SELECTION OF OPTIMUM OPERATIONAL CONDITIONS FOR THE TREATMENT PERFORMANCE OF GEOTEXTILE BIOFILTERS USING ARTIFICIAL NEURAL NETWORKS
The premise of this study is to develop an artificial neural networks (ANNs) based method to model and simulate the effluent concentrations of NH sub(3), NO super(-) sub(3), BOD sub(5) and other parameters for a geotextile biofilter developed for waste-water treatment. The model selects the best bac...
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| Veröffentlicht in: | Fresenius environmental bulletin 2010-01, Vol.19 (11), p.2587-2596 |
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| container_title | Fresenius environmental bulletin |
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| creator | Yaman, C Karaca, F Korkut, EN Martin, J P Cinar, Oe |
| description | The premise of this study is to develop an artificial neural networks (ANNs) based method to model and simulate the effluent concentrations of NH sub(3), NO super(-) sub(3), BOD sub(5) and other parameters for a geotextile biofilter developed for waste-water treatment. The model selects the best backpropagation algorithm and optimizes the structure of selected algorithm for any type of input and output parameters. Using the obtained model, the effluent concentrations of a specially designed geotextile biofilter are predicted under different operational conditions and the results are compared with the measured data. It is concluded that neural networks based models are appropriate for modeling nonlinear dependence of the treatment performance of geotextile biofilters. Then, this model is used to simulate the effects of input variables on the treatment performance of the geotextile biofilter. Finally, the model is used as a tool to define the optimum range of operational parameters of the geotextile biofilter. |
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The model selects the best backpropagation algorithm and optimizes the structure of selected algorithm for any type of input and output parameters. Using the obtained model, the effluent concentrations of a specially designed geotextile biofilter are predicted under different operational conditions and the results are compared with the measured data. It is concluded that neural networks based models are appropriate for modeling nonlinear dependence of the treatment performance of geotextile biofilters. Then, this model is used to simulate the effects of input variables on the treatment performance of the geotextile biofilter. 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The model selects the best backpropagation algorithm and optimizes the structure of selected algorithm for any type of input and output parameters. Using the obtained model, the effluent concentrations of a specially designed geotextile biofilter are predicted under different operational conditions and the results are compared with the measured data. It is concluded that neural networks based models are appropriate for modeling nonlinear dependence of the treatment performance of geotextile biofilters. Then, this model is used to simulate the effects of input variables on the treatment performance of the geotextile biofilter. Finally, the model is used as a tool to define the optimum range of operational parameters of the geotextile biofilter.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Computer simulation</subject><subject>Effluents</subject><subject>Geotextiles</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Waste water</subject><issn>1018-4619</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kM9OhDAYxHvQxA3uO_SmF5L-25YeEQvbCNSUEr1tulASDbqr7D6BLy6oZ-fym0zmm8N3AVYY4SRmHMsrsJ6mVzSLE0E4XYGvRpUqc9rU0OTQPDpdtdVMZdMlTEuYmfpeL76BubHQbRV0VqWuUrWDc28Oq7TO1HJfKOPUs9Olgnfa5Lp0yjawbXRdwNQ6netMz5O1au0P3JOxD801uBz8OIX1HyPQ5spl27g0hc7SMj4Sgk6x5FImgu0J6WXSbQhmXHa99575wGTX9QGzfpCcsETSPcaIeka5SAZBuyFwTCNw87t7_Dx8nMN02r29TF0YR_8eDudplzDBGFr-EoHbf5tYCEQZohtEvwHykmC9</recordid><startdate>20100101</startdate><enddate>20100101</enddate><creator>Yaman, C</creator><creator>Karaca, F</creator><creator>Korkut, EN</creator><creator>Martin, J P</creator><creator>Cinar, Oe</creator><scope>7SU</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>7QH</scope><scope>7ST</scope><scope>7TV</scope><scope>7UA</scope><scope>F1W</scope><scope>H97</scope><scope>L.G</scope><scope>SOI</scope></search><sort><creationdate>20100101</creationdate><title>SELECTION OF OPTIMUM OPERATIONAL CONDITIONS FOR THE TREATMENT PERFORMANCE OF GEOTEXTILE BIOFILTERS USING ARTIFICIAL NEURAL NETWORKS</title><author>Yaman, C ; Karaca, F ; Korkut, EN ; Martin, J P ; Cinar, Oe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p220t-9699874b22d98c521469cdaaa4ae49ccde14df9624893b1103a43678f73cfe613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Computer simulation</topic><topic>Effluents</topic><topic>Geotextiles</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Waste water</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yaman, C</creatorcontrib><creatorcontrib>Karaca, F</creatorcontrib><creatorcontrib>Korkut, EN</creatorcontrib><creatorcontrib>Martin, J P</creatorcontrib><creatorcontrib>Cinar, Oe</creatorcontrib><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Pollution Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Fresenius environmental bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yaman, C</au><au>Karaca, F</au><au>Korkut, EN</au><au>Martin, J P</au><au>Cinar, Oe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SELECTION OF OPTIMUM OPERATIONAL CONDITIONS FOR THE TREATMENT PERFORMANCE OF GEOTEXTILE BIOFILTERS USING ARTIFICIAL NEURAL NETWORKS</atitle><jtitle>Fresenius environmental bulletin</jtitle><date>2010-01-01</date><risdate>2010</risdate><volume>19</volume><issue>11</issue><spage>2587</spage><epage>2596</epage><pages>2587-2596</pages><issn>1018-4619</issn><abstract>The premise of this study is to develop an artificial neural networks (ANNs) based method to model and simulate the effluent concentrations of NH sub(3), NO super(-) sub(3), BOD sub(5) and other parameters for a geotextile biofilter developed for waste-water treatment. 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| ispartof | Fresenius environmental bulletin, 2010-01, Vol.19 (11), p.2587-2596 |
| issn | 1018-4619 |
| language | eng |
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| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Artificial neural networks Computer simulation Effluents Geotextiles Mathematical models Neural networks Optimization Waste water |
| title | SELECTION OF OPTIMUM OPERATIONAL CONDITIONS FOR THE TREATMENT PERFORMANCE OF GEOTEXTILE BIOFILTERS USING ARTIFICIAL NEURAL NETWORKS |
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