Corresponding States Method for Determination of the Viscosity of Gases at Atmospheric Pressure

The accuracy and predictability of empirical/semiempirical
correlations for evaluation of the physicochemical properties of chemical
compounds are of much interest in chemical industry. In this study,
our objective is applying the Gene Expression Programming
(GEP) mathematical algorithm to propose a correlation based on the
corresponding states method to calculate/estimate the gas viscosity
of about 1600 chemical compounds (mostly organic) from 81 chemical
families at different temperatures and atmospheric pressure. The parameters
of the correlation include the temperature, critical temperature,
critical pressure, and molecular weight. Around 13 000 experimental
gas viscosity data are randomly selected for developing the correlation,
and about 8500 data are treated for checking its prediction capability.
The obtained statistical parameters including average absolute relative
deviations (AARD %) of the results from the experimental data (about
7%) indicate the accuracy and applicability of the presented
correlation along with its simplicity compared with the most widely
used corresponding states method (with AARD of about 9%) available
in open literature.

用于评估化合物理化性质的经验/半经验关联式，其精度与可预测性始终是化工行业的重点关注方向。本研究采用基因表达式编程（Gene Expression Programming, GEP）数学算法，基于对应状态法构建关联式，以在不同温度与常压条件下，计算或估算81类化学家族共约1600种化合物（以有机物为主）的气体黏度。该关联式的输入参数包含温度、临界温度、临界压力及分子量。本研究随机选取约13000组实验气体黏度数据用于关联式的开发，并预留约8500组数据用于验证其预测能力。所得统计参数（如与实验数据对比的平均绝对相对偏差（AARD %）约为7%）表明，本研究提出的关联式兼具优异精度与适用性，且相较于公开文献中应用最广泛的对应状态法（其AARD约为9%）更为简洁。