Estimation of Aniline Point Temperature of Pure Hydrocarbons: A Quantitative Structure−Property Relationship Approach

In the present work, a quantitative structure−property relationship (QSPR) study is performed to predict the aniline point temperature of pure hydrocarbon components. As a powerful tool, genetic algorithm-based multivariate linear regression (GA-MLR) is applied to select most statistically effective molecular descriptors on the aniline point temperature of pure hydrocarbon components. Also, a three-layer feed forward neural network (FFNN) is constructed to consider the nonlinear behavior of appearing molecular descriptors in GA-MLR result. The obtained results show that the constructed FFNN can accurately predict the aniline point temperature of pure hydrocarbon components.

本研究开展了定量构效关系（quantitative structure−property relationship, QSPR）研究，以预测纯烃类组分的苯胺点温度。作为一种强有力的分析工具，基于遗传算法的多元线性回归（genetic algorithm-based multivariate linear regression, GA-MLR）被用于筛选对纯烃类组分苯胺点温度具有最优统计学有效性的分子描述符。此外，本研究还构建了三层前馈神经网络（three-layer feed forward neural network, FFNN），以考量GA-MLR结果中所涉及分子描述符的非线性行为。所得结果表明，所构建的FFNN能够精准预测纯烃类组分的苯胺点温度。