Deriving the Molecular Composition of Vacuum Distillates by Integrating Statistical Modeling and Detailed Hydrocarbon Characterization

详细信息    查看全文

• 作者：Anton Alvarez-Majmutov ; Rafal Gieleciak ; Jinwen Chen
• 刊名：Energy & Fuels
• 出版年：2015
• 出版时间：December 17, 2015
• 年：2015
• 卷：29
• 期：12
• 页码：7931-7940
• 全文大小：574K
• ISSN：1520-5029

文摘

Characterization of the chemical composition of petroleum vacuum distillate fractions is essential to advance the understanding of the fundamental chemistry of refining processes, such as fluid catalytic cracking and hydrocracking. This is a challenging task, primarily as a result of the limitations of current analytical techniques to deal with heavy hydrocarbon samples. A different path toward this goal is through the use of hydrocarbon composition modeling techniques to derive the molecular make up of petroleum fractions with limited analytical data. The purpose of this study is to demonstrate this approach for simulating the molecular composition of vacuum distillates. The method consists of generating a computational mixture of representative hydrocarbon molecules that mimics the properties of an actual oil sample. Molecules are built according to the specific chemistry of vacuum distillates with a Monte Carlo algorithm, and the abundance of each molecule is optimized by entropy maximization. The model was applied to simulate two vacuum gas oil samples differing substantially in chemical composition and geographic origin. The samples were experimentally characterized in detail to obtain the necessary model inputs. Simulations revealed that the model adequately predicts the analytical properties and carbon number distributions of the two samples, proving its capability to capture a wide range of distinct vacuum distillate chemistries.