非电解质液体混合物表面张力的统计热力学模型

运用Davis模型导出一个统计热力学的表面张力模型．对22个双组分体系的表面张力作了推算和关联,总平均相对偏差分别为5.45％和1.95％.并利用关联双组分体系得到的参数值,直接推测了三组分体系的表面张力,平均相对偏差为2.21％.结果令人满意.表明该方法简单、精确,便于工程应用．     

由PR状态方程计算液体混合物表面张力

在界面化学位的基础上，定义了组分的界面逸度系数，推导出一个新的液体混合物表面张力的表达式，同时用Ｐｅｎｇ－Ｒｏｂｉｎｓｏｎ状态方程计算液体混合物体相和表面相组分的逸度系数，从而实现了由状态方程计算液体混合物表面张力。文中用这一新模型计算了５６个二元体系的表面张力，平均相对偏差为３．３８％，说明该方法是可行的。     

UNIFAC基团贡献法——推算二元和多元非电解质液体混合物表面张力

本文将Butler方程与UNIFAC模型相结合,建立了一个仅需纯液体摩尔体积V_i、表面张力σ_i和UNIFAC基团参数便能推算二元和多元液体混合物表面张力的新方法.用此法对100个二元体系、10个三元体系和2个四元体系的表面张力做了推算,二元和多元体系的总平均相对偏差相应为2.53%和5.33%,表明计算方法可靠,推算精度能满足工程设计要求.     

液体混合物表面张力的测定

<正> 液体表面张力的测定方法,已有不少文献报导,各种方法有其优缺点。圆锥最大拉力法适用于测定液体混合物的表面张力。本文改进了文献[1]中所用的仪器,使它具有更高的稳定性,并能测定较高温度下易挥发溶液的表面张力,准确度在±0.10mNm~(-1)以内。     

混合物表面张力的新方程

本文基于Ｈａｎｓｅｎ展开式，采用表面相表面积分率，建立了一个液体混合物表面张力的新方程。对于７１个二元体系，关联和计算的总平均相对误差为０．４８０％；对于６个三元体系，预测的总平均相对误差为２．２８％。新方程形式简单，参数少，精度较高，并且具有预测能力。     

由PR状态方程计算液体混全物表面张力

在界面化学位的基础上，定义了组分的界面逸度系数，推导出一个新的液体混合物表面张力的表达式，同时用Ｐｅｎｇ－Ｒｏｂｉｎｓｏｎ状态方程计算液体混合物体相和表面相组分的逸度系数，从而实现了由状态方程计算液体混合物表面张力。     

液体混合物的导热系数方程

本文从传质反应速率理论的基本思想出发,建立了一个新的传热理论——传热反应速率理论。结合统计热力学模型,引进局部组成概念,导出了一个新的导热系数方程。对于n元液体混合物,该方程可表示为: λ=(2πRT~3 multiply from i=1 to n M_ix_1)(1/2)·〔(s/2)sum from i=1 to n Q_ix_1U_11-RT sum from i=1 to n θ_i lnτ_(jt)〕~2·exp〔sum from i=1 to n q_in_ix_i sum from j=1 to nθ_(ji)lnτ_(ji)-a/2RT sum from i=1 to n q_jn_ix_iU_(ji)〕该方程应用于87个二元体系的关联和16个多元体系的推算结果表明该方程是可行的和可靠的,计算精度是令人满意的。     

熔盐混合物表面张力的群论模型参数

研究得到熔盐混合物表面张力的群论模型参数随温度的变化规律，按此规律可推算任一温度下熔盐混合物全组成范围内的表面张力，用１２个物系６００个数据点检验，总平均相对误差为０．６２％。     

预测纯流体表面张力的一个新模型

A new model based on the theoretical work of Boudh-Hir and Mansoori was developed for prediction of surface tension of pure fluids. The new model has the advantage of not requiring densities in the calculation, and the input parameters are critical temperature and connectivity indices. A total of 209 compounds covering a wide variety of substances were used to develop the model, and the overall correlative AAD is 4.21%. To test its predictive ability, the model is further used to predict the surface tension of 25 more compounds that were not included in the model development. The overall predictive AAD is 4.07%, which illustrates that the model is reliable. The model proposed is simple and easy to apply, with good predictive accuracy.     

计算液体混合物导热系数的新型方程

依据Ｃｈａｐｍａｎ导热理论关于液体导热系数与粘度的关系，结合由Ｅｙｒｉｎｇ反应速率理论及ＮＲＴＬ方程导出的液体粘度关联式，得到一个计算液体混合物导热系数的新型方程。经对１８个二元体系和４个三元体系、１个四元体系共１６４６个数据点的检验，结果较为满意，与文献值或实测值相比总的平均误差仅为０．３９４％。     

表面张力和表面吉布斯自由能的严格证明

表面张力和表面吉布斯自由能是界面现象中非常重要的两个概念,表面张力定义为垂直作用在单位长度界面上的力,而表面吉布斯自由能是定温定压条件下,增加单位表面积引起系统吉布斯自由能的增量,实际上它们是从不同角度观察表面现象的同一物理量,但各教材中基本上没有详细的论述。文章从吉布斯自由能的实际意义角度对两者同一性进行严格的证明。     

A Statistical Thermodynamic Model of Vapor-Liquid Interfacial Tension for Liquid Mixtures at High Pressure

1 INTRODUCTIONInterfacial tension of pure fluids and their mixtures is an important property for the development,design and simulation of many chemical processes.The interfacial behavior has important prac-tical applications in material science,environment science and chemical engineering separationprocesses as well as in secondary or tertiary recovery of petroleum.Although interfacial phenom-ena are rather complicated,interfacial tension of free vapor-liquid interface is considered as a firstand fundamental step of the study.For example,enhanced carbon dioxide“flooding”technique     

Surface Tension Estimation of Binary Mixtures of Organic Compounds Using Artificial Neural Networks

The surface tension of binary mixtures at different temperatures and compositions is required in much scientific and technological research. Therefore, having an exact correlation between surface tension and easily accessible physical properties is essential. In this work, the sensitivity of the surface tension to some physical properties was studied by using artificial neural networks (ANNs) to find the most effective ones. Furthermore, ANNs were used to estimate the surface tension of binary systems as a function of the most effective physical properties including critical pressure, reduced temperature, acentric factor, and molar density. The experimental data, collected for training and verifying the networks, include various materials such as alkanes, alkenes, aromatics, alcohols, organic acids, as well as chlorine, iodine, sulfur, nitrogen, and fluorine-containing compounds in the composition ranges from 0 to 100 mole percent, and temperatures between 116 K and 393 K. The average absolute relative deviation (AARD) of the most accurate network, obtained for all 2038 data points regarding 83 binary mixtures, is 1.75%.     

己内酰胺-四丁基氟化铵离子液体表面张力研究

离子液体因其溶解能力良好,物理、化学性质稳定,沸点高,难挥发,不会造成二次污染等诸多优点,成为了21世纪最有前景的绿色溶剂之一。将己内酰胺-四丁基氟化铵离子液体配置成不同浓度的水溶液,测定其表面张力。结果表明,该离子液体的表面张力随温度的升高而降低,随离子液体浓度的增加而减小;在一定浓度的时候,离子液体的表面张力小于水的表面张力。     

用NRTL方程计算含离子液体体系的汽液平衡

离子液体作为环境友好溶剂在反应和分离过程中具有良好的应用前景,含离子液体体系汽液平衡的计算及模型化研究具有重要的理论和实际意义.采用非电解质溶液NRTL方程表示溶液的非理想性,关联了［bmim］［PF₆］H₂O及［C₈mim］［PF₆］H₂O二元体系的等温汽液平衡,关联误差在2%之内;预测了这些体系在其他温度下的汽液平衡,预测的总平均误差均在5%之内.通过关联不同温度下有机物在离子液体C₈H₁₄S₂O₄F₆N₃和C₉H₁₆S₂O₄F₆N₃中的无限稀释活度因子的实验数据,得到了有关NRTL方程的二元作用参数,在此基础上预测了离子液体对二元共沸体系汽液平衡的影响.结果表明,含离子液体体系的汽液平衡可以采用传统的非电解质溶液模型如NRTL方程来描述,离子液体的“盐效应”可以显著改善组分的相对挥发度甚至消除共沸现象.     

用NRTL方程预测部分互溶体系的汽液平衡

用五参数NRTL方程，对辛烷－水二元部分互溶全系的汽－液相平衡数据和液－液相平衡数据进行关联，并用液－液相平衡数据优化所得的参数，预测汽液相平衡，关联和预测的结果均能满足工程设计要求。     

Temperature dependence of interaction parameters in electrolyte NRTL model

The electrolyte NRTL model captures the electrolyte solution nonideality over the entire concentration range with two binary interaction parameters. Here, the temperature dependence of the binary parameters is formulated with a Gibbs‐Helmholtz expression containing three temperature coefficients associated with Gibbs energy, enthalpy, and heat capacity contributions. We show the Gibbs energy term is correlated to the excess Gibbs energy of aqueous single electrolyte systems at 298.15 K. With the Gibbs energy term identified, the enthalpy term is correlated to the excess enthalpy at 298.15 K. With the Gibbs energy term and the enthalpy term identified, the heat capacity term is correlated to the excess heat capacity at 298.15 K. Once the temperature coefficients are properly quantified by regressing data of mean ionic activity coefficient, excess enthalpy, and heat capacity at 298.15 K or the equivalents, the model provides a comprehensive thermodynamic framework to represent all thermodynamic properties of electrolyte solutions. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1244–1253, 2016