A hollow tubular copper removal adsorbent was prepared with oyster shell and cement as the main raw materials. The effects of different formulas, different initial copper concentrations and different pH values of samp...A hollow tubular copper removal adsorbent was prepared with oyster shell and cement as the main raw materials. The effects of different formulas, different initial copper concentrations and different pH values of samples on the copper removal efficiency were investigated to determine the optimal conditions for copper removal. The content of copper in the wastewater is determined by Atomic Absorption Spectrophotometer. The microstructure and elemental composition of the samples were characterized by scanning electron microscopy（SEM） and EDS. As a result, the formula with the content of cement to be 8 wt% and the oyster shell powder of 92 wt% is optimal. Under the condition of 30 ℃, when the pH value was 9.0, the Cu2＋ adsorption capacity of the sample could reach 0.59 mg/g at 48 h. SEM analysis revealed that there are abundant pores in the sample, which is beneficial for Cu2＋ absorption on the adsorbent.展开更多
由于烧结过程中存在众多不确定性因素,使得机理分析和点预测结果的可靠性不足.基于此提出随机森林-极限树-核密度估计(random forest-extreme tree-kernel density estimation,RF-ET-KDE)算法对物理指标(粒度、水分)进行区间预测.首先,...由于烧结过程中存在众多不确定性因素,使得机理分析和点预测结果的可靠性不足.基于此提出随机森林-极限树-核密度估计(random forest-extreme tree-kernel density estimation,RF-ET-KDE)算法对物理指标(粒度、水分)进行区间预测.首先,采用数据预处理和特征选择操作筛选出最适合建模的特征变量.其次,使用基于Stacking的RF-ET算法对指标进行点预测,该算法使得模型有较高的准确性和泛化性.然后,采用KDE算法计算指标的预测误差,得到了一定置信水平下的分布区间和区间预测结果.最后,用所建模型与其余组合模型进行对比.结果表明,RF-ET算法有较高的点预测效果,KDE算法可以很好地量化指标的误差,可以得到较高可靠度的区间预测结果.展开更多
基金Sponsored by the National Natural Science Foundation of China(No.51102047)Fujian Science Foundation for Distinguished Young Scholars(2012J06011)
文摘A hollow tubular copper removal adsorbent was prepared with oyster shell and cement as the main raw materials. The effects of different formulas, different initial copper concentrations and different pH values of samples on the copper removal efficiency were investigated to determine the optimal conditions for copper removal. The content of copper in the wastewater is determined by Atomic Absorption Spectrophotometer. The microstructure and elemental composition of the samples were characterized by scanning electron microscopy（SEM） and EDS. As a result, the formula with the content of cement to be 8 wt% and the oyster shell powder of 92 wt% is optimal. Under the condition of 30 ℃, when the pH value was 9.0, the Cu2＋ adsorption capacity of the sample could reach 0.59 mg/g at 48 h. SEM analysis revealed that there are abundant pores in the sample, which is beneficial for Cu2＋ absorption on the adsorbent.
文摘由于烧结过程中存在众多不确定性因素,使得机理分析和点预测结果的可靠性不足.基于此提出随机森林-极限树-核密度估计(random forest-extreme tree-kernel density estimation,RF-ET-KDE)算法对物理指标(粒度、水分)进行区间预测.首先,采用数据预处理和特征选择操作筛选出最适合建模的特征变量.其次,使用基于Stacking的RF-ET算法对指标进行点预测,该算法使得模型有较高的准确性和泛化性.然后,采用KDE算法计算指标的预测误差,得到了一定置信水平下的分布区间和区间预测结果.最后,用所建模型与其余组合模型进行对比.结果表明,RF-ET算法有较高的点预测效果,KDE算法可以很好地量化指标的误差,可以得到较高可靠度的区间预测结果.
