Binary azeotropes, which contain two chemicals with a relative volatility of 1, are very common in the chemical industry. Understanding azeotropes is essential for effectively separating binary azeotropes containing l...Binary azeotropes, which contain two chemicals with a relative volatility of 1, are very common in the chemical industry. Understanding azeotropes is essential for effectively separating binary azeotropes containing lower alcohols. Experimental techniques and ab initio approaches can produce accurate results;however, these two processes are time consuming and labor intensive. Although thermodynamic equations such as UNIFAC are widely used, experimental values are required, and it is difficult to choose the best groups to represent a complex system. Because of their high efficiency and fast calculation speed, quantitative structure–property relationship(QSPR) tools were used in this work to predict the azeotropic temperatures and compositions of binary azeotropes containing lower alcohols. The QSPR models for 64 binary azeotropes based on centroid approximation and weighted-contribution-factor approximation were established using the genetic function approximation(GFA) procedure in Materials Studio software, and a leave-one-out cross-validation procedure was conducted.External tests of an additional 16 azeotropes were also investigated, and high determination coefficient values were obtained. The best QSPR models were explained in terms of the molecular structure of the azeotropes,and good predictive ability was obtained within acceptable prediction error levels.展开更多
To improve separate effect of binary heterogeneous azeotrope in the magnetic field with different magnetic induction intensity, the influence of magnetic field on the rectification process of binary heterogeneous azeo...To improve separate effect of binary heterogeneous azeotrope in the magnetic field with different magnetic induction intensity, the influence of magnetic field on the rectification process of binary heterogeneous azeotrope was investigated with l-butanol-water system. The results show that the composition of liquid-liquid phase equilibrium of l-butanol-water system has definitely changed, the composition of l-butanol in light phase （l-butanol layer） increases by 1. 17%-1.63% and the composition of water in heavy phase （water layer） increases by 1.21%-1.58% under the influence of magnetic field. By separation of magnetization, the composition of l-butanol increases by 0.8%-1.2% and the recovery ratio of 1 -butanol increases by 1.6%-2.5%. Magnetic field has positive effect, however, the magnetized effect is not in proportion to magnetic induction intensity and has an optimum condition, in the range of 0.25 T-0. 3 T.展开更多
以125种含水二元共沸物为研究对象,基于定量结构-性质关系,对该类共沸物在常压下的共沸温度及组成进行了预测研究,分别建立了多个预测模型。首先,利用HyperChem 8.0软件绘制了纯组分的三维分子结构,并利用分子力学方法和量子力学半经验...以125种含水二元共沸物为研究对象,基于定量结构-性质关系,对该类共沸物在常压下的共沸温度及组成进行了预测研究,分别建立了多个预测模型。首先,利用HyperChem 8.0软件绘制了纯组分的三维分子结构,并利用分子力学方法和量子力学半经验方法对分子结构进行优化;然后,利用Materials Studio 8.0软件计算纯物质的分子描述符;其次,利用遗传算法分别筛选出与共沸温度及组成最为密切的特征描述符;再运用多元线性回归方法建立了6个共沸温度预测模型及5个共沸组成预测模型,并对模型的稳定性、拟合能力和预测能力进行对比分析;最后,对最适宜模型分别进行内部验证、外部验证、应用域分析、与文献中同类模型及UNIFAC基团贡献法进行对比。结果表明:最适宜共沸温度/组成预测模型分别是利用8/5个特征描述符所建立的模型;其复相关系数,调整复相关系数,均方根误差,平均绝对误差,留一法交叉验证系数和外部验证系数分别为0.9606/0.9970、0.9572/0.9969、2.9400/0.0161、1.8900/0.0104、0.9475/0.9957和0.9439/0.9976,且模型的稳定性、预测能力和泛化能力均优同类模型。展开更多
基金Supported by the National Natural Science Foundation of China(21776145,21676152)Key Research Project of Shandong Province(2016GSF116004)
文摘Binary azeotropes, which contain two chemicals with a relative volatility of 1, are very common in the chemical industry. Understanding azeotropes is essential for effectively separating binary azeotropes containing lower alcohols. Experimental techniques and ab initio approaches can produce accurate results;however, these two processes are time consuming and labor intensive. Although thermodynamic equations such as UNIFAC are widely used, experimental values are required, and it is difficult to choose the best groups to represent a complex system. Because of their high efficiency and fast calculation speed, quantitative structure–property relationship(QSPR) tools were used in this work to predict the azeotropic temperatures and compositions of binary azeotropes containing lower alcohols. The QSPR models for 64 binary azeotropes based on centroid approximation and weighted-contribution-factor approximation were established using the genetic function approximation(GFA) procedure in Materials Studio software, and a leave-one-out cross-validation procedure was conducted.External tests of an additional 16 azeotropes were also investigated, and high determination coefficient values were obtained. The best QSPR models were explained in terms of the molecular structure of the azeotropes,and good predictive ability was obtained within acceptable prediction error levels.
基金Supported by Natural Science Foundation of Tianjin (No.033603611).
文摘To improve separate effect of binary heterogeneous azeotrope in the magnetic field with different magnetic induction intensity, the influence of magnetic field on the rectification process of binary heterogeneous azeotrope was investigated with l-butanol-water system. The results show that the composition of liquid-liquid phase equilibrium of l-butanol-water system has definitely changed, the composition of l-butanol in light phase （l-butanol layer） increases by 1. 17%-1.63% and the composition of water in heavy phase （water layer） increases by 1.21%-1.58% under the influence of magnetic field. By separation of magnetization, the composition of l-butanol increases by 0.8%-1.2% and the recovery ratio of 1 -butanol increases by 1.6%-2.5%. Magnetic field has positive effect, however, the magnetized effect is not in proportion to magnetic induction intensity and has an optimum condition, in the range of 0.25 T-0. 3 T.
文摘以125种含水二元共沸物为研究对象,基于定量结构-性质关系,对该类共沸物在常压下的共沸温度及组成进行了预测研究,分别建立了多个预测模型。首先,利用HyperChem 8.0软件绘制了纯组分的三维分子结构,并利用分子力学方法和量子力学半经验方法对分子结构进行优化;然后,利用Materials Studio 8.0软件计算纯物质的分子描述符;其次,利用遗传算法分别筛选出与共沸温度及组成最为密切的特征描述符;再运用多元线性回归方法建立了6个共沸温度预测模型及5个共沸组成预测模型,并对模型的稳定性、拟合能力和预测能力进行对比分析;最后,对最适宜模型分别进行内部验证、外部验证、应用域分析、与文献中同类模型及UNIFAC基团贡献法进行对比。结果表明:最适宜共沸温度/组成预测模型分别是利用8/5个特征描述符所建立的模型;其复相关系数,调整复相关系数,均方根误差,平均绝对误差,留一法交叉验证系数和外部验证系数分别为0.9606/0.9970、0.9572/0.9969、2.9400/0.0161、1.8900/0.0104、0.9475/0.9957和0.9439/0.9976,且模型的稳定性、预测能力和泛化能力均优同类模型。
