New Multiway Model for Identification of Crude Oil and Asphaltene

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A new multi-way model for identification of crude oil and asphaltene origin based on diffusion NMR spectroscopy Jelena Parlov-Vukovi#, Tomica Hrenar, Predrag Novak, Miha Friedrich, and Janez Plavec Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01358 • Publication Date (Web): 11 Jul 2017 Downloaded from http://pubs.acs.org on July 12, 2017

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A new multi-way model for identification of crude oil and asphaltene origin based on diffusion NMR spectroscopy

Jelena Parlov Vuković,a,* Tomica Hrenarb,* Predrag Novak,b,* Miha Friedrichc and Janez Plavec,c

a

INA-Industrija nafte d.d., Refining & marketing business division, Product development department, Lovinčićeva 4, 10002 Zagreb, Croatia

b

University of Zagreb, Faculty of Science, Department of Chemistry, Horvatovac 102a, HR10000, Zagreb, Croatia, Croatia c

Slovenian NMR Center, National Institute of Chemistry, SI-1000 Ljubljana, Slovenia

ABSTRACT

Identification and characterization of asphaltenes, the most polar and predominantly aromatic components of crude oil, still post a challenge for researchers in oil industry owing to their

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complex molecular architecture. Recently, much effort has been devoted to understanding their structure and physico-chemical properties. In this paper a combination of diffusion NMR spectroscopy (DOSY) and statistical multi-way methods was used to investigate petroleum samples and asphaltenes of different origin. Such multi-way TUCKER3 decomposition has been used for the first time to analyse two-dimensional matrix of complex NMR data for petroleum samples. DOSY NMR spectra of fifty samples (45 for establishing statistical model and 5 for its validation) were recorded and evaluated by an in-house developed code incorporating multi way analysis in order to set up a tool for predicting their identity and origin. A statistical model was developed that can identify and separate asphaltenes samples from those of crude oils, vacuum and atmospheric residues and resins which is not possible directly from the NMR spectra. Furthermore, the model clearly demonstrated that all asphaltene samples clustered into the same group irrespective of the geographical origin implicating their structural and size similarities. The validity of the model was further tested by analyzing additional set of asphaltenes. It has been demonstrated that the proposed approach is very useful for analyzing complex oil mixtures and has a potential for developing a robust quantitative model.

1. INTRODUCTION Crude oil, oil derivatives and products are very complex natural mixtures consisting of thousands of components ranging in size and structure. The characterization of crude oils, especially heavy crude oils and their residues is an important task in oil industry with regard to quality of final products. One of the main problem during refining process is associated with the presence of the compounds called asphaltenes. Asphaltenes are the most polar components from crude oils with molecular weights between 500 and 1000 Da.1-5 Their characterization is rather


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difficult because they consist of large number of molecules whit different chemical and physical characteristics. A nice review of data collected on asphaltene characterization has recently been published by Mullins and cooworkers.5 In general, asphaltenes contain condensed aromatic and saturated rings, aliphatic moieties and some heteroatoms. Asphaltenes are insoluble in light nalkane (n-heptane or n-pentane) and soluble in toluene and benzene. They may aggregate and precipitate during petroleum processing due to changes in temperature, pressure and exposure to solid surface. This can lead to serious problems in production, transportation and storage. Some of the problems include blocking of production pipes, reduction in oil flow, catalyst deactivation and poisoning. Asphaltenes were found to form aggregates usually above the concentration of 100 mg/L.5 NMR spectroscopy has proven very useful technique to study crude oils and asphaltenes.1-11 The main characteristic of proton and carbon NMR spectra of petroleum samples is a severe signal overlapping which prevents unambiguous spectral analysis and resonance assignments. Hence, a method is needed to disentangle the spectral complexity. It has already been reported that diffusion-ordered (DOSY) NMR spectroscopy could serve as one such method to provide further insights into nature and structure of asphaltenes.3-5, 7-10 DOSY can provide information on individual components present in a mixture without their physical separation based on their translational diffusion properties. DOSY NMR spectrum is a pseudo-two dimensional spectrum where chemical shifts represent one dimension while diffusion coefficients represent the other one.12 The diffusion coefficient is dependent upon the size and shape of a molecule or an aggregate. Various kinds of aggregates in petroleum samples of different origin can be separated and identified according to their diffusion coefficients. However, in spite of the fact that DOSY provides additional information with respect to one-dimensional NMR spectra, differentiation


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among DOSY spectra of crude oil, atmospheric residues and asphaltenes is still not straightforward just by spectral inspection (see Figure 1). Therefore, further evaluation is needed by employing statistical methods. Up to date, no reports on statistical evaluation of DOSY NMR spectra could be find in the literature. In this paper we explore the potential of the DOSY NMR spectroscopy coupled with statistical multi-way analysis (MWA) to identify and classify petroleum samples. Multi-way analysis (MWA) incorporating an in-house developed code deals with large complex data sets represented as multidimensional arrays. The considerable advantage in using MWA coupled with DOSY spectra, rather than with one-dimensional NMR spectra, is to take into consideration the additional diffusion dimension, enabling the use of advanced trilinear decomposition algoritam which is not possible with other statistical approaches such as PCA or ICA for example. This algoritm allows for obtaining classification data by using simultanious information from 1H NMR spectra and from diffusion properties even when experimental data looks almost the same (Figure 1) and in case of significantly overlapped resonances. We evaluated 47 DOSY spectra of crude oils, vacuum residues, atmospheric residues, resins and asphaltenes (50 as training set to establish the model and five for its validation).

2. EXPERIMENTAL SECTION 2.1. Samples All crude oil samples were of different origin and processed during 2013-2015 in Croatian refinery. The distillation and fractionation of the crude oil samples were made on fully automatic


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distillation apparatuses EuroDist System True Boiling Point and EuroDist System Postill in accordance with standard methods ASTM D 2892-13 and ASTM D 5236-13.13,14 The crude oils were fractionated in the several fractions including those obtained above 550 °C. The crude oils were obtained from different regions in Croatia (Croatia 1 – 2), Africa (Africa 1) and Asia (West Asia 1 – 4, North Asia 1 – 2

and South Asia 1 – 2).

Numbering and

designation of all samples are given in the Supporting Information (Table S1). Asphaltenes were extracted from crude oil samples, heavy vacuum and atmospheric residue fractions by using ASTM D 6560-12 method.15 It is the standard test method for the determination of the heptane insoluble asphaltene content. A test portion of the sample was mixed with heptane, the mixture was heated under reflux and the precipitated asphaltenes, waxy substances, and inorganic material were collected on a filter paper. The waxy substances were removed by washing with hot heptane in an extractor. After removal of the waxy substances, the asphaltenes were separated from the inorganic material by dissolution in hot toluene and the extraction solvent was evaporated.

2.2. NMR Measurements NMR spectra were recorded on an Agilent Technologies DD2 600 MHz NMR spectrometer equipped with 5 mm 1H{13C/15N} 13C enhanced Cold Probe (with Salt Tolerance Conversion kit) in CDCl3 as a solvent. DOSY experiments were carried out using DgcsteSL_cc (DOSY gradient compensated stimulated echo with spin lock and convection compensation) pulse sequence with gradient strengths from 2.4 to 57.1 G cm-1, diffusion gradient length of 3.0 ms and diffusion delay of 50.0 ms. Spectra were obtained with a 14368 Hz sweep width, 0.682 s acquisition time,


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7.0 µs (90˚) pulse width, 4 s delay time and 16 scans at temperature of 298 K. Chemical shifts were referenced to the signal of TMS.

2.3. Multi-way Analysis Multi-way analysis (MWA) is the numerical analysis of large data sets represented as multidimensional arrays (or a higher order tensor). This tensor is composed from sequences of numbers dependent on different physical dimensions. In our case, the 3rd order tensor consist of two-dimensional NMR DOSY spectra of different petroleum samples (Figure 1). Each NMR DOSY spectrum had 15×10928 records providing the total dimensions of the 3rd order tensor: 15×10928×47. The data in this 3rd order tensor depends on three independent variables: chemical shift, magnetic gradient pulse amplitude and diversity of sample. To extract the qualitative classification information, we used MWA as a tool that will allow detection of differences and similarities among the samples based on these 2-dimensional NMR DOSY data sets (2-dimensional because the intensities depends on two independent variables, chemical shift and magnetic gradient pulse amplitude). Each 2-dimensional NMR DOSY spectrum is finally represented as a point in reduced space. MWA on the set of DOSY NMR spectra placed in the 3rd order tensor is carried out using the 3-way decomposition model TUCKER3.16 X = AG(C ⊗ B)τ + E where A, B, and C are 1st-way, 2nd-way, and the 3rd-way loadings matrices, respectively (⊗ represents Kronecker matrix product). The G matrix is called the core-array and is associated


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with the amount of variation explained by loadings in the different modes (Figure 2). Multi-way analysis is performed by using our code moonee17,18 developed in-house with option progressive where the starting model dimensions (1,1,1) were gradually increased up to (5,5,5) giving total of 125 tested models.

3. RESULTS AND DISCUSSION 3.1. NMR Spectroscopy In this study we applied proton and DOSY NMR spectroscopy to extract experimental data for a number of crude oils, vacuum residues, atmospheric residues and isolated asphaltenes of different origin. We were keen to explore a potential of multi-way statistical approach to analyze the obtained complex spectral data in order to make sample identification, classification and a clear distinction among them. Proton (Figure 3) NMR spectra of the studied samples exhibit severely overlapped sresonances and straightforward spectral analysis and assignment are extremely difficult and prone to errors. The spectral complexity can be reduced to some extent by using two-dimensional experiments.11 Here we apply DOSY NMR technique which represents a pseudo two-dimensional experiment where a second dimension is introduced by translational diffusion coefficients. Hence, components with different size and shape properties display different motional behavior which is reflected in their diffusion coefficients. Mixture components will be separated in the diffusion dimension which makes the spectral analysis much easier. As already mentioned DOSY NMR spectra have proven useful in structure analysis and study of aggregation process of asphaltenes.3-5,7-10 We have recently shown that for an accurate measurement of diffusion coefficients it is necessary to use a stimulated echo pulse sequence


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with convection compensation module. Convection currents caused by small temperature gradients can cause signal decay and affect diffusion coefficient measurements leading to incorrect values.19 In our previous paper7 we used principal component regression model for quantitative prediction of asphaltenes in vacuum gas oils and vacuum residues from the integrated proton NMR spectra. Here we would like to statistically evaluate DOSY spectral data and see whether this can provide additional valuable information with respect to identity of petroleum samples and asphaltenes and to explore its potential for quantitative purposes. Typical proton spectrum of the crude oil sample and its isolated asphaltene is depicted in Figure 3 and typical DOSY spectra of crude oil and the corresponding asphaltene samples are given in Figures 4, and 5, respectively. The advantage of the DOSY spectrum with regard to the simple proton spectrum is clearly demonstrated in both Figures 3 and 4. As can be seen in Figure 3, DOSY spectra of crude oil samples display separated signals belonging to species with different diffusion properties. These signals are overlapped in chemical shift dimension (Figure 3) and thus cannot be unambiguously identified. The same is true for the DOSY spectra of asphaltene samples isolated from the respective crude oils as shown in Figure 4 where several aggregates can be recognized according to their different diffusion coefficients. Although different components present in oil samples can be separated in the diffusion dimension by their mass and size, it is not straightforward to identify and differentiate which spectra belong to asphaltenes and which to other oil samples. So the DOSY spectra depicted in Figure 1 look almost the same but the samples are significantly separated in the classification space.


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Therefore, to evaluate crude oils, their residues and isolated asphaltenes we have recorded DOSY spectra of 45 different samples and apply a novel statistical approach developed in-house, with the aim of establishing a model for their discernment. The model was then tested by the analysis of additional asphaltene samples isolated from two West Asia 3 and three West Asia 4 heavy crude oils. 3.2. Multi-way Analysis Using the TUCKER3 decomposition model for DOSY spectra with different dimensions, we performed progressive search starting from model (1,1,1), which explained 42.37 % of total variance, up to the model (5,5,5), which explained 87.62 % of total variance among the data. Each dimension was gradually increased by 1 giving the total number of the models 5×5×5=125. Explained variances for all investigated models are presented in Figure 6.

Subsequently, the model (5,5,5) was chosen for further analysis, and from this model the first three components were used for classification of samples and visualization. These three components describe 68.45 % of total variance, and their 3rd-way loadings plots are presented in Figure 7. This percentage is high enough to ensure that the most important properties of the investigated systems important for the proper classification are retained within this statistical model. The computed classification of petroleum samples with respect to the asphaltene samples was excellent (Figure 7). 25+5 asphaltene samples are placed in single cluster, well separated from the others confirming the possibility of differentiation among petroleum samples. The fact that asphaltenes cluster into the same group regardless of their origin is a strong indication that their size and structural characteristics resemble.


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There is a "trajectory" type connection between several petroleum samples that can be seen from Figures 7 and 9 giving indication that even quantitative analysis of asphaltene content could be performed, but the independent analytical method should be introduced to confirm this hyphotesis. Since the first component covers the most variance among the asphaltene samples, from Figure 7a one can see that this component alone could present the basis for quantitative analysis. Plotting this component reveals the main differences among the set of DOSY NMR (Figure 8). These signals correspond to the variation in content of aliphatic hydrogen atoms. To test the validity of the model, we used 5 additional asphaltene samples isolated form West Asia 3 crude oil labeled with A and B, and samples isolated form West Asia 4 labeled with C, D and E, and run them within the selected model (5,5,5). Obtained results, which confirm the validity of our model are presented in Figure 9.

CONCLUSION We have demonstrated that a combination of DOSY NMR spectroscopy and multi-way statistical analysis represents a valuable tool for identification and classification of petroleum samples of different origin. Furthermore, a statistical decomposition model for evaluation of DOSY spectra has been proposed. Hence, asphaltene samples can be identified, clustered and well separated from the other samples. The validity of the model was tested and proved by using 5 additional independent asphaltene samples. The position of each of these samples in the reduced space spanned by only three principal components confirms the validity of the


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established model for classification of asphaltenes. The obtained results implicate that structures and size of asphaltenes are closely related irrespective of the fact that they have different geographical origin. Furthermore, the first principal component which covered the most variance, could serve as the basis for further investigations aimed at quantitative determination of asphaltenes in the complex petroleum samples.


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Figure 1. 3D representation of NMR DOSY spectra of selected oil samples. a) Africa 1 atmospheric residue b) West Asia 2 crude oil c) Croatia 2 asphaltenes and d) North Asia 2 atmospheric residue.


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Figure 2. Graphical representation of the TUCKER3 model.


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Figure 3. Proton spectra of a) Asia 4 crude oil and b) isolated asphaltene samples.


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Figure 4. DOSY spectra of the Africa 1 (up) and the Croatia 1 (down) crude oils.


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Figure 5. DOSY spectra of Africa 1 asphaltenes (up) and Croatia 1 asphaltenes (down).


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Figure 6. Explained variance in dependence of dimensionality for TUCKER3 model used in decomposition of 3rd-order tensor (NMR DOSY spectra).


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Figure 7. Classification of petroleum samples using the 3rd-way loadings spanned in the space of first three principal components calculated by TUCKER3 decomposition. (Asphaltene samples used for classification and validation are given in red and green color, respectively.)


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Figure 8. The first component loadings of the 2nd-way obtained by TUCKER3 decomposition.


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Figure 9. Classification of petroleum samples in expanded region using the 3rd-way loadings spanned in the space of first two principal components calculated by TUCKER3 decomposition. (Asphaltene samples used for classification and validation are given in red and green color, respectively.) Corresponding Authors * [email protected] * [email protected] * [email protected]

Notes The authors declare no competing financial interest

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Supporting Information. Table S1. Numbering and designation of investigated petroleum samples.


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New Multiway Model for Identification of Crude Oil and Asphaltene

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