Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique
An intensive study has been made to see the performance of the different liner materials with bentonite on the removal efficiency of Cu(II) and Zn(II) from industrial leachate. An artificial neural network (ANN) was used to display the significant levels of the analyzed liner materials on the remova...
| Autori principali: | , , |
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| Natura: | Articolo |
| Lingua: | English |
| Pubblicazione: |
Hindawi Limited
2013-01-01
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| Serie: | The Scientific World Journal |
| Accesso online: | http://dx.doi.org/10.1155/2013/240158 |
| _version_ | 1827004634357563392 |
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| author | Nurdan Gamze Turan Emine Beril Gümüşel Okan Ozgonenel |
| author_facet | Nurdan Gamze Turan Emine Beril Gümüşel Okan Ozgonenel |
| author_sort | Nurdan Gamze Turan |
| collection | DOAJ |
| description | An intensive study has been made to see the performance of the different liner materials with bentonite on the removal efficiency of Cu(II) and Zn(II) from industrial leachate. An artificial neural network (ANN) was used to display the significant levels of the analyzed liner materials on the removal efficiency. The statistical analysis proves that the effect of natural zeolite was significant by a cubic spline model with a 99.93% removal efficiency. Optimization of liner materials was achieved by minimizing bentonite mixtures, which were costly, and maximizing Cu(II) and Zn(II) removal efficiency. The removal efficiencies were calculated as 45.07% and 48.19% for Cu(II) and Zn(II), respectively, when only bentonite was used as liner material. However, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Cu(II) removal (95%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (61.24% and 65.09%). Similarly, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Zn(II) removal (89.19%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (82.76% and 74.89%). |
| first_indexed | 2025-02-18T11:40:44Z |
| format | Article |
| id | doaj.art-f7bb19f4f3b44ff8bcca7cfbc6c541e5 |
| institution | Directory Open Access Journal |
| issn | 1537-744X |
| language | English |
| last_indexed | 2025-02-18T11:40:44Z |
| publishDate | 2013-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj.art-f7bb19f4f3b44ff8bcca7cfbc6c541e52024-11-02T05:17:21ZengHindawi LimitedThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/240158240158Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence TechniqueNurdan Gamze Turan0Emine Beril Gümüşel1Okan Ozgonenel2Department of Environmental Engineering, Engineering Faculty, Ondokuz Mays University, Kurupelit, 55139 Samsun, TurkeyDepartment of Environmental Engineering, Engineering Faculty, Ondokuz Mays University, Kurupelit, 55139 Samsun, TurkeyDepartment of Electric and Electronic Engineering, Engineering Faculty, Ondokuz Mays University, Kurupelit, 55139 Samsun, TurkeyAn intensive study has been made to see the performance of the different liner materials with bentonite on the removal efficiency of Cu(II) and Zn(II) from industrial leachate. An artificial neural network (ANN) was used to display the significant levels of the analyzed liner materials on the removal efficiency. The statistical analysis proves that the effect of natural zeolite was significant by a cubic spline model with a 99.93% removal efficiency. Optimization of liner materials was achieved by minimizing bentonite mixtures, which were costly, and maximizing Cu(II) and Zn(II) removal efficiency. The removal efficiencies were calculated as 45.07% and 48.19% for Cu(II) and Zn(II), respectively, when only bentonite was used as liner material. However, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Cu(II) removal (95%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (61.24% and 65.09%). Similarly, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Zn(II) removal (89.19%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (82.76% and 74.89%).http://dx.doi.org/10.1155/2013/240158 |
| spellingShingle | Nurdan Gamze Turan Emine Beril Gümüşel Okan Ozgonenel Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique The Scientific World Journal |
| title | Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique |
| title_full | Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique |
| title_fullStr | Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique |
| title_full_unstemmed | Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique |
| title_short | Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique |
| title_sort | prediction of heavy metal removal by different liner materials from landfill leachate modeling of experimental results using artificial intelligence technique |
| url | http://dx.doi.org/10.1155/2013/240158 |
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