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Fractionation of Heavy Metals in Sediments from Dianchi Lake,China 被引量:29
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作者 lI Ren-Ying YANG Hao +3 位作者 ZHOU Zhi-Gao lü jun-jie SHAO Xiao-Hua JIN Feng 《Pedosphere》 SCIE CAS CSCD 2007年第2期265-272,共8页
FVactionation of heavy metals in sediments can help in understanding potential hazards of heavy metals. The present study analyzed total concentrations and fractions of selected heavy metals (Cd, Cr, Cu, Pb, and Zn) i... FVactionation of heavy metals in sediments can help in understanding potential hazards of heavy metals. The present study analyzed total concentrations and fractions of selected heavy metals (Cd, Cr, Cu, Pb, and Zn) in surface sediments from Dianchi Lake, Yunnan Province, China, as well as factors that may affect distributions of the various heavy metal fractions. Total concentrations of the heavy metals decreased in the order Zn > Cu > Pb > Cr > Cd. These heavy metals, except Cr, were much higher than their background levels, indicating that Dianchi Lake was polluted by Cd, Zn, Pb, and Cu. Cadmium occurred mainly as the non-residual fraction (sum of the HOAc-soluble, reducible, and oxidizable fractions) (97.6%), and Zn (55.7%) was also predominantly found in the non-residual fraction. In contrast, most of the Cr (88.5%), Pb (81.8%), and Cu (59.2%) occurred in the residual fraction. Correlation analysis showed that total heavy metal concentrations, organic matter and reducible Fe were the main factors affecting the distributions of the various heavy metal fractions. In the Waihai section of Dianchi Lake (comprising 97% of the lake area), the concentrations of Cd, Zn, Pb, and Cu in the non-residual fraction were significantly lower (P < 0.01 or 0.05) than those of the Caohai section (3% of the lake area). This indicated that potential heavy metal hazards in the Caohai section were greater than the Waihai section. 展开更多
关键词 中国 滇池 重金属 水体污染 沉积物 分馏法
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基于极限学习机的风电机组变桨距系统辨识方法研究 被引量:1
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作者 吕俊杰 王欣 《新型工业化》 2018年第10期6-9,49,共5页
针对风电机组在运行过程中难以建立精确的数学模型的特点,将极限学习机应用在风电机组变桨距系统辨识中。通过对变桨距系统的动态过程进行分析,确定了变桨距系统辨识的输入输出。风速和桨距角作为极限学习机神经网络模型的输入,发电机... 针对风电机组在运行过程中难以建立精确的数学模型的特点,将极限学习机应用在风电机组变桨距系统辨识中。通过对变桨距系统的动态过程进行分析,确定了变桨距系统辨识的输入输出。风速和桨距角作为极限学习机神经网络模型的输入,发电机功率作为极限学习机神经网络模型的输出。从而构建输入输出的样本集,对网络进行训练,当学习精度满足要求,确定网络隐层节点数,得出ELM神经网络变桨距辨识模型。仿真结果表明,ELM神经网络算法在变桨距系统辨识中具有比较高的辨识精度和效率。 展开更多
关键词 风电机组 变桨距系统 极限学习机 系统辨识
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