关键词: 燃料油, 有机硫化物, 色谱保留行为, 遗传算法-多元线性回归法, BP神经网络, 列文伯格-马夸尔特-人工神经网络算法, 气相色谱-硫化学发光检测法

Abstract: Sulfur content is indispensable to evaluate the quality of fuel oil. Quantitative Structure Retention Relationship (QSRR) studies were performed for predicting the gas chromatographic retention times of 52 organic sulfur compounds in fuel oil on four different GC columns. The input parameters were selected by Genetic algorithm and multiple linear regression (GA-MLR) method. The final selected parameters including molecular connectivity indexes 1χ and2χ, electron energy (EE) and Y dipole(Dy) were then used as inputs of Error-back Propagation Network (BP) and levenberg-marquardt artificial neural network (L-M ANN). The three QSRR models all have strong stability and good predictive ability, all of the correlation coefficients based on above methods are higher than 0.98. The predictive ability of L-M ANN model is superior to other two models and indicates that L-M ANN can be used as an alternative modeling tool for QSRR studies.

Key words: fuel oil, organic sulfur compound, GC retention behavior, GA-MLR, BP neural network, L-M ANN, GC-SCD