Petroleum Science >2022,??lssue 2:??- DOI: https://doi.org/10.1016/j.petsci.2022.01.002
Diesel molecular composition and blending modeling based on SU-BEM framework Open?Access
文章信息
作者：Yue-Ming Guan, Dong Guan, Cheng Zhang, Sheng-Hua Yuan, Guang-Qing Cai, Lin-Zhou Zhang,
作者單位：
投稿時間：
引用方式：Yue-Ming Guan, Dong Guan, Cheng Zhang, Sheng-Hua Yuan, Guang-Qing Cai, Lin-Zhou Zhang, Diesel molecular composition and blending modeling based on SU-BEM framework, Petroleum Science, Volume 19, Issue 2, 2022, Pages 839-847,
文章摘要
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.
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.
關(guān)鍵詞
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Molecular compositional model; Molecular blending model; SU-BEM Framework; Diesel