Toward Identification of Molecules in Ill-Defined Hydrocarbons Using Infrared, Raman, and Nuclear Magnetic Resonance (NMR) Spectroscopy

Abstract

The molecular composition of heavy oil and bitumen is critical for process design calculations and for the selection of production and refining processes, reaction schemes, and conditions that optimize their economic value. These materials remain ill-defined on a molecular basis. For example, diverse molecular structures have been proposed for asphaltenes on the basis of the same physical samples and analytical data [1H and 13C nuclear magnetic resonance (NMR) spectroscopy, mass spectroscopy, and elemental composition]. Molecule construction algorithms appear under constrained by these analytical data, particularly at the molecular subunit length scale (e.g., naphthenic and aromatic groups and aliphatic chains) from both an identification and a mass balance perspective. In this work, we address whether spectral data can provide additional constraints at this length scale that reduces the ambiguity of outcomes from molecule construction algorithms, by making use of spectral data for possible subunits. In this proof of concept investigation, infrared (IR), Raman, and 1H and 13C NMR spectra were computed using density functional theory (DFT) and the B3LYP/6-311G basis set. The molecular subunits present in more than 20 large molecules were probed by spectral subtraction based on spectral contributions from a library of small molecules, because it is at this length scale that the greatest uncertainty in molecule identification and construction algorithms appears to arise. Spectral subtraction using 1H NMR spectroscopy failed to identify molecular subunits in any of the more than 20 large molecules evaluated. 13C NMR spectroscopy identified only 3 large molecules. In contrast, joint use of IR and Raman spectroscopy identified more than 75% of the aromatic subunits present, and naphthenic and aromatic subunits were discriminated. Aliphatic chain length remained poorly defined, and the resulting molecule compositions are qualitative. It is expected that the results of this work will inform molecule construction and identification algorithms for ill-defined hydrocarbons.