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NH4Y zeolite was prepared through ion-exchange of NaY zeolite with an ammonium salt. Then LaY zeolite was obtained through a secondary ion-exchange of NH4Y zeolite with a rare earth salt solution followed by calcination of the zeolite product. Dynamic adsorptive desulfurization of naphtha was conducted in the presence of the modified LaY zeolite, and the sulfur content of the treated naphtha samples was analyzed by microcoulometry. The test results showed that under dynamic conditions the LaY zeolite prepared through secondary ion-exchange of NH4Y zeolite, which was prepared using 1.0 mol/L ammonium salt, with the rare earth salt exhibited a better desulfurization efficiency. Furthermore, the LaY zeolite achieved a best desulfurization effect at an adsorption temperature of 45 ℃ and an adsorbent/oil ratio of 1:2. 相似文献
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基于粒计算视角,提出粒化-融合框架下的海量高维数据特征选择算法.运用BLB(Bag of Little Bootstrap)的思想,首先将原始海量数据集粒化为小规模数据子集(粒),然后在每个粒上构建多个自助子集的套索模型,实现粒特征选择,最后,各粒特征选择结果按权重融合、排序,得到原始数据集的有序特征选择结果.人工数据集和真实数据集上的实验表明文中算法对海量高维数据集进行特征选择的可行性和有效性. 相似文献
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产生式方法和判别式方法是解决分类问题的两种不同框架,具有各自的优势。为利用两种方法各自的优势,文中提出一种产生式与判别式线性混合分类模型,并设计一种基于遗传算法的产生式与判别式线性混合分类模型的学习算法。该算法将线性混合分类器混合参数的学习看作一个最优化问题,以两个基分类器对每个训练数据的后验概率值为数据依据,用遗传算法找出线性混合分类器混合参数的最优值。实验结果表明,在大多数数据集上,产生式与判别式线性混合分类器的分类准确率优于或近似于它的两个基分类器中的优者。 相似文献
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