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