| 000 | 03112naa2a2200397 4500 | ||
|---|---|---|---|
| 001 | 575615 | ||
| 005 | 20231030032410.0 | ||
| 035 | _a(RuTPU)RU\TPU\prd\274469 | ||
| 035 | _aRU\TPU\prd\274468 | ||
| 090 | _a575615 | ||
| 100 | _a20171017a2017 k y0rusy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aRU | ||
| 135 | _adrcn ---uucaa | ||
| 200 | 1 |
_aSynthesis and characterization of novel activated carbon from Medlar seed for chromium removal: Experimental analysis and modeling with artificial neural network and support vector regression _bElectronic resource _fM. Solgi [et al.] |
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| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 320 | _a[References: p. 247-248 (49 tit.)] | ||
| 330 | _aIn 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 _x2405-6537 _tResource-Efficient Technologies _oelectronic scientific journal _fNational Research Tomsk Polytechnic University (TPU) _d2015- |
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| 463 | 1 |
_0(RuTPU)RU\TPU\prd\274446 _tVol. 3, iss. 3 _v[P. 236-248] _d2017 |
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| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aactivated carbon | |
| 610 | 1 | _aartificial neural networks | |
| 610 | 1 | _aактивированный уголь | |
| 610 | 1 | _aискусственные нейронные сети | |
| 610 | 1 | _aрегрессия | |
| 701 | 1 |
_aSolgi _bM. _gMostafa |
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| 701 | 1 |
_aTahereh _bN. _gNajibb |
|
| 701 | 1 |
_aAhmadnejadc _bS. _gShahyar |
|
| 701 | 1 |
_aNasernejadb _bB. _gBahram |
|
| 801 | 1 |
_aRU _b63413507 _c20090623 _gPSBO |
|
| 801 | 2 |
_aRU _b63413507 _c20180831 _gPSBO |
|
| 856 | 4 | _uhttp://earchive.tpu.ru/handle/11683/50298 | |
| 942 | _cBK | ||