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

Mostafa Solgi , Tahereh Najib , Shahyar Ahmadnejad , Bahram Nasernejad
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引用次数: 38

Abstract

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), endothermic (ΔH > 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.

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枸杞籽新型除铬活性炭的合成与表征:基于神经网络和支持向量回归的实验分析与建模
本研究首次以枸杞种子为原料,经KOH化学活化制备了活性炭。在450、550、650和750℃的不同温度下进行炭化过程。对吸附剂进行了氮吸附-解吸、傅里叶红外光谱(FTIR)和场发射扫描电镜(FESEM)分析。考察了pH、Cr(VI)初始浓度、吸附剂投加量和接触时间等操作参数对吸附效果的影响。实验数据与Langmuir模型吻合较好,最大吸附量为200 mg/g。动力学研究表明,吸附过程符合准二级模型,化学反应为限速步骤。热力学参数表明,吸附过程可以认为是自发(ΔG < 0)吸热(ΔH > 0)过程,导致熵增加(ΔS > 0)。采用支持向量机、遗传算法(SVM-GA)和人工神经网络(ANN)相结合的方法预测合成活性炭对水溶液中铬的去除率。将人工神经网络与SVR-GA模型的相关系数R2与实验数据进行比较,结果表明,两种模型均能预测合成活性炭对铬的去除率,而SVR-GA模型的预测更为准确。
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