节点文献
α-生育酚在超临界CO2中溶解度神经网络模型的建立
Solubility Neural Networks Model of α-Tocopherol in Supercritical CO2
【摘要】 生育酚有很高的生理活性 ,油脂生产中得到的脱臭馏出物含有丰富的天然生育酚 作为萃取生育酚的基础 ,对甲酯化油脂脱臭馏出物中α -生育酚在超临界CO2 中的溶解度进行了测试 ,并用Chrastil分子缔合模型和RBF神经网络模型对溶解度数据进行了拟合 Chrastil分子缔合模型的相对误差为 2 5.36% 对于RBF神经网络模型 ,经过网络学习和训练 ,训练集平均误差仅为 0 .2 3% ,测试集误差为 6.4 8% ,效果比较理想
【Abstract】 Tocopherol has very high physiological activity. Soybean deodorized sludge contains plentiful natural tocopherol. For extracting tocopherol from soybean deodorized sludge, solubility of α tocopherol (α-T) in supercritical CO 2 is measured. The solubility data are correlated by RBF neural networks model and chrastil model. Relative error of chrastil model is 25.36%. By means of learning and training on RBF neural networks model, the average error of training set is 0.23% and that of testing set is 6.48%. RBF neural networks model is better.
【Key words】 tocopherol； supercritical CO 2； solubility； neural networks； model；
- 【文献出处】 江苏理工大学学报(自然科学版) ,JOURNAL OF JIANGSU UNIVERSITY OF SCIENCE AND TECHNOLOGY , 编辑部邮箱 ,2000年06期
- 【分类号】TS221
- 【被引频次】4
- 【下载频次】70