Characterization and Classification of Crude Oils Using a Combination of Spectroscopy and Chemometrics

Research has been carried out to the utility of chemometric models to predict long residue (LR) and short residue (SR) properties of a crude oil directly from its absorption or magnetic resonance spectrum. Such a combined spectroscopic-chemometric approach might offer a fast alternative for the elaborate crude oil assays that are currently used in petrochemical industries. Six different spectroscopic techniques have been explored: infrared (IR), near IR (NIR), Raman, UV-Vis, 1H-NMR and 13C-NMR. Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were selected as chemometric modeling techniques. Seven different LR properties have been studied, i.e., yield-long-on-crude (YLC), density (DLR), viscosity (VLR), pour point (PP), asphaltenes (Asph), carbon residue (CR) and sulfur content (S). Four SR properties as function of the atmospheric equivalent flash temperature (AFT) were considered, i.e., penetration depth (P), ring and ball (R&B), density (DSR) and viscosity (VSR). IR and NIR spectroscopy proved to be the most useful techniques for LR and SR prediction. Despite the high information content of the spectra, NMR performed less good, while UV-Vis and Raman spectroscopy turned out to be not useful. To improve the LR and SR prediction models, the combination of IR and NMR spectra as input for modeling was explored, but enhancement was not achieved. Furthermore, the influence of temperature treatments as a tool for improvement was investigated. The crude oils were exposed to 65C to reduce the contribution of volatile constituents in the IR spectra. Modeling of these spectra did not result in better LR or SR prediction models. Besides, the influence of the temperature on the IR spectra of the exposed crude oils measured at 20, 40 and 60C was studied. The PLS models based on these variable temperature data for LR predictions appeared to need fewer latent variables (LV’s) and a slight improvement in the SR predictions were obtained. In the next step, the applicability of the IR models to predict the LR properties of mathematically created IR spectra of blends was investigated. Physically prepared blends of two crude oils in various weight ratios could be mimicked by co-adding the IR spectra in the same ratio. The predictions of the LR properties of the artificial and the real blends were found to be largely the same. The robustness of the LR prediction models was tested by measuring the IR spectra on different instrumental set-ups. The models performed best in case the validation spectra were recorded on the same set-up as the calibration spectra. If another set-up was used, the accuracy decreased by a factor of 2. Finally, as an additional goal, PLS-modeling of IR spectra as a tool for sulfur speciation of crude oils was investigated. This application turned out to be limited compared to 2D gas chromatography analysis, which is widely used for this purpose. However, the models to predict the total sulfur concentration and the concentration of dibenzothiophenes and 3 different benzothiophene classes perform reasonably well. Based on the results of the described study, a computer program to predict LR and SR properties of crude oils and blends has been developed. This program is currently being tested on-site and the underlying methodology has been patented

Linkage abundance and molecular weight characteristics of technical lignins by attenuated total reflection-FTIR spectroscopy combined with multivariate analysis

The authors gratefully acknowledge the support of the Smart Mix Program of the Netherlands Ministry of Economic Affairs and the Netherlands Ministry of Education, Culture and Science.Lignin is an attractive material for the production of renewable chemicals, materials and energy. However, utilization is hampered by its highly complex and variable chemical structure, which requires an extensive suite of analytical instruments to characterize. Here, we demonstrate that straightforward attenuated total reflection (ATR)‐FTIR analysis combined with principle component analysis (PCA) and partial least squares (PLS) modelling can provide remarkable insight into the structure of technical lignins, giving quantitative results that are comparable to standard gel‐permeation chromatography (GPC) and 2D heteronuclear single quantum coherence (HSQC) NMR methods. First, a calibration set of 54 different technical (fractionated) lignin samples, covering kraft, soda and organosolv processes, were prepared and analyzed using traditional GPC and NMR methods, as well as by readily accessible ATR‐FTIR spectroscopy. PLS models correlating the ATR‐FTIR spectra of the broad set of lignins with GPC and NMR measurements were found to have excellent coefficients of determination (R2 Cal.>0.85) for molecular weight (Mn, Mw) and inter‐unit abundances (β‐O‐4, β‐5 and β‐β), with low relative errors (6.2–14 %) as estimated from cross‐validation results. PLS analysis of a second set of 28 samples containing exclusively (fractionated) kraft lignins showed further improved prediction ability, with relative errors of 3.8–13 %, and the resulting model could predict the structural characteristics of an independent validation set of lignins with good accuracy. The results highlight the potential utility of this methodology for streamlining and expediting the often complex and time consuming technical lignin characterization process.Publisher PDFPeer reviewe

Ibuprofen-loaded calcium phosphate granules : combination of innovative characterization methods to relate mechanical strength to drug location

This paper studies the impact of the location of a drug substance on the physicochemical and mechanical properties of two types of calcium phosphate granules loaded with seven different contents of ibuprofen, ranging from 1.75% to 46%. These implantable agglomerates were produced by either low or high shear granulation. Unloaded Mi-Pro pellets presented higher sphericity and mechanical properties, but were slightly less porous than Kenwood granules (57.7% vs 61.2%). Nevertheless, the whole expected quantity of ibuprofen could be integrated into both types of granules. A combination of surface analysis, using near-infrared (NIR) spectroscopy coupling chemical imaging, and pellet porosity, by mercury intrusion measurements, allowed ibuprofen to be located. It was shown that, from 0% to 22% drug content, ibuprofen deposited simultaneously on the granule surface, as evidenced by the increase in surface NIR signal, and inside the pores, as highlighted by the decrease in pore volume. From 22%, porosity was almost filled, and additional drug substance coated the granule surfaces, leading to a large increase in the surface NIR signal. This coating was more regular for Mi-Pro pellets owing to their higher sphericity and greater surface deposition of drug substance. Unit crush tests using a microindenter revealed that ibuprofen loading enhanced the mechanical strength of granules, especially above 22% drug content, which was favorable to further application of the granules as a bone defect filler

A Ziegler-type spherical cap model reveals early stage ethylene polymerization growth versus catalyst fragmentation relationships

Polyolefin catalysts are characterized by their hierarchically complex nature, which complicates studies on the interplay between the catalyst and formed polymer phases. Here, the missing link in the morphology gap between planar model systems and industrially relevant spherical catalyst particles is introduced through the use of a spherical cap Ziegler-type catalyst model system for the polymerization of ethylene. More specifically, a moisture-stable LaOCl framework with enhanced imaging contrast has been designed to support the TiCl4 pre-active site, which could mimic the behaviour of the highly hygroscopic and industrially used MgCl2 framework. As a function of polymerization time, the fragmentation behaviour of the LaOCl framework changed from a mixture of the shrinking core (i.e., peeling off small polyethylene fragments at the surface) and continuous bisection (i.e., internal cleavage of the framework) into dominantly a continuous bisection model, which is linked to the evolution of the estimated polyethylene volume and the fraction of crystalline polyethylene formed. The combination of the spherical cap model system and the used advanced micro-spectroscopy toolbox, opens the route for high-throughput screening of catalyst functions with industrially relevant morphologies on the nano-scale

Prediction of long-residue properties of potential blends from mathematically mixed infrared spectra of pure crude oils by partial least-squares regression models

Research has been carried out to determine the feasibility of partial least-squares (PLS) regression models to predict the long-residue (LR) properties of potential blends from infrared (IR) spectra that have been created by linearly co-adding the IR spectra of crude oils. The study is the follow-up of a recently developed method to predict LR and short-residue properties from IR spectra and which is currently the subject of PCT patent application WO 2008/135411 filed by Shell International Research Maatschappij B.V. It is found that the PLS prediction models for seven different LR properties [i.e., yield long on crude (YLC), density (DLR), viscosity (VLR), sulfur content (S), pour point (PP), asphaltenes (Asph), and carbon residue (CR)] enabled us to predict the LR properties of 16 blends in two ways. The first predictions were carried out on the IR spectra recorded from the physically prepared blend samples. Next, IR spectra were submitted to the PLS models that were created mathematically by linearly co-adding the IR spectra of the corresponding crude oils in the appropriate weight ratio. Minor differences in the real and artificial blend spectra have been observed, which have been assigned to nonlinear effects. However, preprocessing of the spectra, by subsequently taking the first derivative, multiplicative scatter correction (MSC), and mean centering (MC), resulted in predicted LR property values of the two parallel sets that are largely the same. It implies that mimicking blend spectra by mathematically mixing the IR spectra of crude oils is a valuable, fast, clean, and cheap alternative for the “dirty” and elaborate preparation and testing methods of real blends currently used in the laboratory. Besides, the method can be used as a rapid screening tool for a large series of potential blends

Partial least squares modeling of combined infrared, 1H NMR and 13C NMR spectra to predict long residue properties of crude oils

Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding 1H and 13C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study elaborates further on a recently developed and patented method to predict this type of information from only IR spectra. In the present study, PLS modeling was carried out for 7 different LR properties, i.e., yield long-on-crude (YLC), density (DLR), viscosity (VLR), sulfur content (S), pour point (PP), asphaltenes (Asph) and carbon residue (CR). Research was based on the spectra of 48 crude oil samples of which 28 were used to build the PLS models and the remaining 20 for validation. For each property, PLS modeling was carried out on single type IR, 13C NMR and 1H NMR spectra and on 3 sets of merged spectra, i.e., IR + 1H NMR, IR + 13C NMR and IR + 1H NMR + 13C NMR. The merged spectra were created by considering the NMR data as a scaled extension of the IR spectral region. In addition, PLS modeling of coupled spectra was performed after a Principal Component Analysis (PCA) of the IR, 13C NMR and 1H NMR calibration sets. For these models, the 10 most relevant PCA scores of each set were concatenated and scaled prior to PLS modeling. The validation results of the individual IR models, expressed as root-mean-square-error-of-prediction (RMSEP) values, turned out to be slightly better than those obtained for the models using single input 13C NMR or 1H NMR data. For the models based on IR spectra combined with NMR data, a significant improvement of the RMSEP values was not observed neither for the models based on merged spectra nor for those based on the PCA scores. It implies, that the commonly accepted complementary character of NMR and IR is, at least for the crude oil and bitumen samples under study, not reflected in the results of PLS modeling. Regarding these results, the absence of sample preparation and the straightforward way of data acquisition, IR spectroscopy is preferred over NMR for the prediction of LR properties of crude oils at site

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

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

Magnetic resonance imaging studies on catalyst impregnation processes: discriminating metal ion complexes within millimeter-sized γ-Al2O3 catalyst bodies

Magnetic resonance imaging (MRI) was used to study the impregnation step during the preparation of Ni/γ-Al2O3 hydrogenation catalysts with Ni2+ metal ion present in different coordinations. The precursor complexes were [Ni(H2O)6]2+ and [Ni(edtaHx)](2−x)− (where x = 0, 1, 2 and edta = ethylenediaminetetraacetic acid), representing a nonshielded and a shielded paramagnetic complex, respectively. Due to this shielding effect of the ligands, the dynamics of [Ni(H2O)6]2+ or [Ni(edtaHx)](2−x)− were visualized applying T2 or T1 image contrast, respectively. MRI was applied in a quantitative manner to calculate the [Ni(H2O)6]2+ concentration distribution after impregnation when it was present alone in the impregnation solution, or together with the [Ni(edtaHx)](2−x)− species. Moreover, the combination of MRI with UV−vis microspectroscopy allowed the visualization of both species with complementary information on the dynamics and adsorption/desorption phenomena within γ-Al2O3 catalyst bodies. These phenomena yielded nonuniform Ni distributions after impregnation, which are interesting for certain industrial applications

Prediction of long and short residue properties of crude oils from their infrared and near-infrared spectra

Research has been carried out to determine the feasibility of chemometric modeling of infrared (IR) and near-infrared (NIR) spectra of crude oils to predict the long residue (LR) and short residue (SR) properties of these samples. A novel method is described to predict short residue properties at different flashing temperatures based on the IR spectrum of a crude oil measured at room temperature. The resulting method is the subject of European patent application number 07251853.3 filed by Shell Internationale Research Maatschappij B.V. The study has been carried out on 47 crude oils and 4 blends, representing a large variety of physical and chemical properties. From this set, 28 representative samples were selected by principle component analysis (PCA) and used for calibration. The remaining 23 samples were used as a test set to validate the obtained partial least squares (PLS) regression models. The results demonstrate that this integrated approach offers a fast and viable alternative for the currently applied elaborate ASTM (American Society for Testing and Materials) and IP (Institute of Petroleum) methods. IR spectra, in particular, were found to be a useful input for the prediction of different LR properties. Root mean square error of prediction values of the same order of magnitude as the reproducibility values of the ASTM methods were obtained for yield long on crude (YLC), density (DLR), viscosity (VLR), and pour point (PP) , while the ability to predict the sulfur contents (S) and the carbon residue (CR) was found to be useful for indicative purposes. The prediction of SR properties is also promising. Modeling of the IR spectra, and to a lesser extent, the NIR spectra as a function of the average flash temperature (AFT) was particularly successful for the prediction of the short residue properties density (DSR) and viscosity (VSR). Similar results were obtained from the models to predict SR properties as a function of the yield short on crude (YSC) values. Finally, it was concluded that the applied protocol including sample pretreatment and instrumental measurement is highly reproducible and instrument and accessory independent