Abstract

Diesel molecular compositional model has important application for diesel quality prediction, blending, and molecular-level process model development. In this paper, different types of diesel molecular compositional and blending models were constructed based on the SU-BEM framework. More than 1500 representative molecules were selected to form the molecular structure library. The probability density functions (PDFs) combination was determined by experimental data and experience. A quadratic optimization strategy combining genetic algorithm with local optimization algorithm was adopted to improve the accuracy of the compositional model. The model results show good agreement with the experimental data. The diesel blending model was constructed at the molecular-level based on the above diesel compositional models. The properties of the blending model accord with the experimental regulations. It is proved that the compositional models and blending model constructed have high accuracy and strong prediction capability, and are applicable to the industrial process.