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TitleDeriving the molecular composition of middle distillates by integrating statistical modeling with advanced hydrocarbon characterization
AuthorAlvarez-Majmutov, AORCID logo; Chen, JORCID logo; Gieleciak, R; Hager, D; Heshka, N; Salmon, S
SourceEnergy & Fuels vol. 28, no. 12, 2014 p. 7385-7393,
PublisherAmerican Chemical Society (ACS)
Mediaon-line; digital
File formatPDF; HTML
SubjectsScience and Technology
Illustrationshistograms; graphs; tables; flow charts
ProgramProgram of Energy Research and Development (PERD)
Released2014 11 10
AbstractTo 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.

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