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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

机译:从枸杞种子中去除铬的新型活性炭的合成与表征:人工神经网络和支持向量回归的实验分析与建模

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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 (ΔG??0) process which leads to an increase in entropy (ΔS?>?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.
机译:在这项研究中,第一次是通过KOH的化学活化作用从枸杞种子(Mespilus germanica)生产活性炭。碳化过程在450、550、650和750°C的不同温度下进行。对吸附剂进行了氮吸附-解吸,傅立叶变换红外光谱(FTIR)和场发射扫描电子显微镜(FESEM)分析。研究了pH,Cr(VI)的初始浓度,吸附剂用量和接触时间等操作参数的影响。实验数据表明与Langmuir模型具有更好的一致性,最大吸附容量估计为200?mg / g。动力学研究表明,吸附过程遵循伪二级模型,化学反应是限速步骤。热力学参数表明,吸附过程可以看作是自发的(ΔGΔ0)过程,从而导致熵的增加(ΔSΔ>α0)。研究了支持向量机与遗传算法(SVM-GA)和人工神经网络(ANN)结合的应用,以预测使用合成活性炭从水溶液中去除铬的百分比。与ANN和SVR-GA模型相关的相关系数(R 2 )与实验数据的比较证明,两个模型都能够预测合成活性炭对铬的去除百分比,而SVR- GA模型预测更为准确。

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