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

Measurement of partial conversions during the solution copolymerization of styrene and butyl acrylate using on-line Raman spectroscopy

The copolymn. of styrene and Bu acrylate in dioxane was monitored by online Raman spectroscopy. The calcn. of the individual monomer concns. on the basis of the individual vinyl peaks is not straightforward for this system, as these bands are overlapping in the Raman spectrum. To tackle this problem, univariate and multivariate approaches were followed to obtain monomer concns. and the results were validated by ref. gas chromatog. data. In the univariate anal., linear relations between various monomer peaks were used to calc. monomer concns. from the Raman data. In principal component anal., the main variation in the spectra could be ascribed to conversion of monomer. Furthermore, principal component anal. pointed out that the second-largest effect in the spectra could be attributed to expt.-to-expt. variation, probably attributable to instrumental factors. In the multivariate partial least squares regression approach, single factor models were used to calc. monomer concns. Both the univariate and the partial least squares regression approaches proved successful in calcg. the individual monomer concns., showing very good agreement with off-line gas chromatog. data. [on SciFinder (R)

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

New method for faecal fat determination by mid-infrared spectroscopy, using a transmission cell:an improvement in standardization

Current techniques used in clinical laboratories for faecal fat determination, such as the Van de Kamer method, are not very accurate or precise. This became apparent when results obtained by different laboratories were compared, and could explain the disappointing performance of near-infrared and mid-infrared spectroscopy since the accuracy of these techniques depends upon the accuracy of the calibration used (i.e. inaccurate wet chemical analysis). In order to improve standardization, we developed and tested a new quantitative method in three laboratories, based on Fourier transform infrared (FT-IR) spectroscopy. Fatty acids were extracted from faecal samples with acidified petroleum ether-ethanol and the extracts were dried and dissolved in chloroform. An infrared spectrum of the extracts was recorded in the range 4000-650 cm(-1), using an infrared transmission cell. Standard mixtures of stearic and palmitic acids (65:35) were used for calibration. Quantification was based on the absorbance band of the CH2 group (2855 cm(-1)) of free fatty acids and fatty acid glycerol esters. The calibration curve showed excellent linearity. The correlation coefficient between the titrimetric Van de Kamer and FT-IR methods was 0.96 (y = 1.12x-0.02, standard error of prediction = 0.89 g% fat). No significant difference was found when the FT-IR results of 28 faecal samples from patients were compared between two different university hospital laboratories. The new FT-IR method, using primary standards, is simple and rapid, and provides satisfactory intra- and inter-laboratory precision for the diagnosis and monitoring of steatorrhoea

Photo-spectroscopy of mixtures of catalyst particles reveals their age and type

Within a fluid catalytic cracking (FCC) unit, a mixture of catalyst particles that consist of either zeolite Y (FCC-Y) or ZSM-5 (FCC-ZSM-5) is used in order to boost the propylene yield when processing crude oil fractions. Mixtures of differently aged FCC-Y and FCC-ZSM-5 particles circulating in the FCC unit, the so-called equilibrium catalyst (Ecat), are routinely studied to monitor the overall efficiency of the FCC process. In this study, the age of individual catalyst particles is evaluated based upon photographs after selective staining with substituted styrene molecules. The observed color changes are linked to physical properties, such as the micropore volume and catalytic cracking activity data. Furthermore, it has been possible to determine the relative amount of FCC-Y and FCC-ZSM-5 in an artificial series of physical mixtures as well as in an Ecat sample with unknown composition. As a result, a new practical tool is introduced in the field of zeolite catalysis to evaluate FCC catalyst performances on the basis of photo-spectroscopic measurements with an off-the-shelf digital single lens reflex (DSLR) photo-camera with a macro lens. The results also demonstrate that there is an interesting time and cost trade-off between single catalyst particle studies, as performed with e.g. UV-vis, synchrotron-based IR and fluorescence micro-spectroscopy, and many catalyst particle photo-spectroscopy studies, making use of a relatively simple DSLR photo-camera. The latter approach offers clear prospects for the quality control of e.g. FCC catalyst manufacturing plants