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首页> 外文期刊>Fluid Phase Equilibria >Estimation of the enthalpy of vaporization at normal boiling temperature of organic compounds by a new group contribution method
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Estimation of the enthalpy of vaporization at normal boiling temperature of organic compounds by a new group contribution method

机译:新集团贡献方法估计有机化合物正常沸腾温度蒸发焓

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摘要

We need to know the thermo-physical properties of different compounds in order to design, model or optimize the industrial processes. Among various thermo-physical properties, enthalpy of vaporization at normal boiling temperature is one of the most important and useful properties in industrial processes. Although experimental data are available from various sources, but in order to complete these data, theoretical methods may be required to estimate and predict this property. The new group contribution method which can be regarded as a modification to the original Joback and Reid method gives more accurate estimations of the enthalpy of vaporization at normal boiling temperature of organic compounds. Group contribution values have been optimized using 3950 experimental data of organic components with a molecular weight range of 28–565 (g?mol?1) and number of carbon atoms range of 1–40. Checking the results of the new method on 3950 different organic compounds shows an average absolute error of 0.620?kJ/mol and the percentage average relative error of 1.683%. The results of the new method have been compared with the original method of Joback and Reid, and the Kolska, Ruzicka and Gani method. The results showed the better agreement between the predictions of the present method and the experimental data, comparing to the other group contribution methods.
机译:我们需要了解不同化合物的热物理性质,以设计,模拟或优化工业过程。在各种热物理性质中,正常沸腾温度蒸发焓是工业过程中最重要和最有用的性质之一。尽管从各种来源获得实验数据,但为了完成这些数据,可能需要理论方法来估计和预测这种属性。可以被视为对原始作业和Reid方法的改进的新组贡献方法,可以更准确地估计有机化合物的正常沸腾温度下的蒸发焓。使用3950个有机组分的实验数据优化了组贡献值,其中分子量范围为28-565(g?mol'1)和1-40的碳原子数。检查3950不同有机化合物的新方法的结果显示0.620Ω·kJ / mol的平均绝对误差,平均相对误差为1.683%。新方法的结果已经与乔克和Reid的原始方法进行了比较,以及Kolska,Ruzicka和Gani方法。结果表明,与其他组贡献方法相比,本方法的预测和实验数据之间的预测符合较好。

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