</cop><pub>WILEY-VCH Verlag</pub><doi>10.1002/ceat.201200593</doi><tpages>9</tpages></addata></record> |
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subjects | Artificial neural networks Drying kinetics Fixed-bed dryer Thin-layer drying |
title | Drying Kinetics Prediction of Solid Waste Using Semi-Empirical and Artificial Neural Network Models |
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