Separation of Ginkgo flavonoids using simulated moving bed chromatography to replace the batch chromatography is discussed.The product of higher purity is obtained,and in this process,the yield of product increased an...Separation of Ginkgo flavonoids using simulated moving bed chromatography to replace the batch chromatography is discussed.The product of higher purity is obtained,and in this process,the yield of product increased and cost decreased.Furthermore,this process is very clean.展开更多
在锶(钡) 二溴对甲偶氮甲磺显色体系中,应用 Kohonen神经网络优选波长,用遗传算法优化确定BP神经网络结构和参数,得到优化结构的网络,即 KNN GA BP ANN(29 3 3 2),学习速率η=0.233 3,动量因子α=0.974 7。用优化了的神经网络解析锶、...在锶(钡) 二溴对甲偶氮甲磺显色体系中,应用 Kohonen神经网络优选波长,用遗传算法优化确定BP神经网络结构和参数,得到优化结构的网络,即 KNN GA BP ANN(29 3 3 2),学习速率η=0.233 3,动量因子α=0.974 7。用优化了的神经网络解析锶、钡配合物的混合吸收光谱,不经分离光度法同时测定锶和钡。将BP ANN 、KNN BP ANN与KNN GA BP ANN三种神经网络方法的分析结果进行比较,表明 KNN GA BP ANN最优。锶和钡的配合物的表观摩尔吸光系数分别为εSr635=6.9×104L·mol-1·cm-1,εBa634=8.0×104L·mol-1·cm-1。展开更多
文摘Separation of Ginkgo flavonoids using simulated moving bed chromatography to replace the batch chromatography is discussed.The product of higher purity is obtained,and in this process,the yield of product increased and cost decreased.Furthermore,this process is very clean.
文摘在锶(钡) 二溴对甲偶氮甲磺显色体系中,应用 Kohonen神经网络优选波长,用遗传算法优化确定BP神经网络结构和参数,得到优化结构的网络,即 KNN GA BP ANN(29 3 3 2),学习速率η=0.233 3,动量因子α=0.974 7。用优化了的神经网络解析锶、钡配合物的混合吸收光谱,不经分离光度法同时测定锶和钡。将BP ANN 、KNN BP ANN与KNN GA BP ANN三种神经网络方法的分析结果进行比较,表明 KNN GA BP ANN最优。锶和钡的配合物的表观摩尔吸光系数分别为εSr635=6.9×104L·mol-1·cm-1,εBa634=8.0×104L·mol-1·cm-1。
