Quantitative Structure–Property Relationship Model for Hydrocarbon Liquid Viscosity Prediction

The liquid viscosity of hydrocarbon compounds is essential in the chemical engineering process design and optimization. In this paper, we developed a quantitative structure–property relationship (QSPR) model to predict the hydrocarbon viscosity at different temperatures from the chemical structure. We collected viscosity data at different temperatures of 261 hydrocarbon compounds (C3–C64), covering n-paraffins, isoparaffins, olefins, alkynes, monocyclic and polycyclic cycloalkanes, and aromatics. We regressed the experimental data using an improved Andrade equation at first. Hydrocarbon viscosity versus temperature curves were characterized by only two parameters (named B and T0). The QSPR model was then built to capture the complex dependence of the Andrade equation parameters upon the chemical structures. A total of 36 key chemical features (including 15 basic groups, 20 united groups, and molecular weights) were manually selected through the trial-and-error process. An artificial neural network was trained to correlate the Andrade model parameters to the selected chemical features. The average relative errors for B and T0 predictions are 2.87 and 1.05%, respectively. The viscosity versus temperature profile was calculated from the predicted Andrade model parameters, reaching the mean absolute error at a value of 0.10 mPa s. We also proved that the established QSPR model can describe the viscosity versus temperature profile of different isomers, such as isoparaffins, with different branch degrees and aromatic hydrocarbons with different substituent positions. At last, we applied the QSPR model to predict gasoline and diesel viscosities based on the measured molecular composition. A good agreement was observed between predicted and experimental data (absolute mean deviation equals 0.21 mPa s), demonstrating that it has capacity to calculate viscosity of hydrocarbon mixtures.烃类化合物的液态黏度对于化工过程设计与优化至关重要。本研究构建了定量构效关系（quantitative structure–property relationship, QSPR）模型，可通过化学结构预测不同温度下的烃类黏度。我们收集了261种碳数范围为C3~C64的烃类化合物在不同温度下的黏度数据，涵盖正构烷烃、异构烷烃、烯烃、炔烃、单环及多环环烷烃与芳香烃。本研究首先采用改进型安德拉德方程（Andrade equation）对实验数据进行回归拟合，发现烃类黏度-温度曲线仅需两个参数（记为B与T0）即可表征。随后构建定量构效关系模型，以捕捉安德拉德方程参数对化学结构的复杂依赖关系。研究人员通过试错法手动筛选出36项关键化学特征，包括15个基础基团、20个联合基团以及分子量。随后训练人工神经网络（artificial neural network），以建立安德拉德方程参数与筛选出的化学特征之间的关联关系。参数B与T0预测的平均相对误差分别为2.87%与1.05%。基于预测得到的安德拉德方程参数计算得到的黏度-温度曲线，其平均绝对误差为0.10 mPa·s。本研究同时证实，所构建的定量构效关系模型可准确表征不同同分异构体的黏度-温度曲线，例如不同支化度的异构烷烃以及取代位置各异的芳香烃。最后，本研究基于实测的分子组成，将该定量构效关系模型应用于汽油与柴油的黏度预测。预测结果与实验数据吻合良好，平均绝对偏差为0.21 mPa·s，证实该模型具备计算烃类混合物黏度的能力。