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由表1、2的RD和ARD值的大小可以看出，采用Apelblat方程的结果最好，经验多项式方程、λh方程关联的结果次之，即Apelblat方程在本文所研究的体系及温度范围更为适合。相对而言，Apelblat方程关联的相关系数R2均在0.99以上，平均相对误差（ARD）小于4%，最大的均根方偏差（105RSMD）为27.56；经验多项式方程的相关系数R2均在0.99以上，平均相对误差（ARD）小于4%，最大的均根方偏差（105RSMD）为48.16；λh方程关联的相关系数R2均在0.91以上，平均相对误差（ARD）小于18%，最大的均根方偏差（105RSMD）为84.73。图1-12更明显的展示各个方程的关联结果。 用Apelblat方程拟合后计算的数据与实验数据对比如下： |
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As can be seen by the values of RD and ARD in tables 1 and 2, the best results were obtained with Apelblat equation, followed by empirical polynomial equation and λh equation. Therefore, Apelblat equation was more suitable for the study system and the temperature range used in this paper. Relatively speaking, all the correlation coefficients R2 obtained with Apelblat equations were above 0.99, with the average relative error (ARD) less than 4% and the maximum root mean square deviation (105RSMD) of 27.56. Similarly, all the correlation coefficients R2 obtained with experience polynomial equation were also above 0.99, with the average relative error (ARD) less than 4% and the maximum root mean square deviation (105RSMD) was 48.16. However, all the correlation coefficients R2 obtained with λh equations were above 0.91, with the average relative error (ARD) less than 18% and the maximum root mean square deviation (105RSMD) was 84.73. Figure 1-12 showed a more straightforward results of the individual equations. The Data calculated with Apelblat equation fitting and the experimental data were compared as follows: |
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