Molecular Reconstruction of Petroleum Fractions: Application to Vacuum Residues from Different Origins
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Abstract:
A vacuum residue is a complex hydrocarbon mixture of several thousand different chemical species. Even today, no analytical technique is powerful enough to obtain the molecular detail that is required for the development of a detailed kinetic model. To overcome this drawback, a two-step reconstruction algorithm has been developed to build a representative set of molecules from partial analytical data. The first step, called Stochastic Reconstruction (SR), creates an initial mixture of molecules by a Monte Carlo sampling method. The second step, termed Reconstruction by Entropy Maximization (REM), modifies the molar fractions of the molecules in order to improve the representativeness of the generated mixture. The combined SR-REM algorithm creates a synthetic blend of molecules whose mixture properties are close to the analytical data of the petroleum fraction. The method has been applied to petroleum vacuum residue fractions from four different geographic locations with substantially different compositions. All cases are well represented, clearly illustrating the versatility of the SR-REM method. As an extension to this base algorithm, a novel indirect two-step reconstruction algorithm was developed, in which the SR step is used to build a single reference mixture. The set of molecules thus obtained is subsequently used in the second step to represent various petroleum fractions via the REM method. This allows to simultaneously reduce the computational burden and to represent the vacuum residue fractions with the same set of molecules. To validate this alternative approach, eight vacuum residues from different origins have been reconstructed with this technique. The results in terms of analysis prediction have shown a very good agreement.