Sulfur Speciation of Crude Oils by Partial Least Squares Regression Modeling of Their Infrared Spectra

Abstract

Research has been carried out to determine the feasibility of partial least-squares regression (PLS) modeling of infrared (IR) spectra of crude oils as a tool for fast sulfur speciation. The study is a continuation of a previously developed method to predict long and short residue properties of crude oils from IR and near-infrared (NIR) spectra. Retention data of two-dimensional gas chromatography (GC GC) of 47 crude oil samples have been used as input for modeling the corresponding IR spectra. A total of 10 different PLS prediction models have been built: 1 for the total sulfur content and 9 for the sulfur compound classes (1) sulfides, thiols, disulfides, and thiophenes, (2) aryl-sulfides, (3) benzothiophenes, (4) naphthenic-benzothiophenes, (5) dibenzothiophenes, (6) naphthenic-dibenzothiophenes, (7) benzonaphthothiophenes, (8) naphthenic-benzo-naphthothiophenes, and (9) dinaphthothiophenes. Research was carried out on a set of 47 IR spectra of which 28 were selected for calibration by means of a principal component analysis. The remaining 19 spectra were used as a test set to validate the PLS regression models. The results confirm the conclusion from previous studies that PLS modeling of IR spectra to predict the total sulfur concentration of a crude oil is a valuable alternative for the commonly applied physicochemical ASTM method D2622. Besides, the concentration of dibenzothiophenes and three different benzothiophene classes can be predicted with reasonable accuracy. The corresponding models offer a valuable tool for quick on-site screening on these compounds, which are potentially harmful for production plants. The models for the remaining sulfur compound classes are insufficiently accurate to be used as a method for detailed sulfur speciation of crude oils.