Characterization of Crude Oil Products Using Data Fusion of Process Raman, Infrared, and Nuclear Magnetic Resonance (NMR) Spectra

Abstract

Process Raman, infrared (IR), and nuclear magnetic resonance (NMR) analyses are currently being performed in industrial settings for the monitoring of large scale reactions. These methods offer a distinct set of advantages such as no sample preparation and rapid noninvasive remote analysis. Process Raman spectroscopy offers information pertaining to the molecular backbone as well as symmetrical non-polar groups. IR spectroscopy yields information pertaining to hydrogen bonding and asymmetric polar groups. NMR spectrometry provides highly resolved information detailing specific proton environments. These distinct spectral characteristics present a unique opportunity to join together the Raman, IR, and NMR spectra to give one set of “fused” spectra containing complementary information from two sources (Raman and IR) and one orthogonal source (NMR) that describe an industrial process. Data fusion enables process modeling and control to be performed using a single data set. This study has applied the concept of data fusion to characterize a series of crude oil fractions. After collection, the respective spectra were scaled and fused together to form one contiguous spectrum. The multivariate models built using the fused data had a root mean square error of prediction (RMSEP) of 0.307%, a significant reduction in the prediction errors when compared to models built using the separate spectra. The use of data fusion with multiple analytical measurements reduces the error associated with inferential property models for industrial process monitoring, thus allowing for increased understanding and control of an industrial process.

Combined chemometric analysis of 1H NMR, 13C NMR and stable isotope data to differentiate organic and conventional milk

“…Several studies described data fusion techniques combining chemical data of different analytical methods (Dearing, Thompson, Rechsteiner, & Marquardt, 2011;Mas, Tauler, & de Juan, 2011;Pere-Trepat & Tauler, 2006;Smilde, van der Werf, Bijlsma, van der Werff-van der Vat, & Jellema, 2005;Vera et al, 2011;Xu, Correa, & Goodacre, 2013). Most of them applied fused chromatographic techniques with a complementary analytical tool (Forshed, Idborg, & Jacobsson, 2007;Mas et al, 2011;Pere-Trepat & Tauler, 2006;Vera et al, 2011).…”

Moffat-Swern Oxidation of Alcohols: Translating a Batch Reaction to a Continuous-Flow Reaction

“…By using PLS regression models built from Raman spectra, Cooper et al demonstrated accurate predictions of chemical and physical properties, including the octane number and vapor pressures of petrochemical solutions [24]. This was further demonstrated by Dearing et al who predicted physical properties of crude oil distillates when combining IR, Raman, and nuclear magnetic resonance (NMR) information into data-fused models [25].…”

Average Molecule Construction of Petroleum Fractions Based on 1H‐NMR

“…For example, the viscosity or American Petroleum Institute (API) gravity of crude oil has been successfully predicted using low‐field nuclear magnetic resonance (NMR) . Dearing et al combined Raman, infrared (IR), and NMR spectroscopic data to predict the API of crude oil products. De Peinder et al combined IR and NMR spectra to establish a correlation model between the composition and properties of the atmospheric residue using principal component analysis and partial least squares.…”