Synthesis and characterization of novel activated carbon from Medlar seed for chromium removal: Experimental analysis and modeling with artificial neural network and support vector regression
| Parent link: | Resource-Efficient Technologies: electronic scientific journal/ National Research Tomsk Polytechnic University (TPU).— , 2015-.— 2405-6537 Vol. 3, iss. 3.— 2017.— [P. 236-248] |
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| Další autoři: | , , , |
| Shrnutí: | Title screen In this study, for the first time the activated carbon has been produced from medlar seed (Mespilus germanica) via chemical activation with KOH. The carbonization process was carried out at different temperatures of 450, 550, 650 and 750 °C. The Nitrogen adsorption-desorption, Fourier transform infrared spectroscopy (FTIR) and Field Emission Scanning Electron Microscope (FESEM) analyses were carried out on the adsorbents. The effect of operating parameters, such as pH, initial concentration of Cr(VI), adsorbent dosage and contact time were investigated. The experimental data showed better agreement with the Langmuir model and the maximum adsorption capacity was evaluated to be 200 mg/g. Kinetic studies indicated that the adsorption process follows the pseudo second-order model and the chemical reaction is the rate-limiting step. Thermodynamic parameters showed that the adsorption process could be considered a spontaneous (G < 0), endothermic (H > 0) process which leads to an increase in entropy (S > 0). The application of support vector machine combined with genetic algorithm (SVM-GA) and artificial neural network (ANN) was investigated to predict the percentage of chromium removal from aqueous solution using synthesized activated carbon. The comparison of correlation coefficient (R2) related to ANN and the SVR-GA models with experimental data proved that both models were able to predict the percentage of chromium removal, by synthetic activated carbon while the SVR-GA model prediction was more accurate. |
| Jazyk: | angličtina |
| Vydáno: |
2017
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| Témata: | |
| On-line přístup: | http://earchive.tpu.ru/handle/11683/50298 |
| Médium: | Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=575615 |
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| 200 | 1 | |a Synthesis and characterization of novel activated carbon from Medlar seed for chromium removal: Experimental analysis and modeling with artificial neural network and support vector regression |b Electronic resource |f M. Solgi [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: p. 247-248 (49 tit.)] | ||
| 330 | |a In this study, for the first time the activated carbon has been produced from medlar seed (Mespilus germanica) via chemical activation with KOH. The carbonization process was carried out at different temperatures of 450, 550, 650 and 750 °C. The Nitrogen adsorption-desorption, Fourier transform infrared spectroscopy (FTIR) and Field Emission Scanning Electron Microscope (FESEM) analyses were carried out on the adsorbents. The effect of operating parameters, such as pH, initial concentration of Cr(VI), adsorbent dosage and contact time were investigated. The experimental data showed better agreement with the Langmuir model and the maximum adsorption capacity was evaluated to be 200 mg/g. Kinetic studies indicated that the adsorption process follows the pseudo second-order model and the chemical reaction is the rate-limiting step. Thermodynamic parameters showed that the adsorption process could be considered a spontaneous (G < 0), endothermic (H > 0) process which leads to an increase in entropy (S > 0). The application of support vector machine combined with genetic algorithm (SVM-GA) and artificial neural network (ANN) was investigated to predict the percentage of chromium removal from aqueous solution using synthesized activated carbon. The comparison of correlation coefficient (R2) related to ANN and the SVR-GA models with experimental data proved that both models were able to predict the percentage of chromium removal, by synthetic activated carbon while the SVR-GA model prediction was more accurate. | ||
| 461 | 1 | |0 (RuTPU)RU\TPU\prd\247369 |x 2405-6537 |t Resource-Efficient Technologies |o electronic scientific journal |f National Research Tomsk Polytechnic University (TPU) |d 2015- | |
| 463 | 1 | |0 (RuTPU)RU\TPU\prd\274446 |t Vol. 3, iss. 3 |v [P. 236-248] |d 2017 | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a activated carbon | |
| 610 | 1 | |a artificial neural networks | |
| 610 | 1 | |a активированный уголь | |
| 610 | 1 | |a искусственные нейронные сети | |
| 610 | 1 | |a регрессия | |
| 701 | 1 | |a Solgi |b M. |g Mostafa | |
| 701 | 1 | |a Tahereh |b N. |g Najibb | |
| 701 | 1 | |a Ahmadnejadc |b S. |g Shahyar | |
| 701 | 1 | |a Nasernejadb |b B. |g Bahram | |
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| 801 | 2 | |a RU |b 63413507 |c 20180831 |g PSBO | |
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/50298 | |
| 942 | |c BK | ||