| Title | Deriving the molecular composition of middle distillates by integrating statistical modeling with advanced hydrocarbon characterization |
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| Author | Alvarez-Majmutov, A ; Chen, J; Gieleciak, R; Hager, D; Heshka, N; Salmon,
S |
| Source | Energy & Fuels vol. 28, no. 12, 2014 p. 7385-7393, https://doi.org/10.1021/ef5018169 |
| Image |  |
| Year | 2014 |
| Publisher | American Chemical Society (ACS) |
| Document | serial |
| Lang. | English |
| Media | on-line; digital |
| File format | PDF; HTML |
| Subjects | Science and Technology |
| Illustrations | histograms; graphs; tables; flow charts |
| Program | Program of Energy Research and Development (PERD) |
| Released | 2014 11 10 |
| Abstract | To fully advance our understanding of hydrocarbon conversion chemistry requires powerful analytical methods to qualitatively and quantitatively characterize complex petroleum fractions at the molecular
level. In the absence of such tools, an alternative solution is to model the molecular composition of hydrocarbon mixtures with limited analytical data. The objective of this study is to integrate modeling techniques with conventional and advanced
petroleum characterization methods to derive the composition of middle distillate fractions at the molecular level. In the present approach, analytical petroleum characterization data are used as input to computationally generate a mixture of
representative molecules that mimics the properties of the real sample. The representing molecules are constructed according to coherent chemical/thermodynamic criteria by Monte Carlo sampling of a set of statistical functions assigned to each
possible molecular feature. The assembled mixture is built on a large set of chemical species and is further optimized with the principle of Maximum Entropy. The approach is applied to simulating two middle distillates differing significantly in
hydrocarbon type composition and origin. The samples are experimentally characterized by standard and advanced analytical methods: density, simulated distillation, elemental analysis, hydrocarbon types/distributions and sulfur compound speciation by
two-dimensional gas chromatography with flame ionization detector (GC × GC-FID) and sulfur chemiluminescence detector (GC × GC-SCD), and 13C nuclear magnetic resonance (NMR), to obtain sufficient information for parameter fitting and model
validation. Simulation results showed that the model is capable of generating representative mixtures that reasonably match the actual physical samples in analytical properties and carbon number distributions. |
| Summary | (Plain Language Summary, not published)
This study presents an advanced molecular modeling and simulation approach to derive the molecular composition of petroleum samples by using fundamental
chemistry theory. In the present approach, a routine characterization of a petroleum sample is computationally transformed into a virtual mixture of representative hydrocarbon molecules that mimics the physical oil sample. The method was developed
and successfully validated with middle distillates samples (boiling range of 200-343°C) differing significantly in composition and origin. Besides accurately predicting the available analytical properties of the physical samples, this method can
generate the complete molecular composition of the oil sample, which is not possible with current analytical methods. The developed approach will enable predicting the molecular composition of oil sands bitumen and its upgraded products. This
information can be readily used to understand and predict the transformation of every single hydrocarbon molecule within oil sands bitumen throughout the processing and upgrading steps. Building modeling tools with such capabilities is particularly
important for developing break-through technologies, optimizing the performance of existing processes, and ultimately reducing green-house gas emissions and other environmental impacts during hydrocarbon processing. |
| GEOSCAN ID | 298909 |
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