Development of Group Contribution Method for Predicting Liquid Viscosities Applicable to High Pressure from Low Pressure
低压适用于高压的液体粘度群贡献法的发展
基本信息
- 批准号:14550744
- 负责人:
- 金额:$ 1.15万
- 依托单位:
- 依托单位国家:日本
- 项目类别:Grant-in-Aid for Scientific Research (C)
- 财政年份:2002
- 资助国家:日本
- 起止时间:2002 至 2003
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
As the fundamental equation for predicting kinematic viscosities at normal pressure, the following absolute reaction velocity theory has used.ln(νM)=Σx_iln(ν_iM_i)-ΔG^E/RTThe ASOG-VSICO was proposed in order to obtain the excess Gibbs free energy using ASOG group contribution method. The group pair parameters have been determined for the group pairs relating to CH2, ArCH, CyCH, OH, H2O, CO, COO, CCl3, and CCl4. The kinematic viscosities were predicted for the systems containing paraffines, aromatic hydrocarbons, alcohols, water, ketones, esters, and chlorides. The absolute arithmetic average deviations between experimental and predicted values are 4.15 and 5.03% for binary and ternary systems, respectively.To propose the predictive method of pure component viscosities using group contribution, the following Andrade uquation was used.lnη=B(1/T-1/T_0)The parameters B and T_0 were predicted using neural network method. The viscosities can be predicted for the paraffines, olefins, alkyns, aromatic hydrocarbons, chlorides, bromides alcohols, ketones, esters, ethers, aldehydes, and organic acids.The predictive method to predict high- pressure viscosities using low-pressure viscosities was proposed. Fundamental equations are Peng-Robinson equation of state and excess free energy mixing rule. The high-pressure viscosities for alcohols + water systems were predicted with the average deviations of 2%.
作为在法向压力下预测运动粘度的基本方程,采用了以下的绝对反应速度理论。ln(νM)=Σx_iln(ν_iM_i)-ΔG^E/ rt为了利用ASOG基团贡献法获得多余的吉布斯自由能,提出了ASOG- vsico。确定了CH2、ArCH、CyCH、OH、H2O、CO、COO、CCl3、CCl4基团对的参数。预测了含石蜡、芳香烃、醇、水、酮、酯和氯化物的体系的运动粘度。对于二、三元体系,实验值与预测值的绝对算术平均偏差分别为4.15%和5.03%。为了提出基于基团贡献的纯组分粘度预测方法,使用了如下的Andrade方程。lnη=B(1/T-1/T_0)采用神经网络方法对参数B和T_0进行预测。可以预测石蜡、烯烃、烷基、芳香烃、氯化物、溴化物、醇类、酮类、酯类、醚类、醛类和有机酸的粘度。提出了用低压粘度预测高压粘度的预测方法。基本方程是Peng-Robinson状态方程和过量自由能混合规则。对醇+水体系的高压粘度进行了预测,平均偏差为2%。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Prediction of Kinematic Viscosities for Binary and Ternary Liquid Mixtures with an ASOG-VISCO Group Contribution Method
使用 ASOG-VISCO 群贡献法预测二元和三元液体混合物的运动粘度
- DOI:
- 发表时间:2005
- 期刊:
- 影响因子:0
- 作者:K.Tochigi;K.Yoshino;V.K.Rattan
- 通讯作者:V.K.Rattan
Prediction of the Liquid Viscosities of Pure Components and Mixtures Using Neural Network and ASOG Group contribution Method
使用神经网络和 ASOG 群贡献法预测纯组分和混合物的液体粘度
- DOI:
- 发表时间:2004
- 期刊:
- 影响因子:0
- 作者:A.Murata;K.Tochigi;H.Yamamoto
- 通讯作者:H.Yamamoto
K.Tochigi, K.Yoshino, V.K.Rattan: "Prediction of Kinematic Viscosities for Binary and Ternary Liquid Mixtures with an ASOG-VISCO Group Contribution Method"International Journal of Thermophysics. In press. (2004)
K.Tochigi、K.Yoshino、V.K.Rattan:“使用 ASOG-VISCO 群贡献法预测二元和三元液体混合物的运动粘度”国际热物理学杂志。
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
A.Murata, K.Tochigi, H.Yamamoto: "Prediction of the Liquid Viscosities of Pure Components and Mixtures Using Neural Network and ASOG-Group Contribution Methods"Molecular Simulation. In press. (2004)
A.Murata、K.Tochigi、H.Yamamoto:“使用神经网络和 ASOG 基团贡献方法预测纯组分和混合物的液体粘度”分子模拟。
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TOCHIGI Katsumi其他文献
TOCHIGI Katsumi的其他文献
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{{ truncateString('TOCHIGI Katsumi', 18)}}的其他基金
Measurement, Correlation and Prediction of Phase Equilibria Necessary For Developing Applicable Method of Ionic Liquids As a Separation Solvent
开发离子液体作为分离溶剂的适用方法所需的相平衡测量、关联和预测
- 批准号:
17560667 - 财政年份:2005
- 资助金额:
$ 1.15万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Development of Modified SRK Group Contribution Method for Predicting High-Pressure Physical Properties for Polymer Solutions
改进的 SRK 基团贡献方法的开发用于预测聚合物溶液的高压物理性质
- 批准号:
11650789 - 财政年份:1999
- 资助金额:
$ 1.15万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Measurement and Prediction, by EOS-group contribution Method, Necessary for separating phenols in Wastewater
分离废水中酚所需的 EOS 基团贡献法测量和预测
- 批准号:
07650908 - 财政年份:1995
- 资助金额:
$ 1.15万 - 项目类别:
Grant-in-Aid for Scientific Research (C)