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Sep 13, 2016 - Andrea Gomez-Escudero,. † and Fernando A. Rojas-Ruiz*,†,‡. †. Instituto Colombiano del Petróleo (ICP), Ecopetrol, Piedecuesta, Santande...
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Combination of (-) ESI and (+) APPI FT-ICR Mass Spectrometry as a Quantitative Approach of Acid Species in Crude Oils. Jorge Armando Orrego-Ruiz, Andrea Gomez-Escudero, and Fernando A. Rojas-Ruiz Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b01524 • Publication Date (Web): 13 Sep 2016 Downloaded from http://pubs.acs.org on September 18, 2016

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Combination of (-) ESI and (+) APPI FT-ICR Mass Spectrometry as a Quantitative Approach of Acid Species in Crude Oils. Jorge A. Orrego-Ruiz,† Andrea Gomez-Escudero † and Fernando A. Rojas-Ruiz †††* † ECOPETROL, Instituto Colombiano del Petróleo, Piedecuesta, Santander 681018, Colombia. ††

Universidad Industrial de Santander, Grupo de Investigación Recobro Mejorado, Bucaramanga, Santander, Colombia

Graphical Abstract

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Combination of (-) ESI and (+) APPI FT-ICR Mass Spectrometry as a Quantitative Approach of Acid Species in Crude Oils. Jorge A. Orrego-Ruiz,† Andrea Gomez-Escudero † and Fernando A. Rojas-Ruiz †††* † ECOPETROL, Instituto Colombiano del Petróleo, Piedecuesta, Santander 681018, Colombia. ††

Universidad Industrial de Santander, Grupo de Investigación Recobro Mejorado, Bucaramanga, Santander, Colombia

ABSTRACT: Crude oils differ from one another in numerous chemical and physical properties, many of which play an important role in defining their quality and price. Generally statistical analysis of price differentials has focused on two main properties, density and sulfur content. However, the growing significance of high-TAN crudes, especially from developing countries, aroused the necessity for extending these models. Consequently, refineries must obtain real and exact information regarding crude oil quality in order to achieve optimal crude selection and processing decisions. This could be attained when a detailed molecular-level characterization is performed. The present work presents the combination of (-) ESI and (+) APPI FT-ICR mass spectrometry, as a prominent approach to semi-quantify the acid species comprised in crude oils. It is proposed a novel polarity index that corrects the relative abundances of (-) ESI classes, where mainly acid species are detected. By considering different indexes, it was possible to enhance the correlation coefficients (R2) from 0.579 to 0.986 between the percentage of acid compounds and total acid number (TAN) of crudes, where most of the samples stand close to a linear tendency. These results avoid the deviations observed in previous works on the correlations between relative abundances of O2 class through (-) ESI and TAN, and could support achieving optimal crude selection and defining their quality and price.

Crude oil price is considered the most important factor for oil companies’ profitability. With petroleum cost accounting for about 80 % of refinery expenditures, processing cheaper crudes might have a positive effect on processing limitations.1 Crude oils differ from one another in a large number of chemical and physical properties, which play an essential role in their initial value, as well as in their refining and sale as petroleum products. In this sense, to afford rational selection of crude oils and processing decisions, petroleum companies must have exact information referred to crude oil quality. Conventionally, statistical analysis of price differentials has focused on two properties as main quality parameters: the lightness measured as density and the sweetness measured as sulfur content.2 However, the impact of acidity (measured as the Total Acid Number-TAN) on the price differentials must be considered since the volume of high acid crude oils has steadily increased in recent years.3 Thus crudes with a high total acid number (TAN> 0.5 mg KOH/g), are expected to have a price reduction because they limit the refining options. TAN is an indicator of the organic acids content in crude oils. Determining the crude oil organic acids content is important since their presence at certain concentrations may generate problems, especially, at crude oil atmospheric-vacuum distillation plants as a consequence of their high acid and corrosion activity.4 Among the acid oxygen-containing components, naphthenic acids (NAPS) are the most common. There are other acidic compounds such as aromatic, olefinic and hydroxilic acids but in a minor proportion.5 NAPS denote all cyclic, acyclic, and aromatic acids in crude oil. Therefore, these compounds exist as a complex mixture with broad polydispersity with respect to both molecular weight and structure (Figure 1). They are considered the most relevant acidic compounds since are known to be an important source of corrosion in refinery facilities, transportation pipelines, and a source of organic scale.6 - 8

Figure 1. Representative structures for naphthenic acids present in crude oil

Based on the facts mentioned above, molecular level characterization of NAPS and other acidic species in crude oil is crucial to identify the main features related to corrosion and flow assurance in order to afford better selection criteria of initial crude oils. As an example, it is desirable to determine the ring type and carbon number distributions because corrosivity of naphthenic acids is dependent on the sizes and structures of their molecules.9 Moreover, characterization of NAPS is of interest for geochemical studies where migration and/or biodegradation are taking place, as well as for environmental concerns especially in wastewater treatment.10 Several studies have reported the characterization of acidic species in crude oils with different separation methodologies. Among others, coordination and adsorption chromatography,11 liquid-liquid extraction,12,13 ionic exchange chromatography,14 nonaqueous ion exchange solid-phase extraction,15 and novel magnetic composite particles have been reported.16 These studies allowed showing that both molecular weight and structure depend considerably on the technique employed for the separation of polar compounds from their matrix.17

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The high resolution power and mass accuracy offered by the Ion Cyclotron Resonance (ICR) analyzer have been used to study the acid compounds from crude oils 18-20. FT-ICR-MS allows assigning unambiguously the elemental composition of almost the totality of molecules comprising a crude oil6,21. An adequate analysis of the FT-ICR MS data allows identifying classes of compounds (functional groups), as well as their molecular weight distributions, aromaticity and carbon number of each detected compound. The availability of soft ionization modes such as electrospray ionization (ESI) makes possible to have a deeper molecular characterization of the polar compounds present in petroleum, including the heaviest molecules.22-28 ESI selectively generates molecular ions without fragmentation creating charged ions by means of both protonation (positive (+) ESI) or deprotonation of species (negative (-) ESI). In particular, (-) ESI can ionize selectively neutral nitrogen containing species, phenols and carboxylic acids such as NAPS 24 while (+) ESI basic nitrogen compounds mainly.27 From elsewhere, Positive Atmospheric Pressure Photo-Ionization ((+) APPI) can form positive ions through different mechanisms including proton transfer reactions.26 In this sense, APPI has extended the variety of compounds that can be analyzed by FT-ICR MS, as it allows identifying and characterizing polar and non-polar complex hydrocarbons,28 as well as porphyrins and asphaltenes,29 demonstrating high sensibility performance when compared to other ionization techniques. NAPS have been widely studied using (-) ESI FT-ICR-MS. Qian et. al. reported the identification of almost the total acid species (oxygenated and sulfonated aromatic compounds) in a South American crude oil using (-) ESI.24,30 They developed a method that offers an acceptable measurement of the TAN of crudes, based on the selective ionization, the identification of the acid structures and the molecular weight distributions.30 Vaz et. al. reported a direct correlation between TAN and acidic composition of 27 Brazilian crude oils using (-) ESI FT-ICR MS.31 However, the best correlation was obtained for samples with TAN > 0.9. In these approaches the O2 species were considered as the only species directly related with the TAN of a crude oil, so as to carboxylic acids described by the general formula CnH2n+zO2 as the main compounds related with. In the present work, we show a methodology for the characterization of the acid components from 18 petroleum samples based on the combination of (+) APPI and (-) ESI FT-ICR MS. This approach allows extending a linear correlation for TAN measurements by (-) ESI, when the acquired data are corrected by a polarity index determined by (+) APPI. Such polarity index is related with the relative concentration of polar and non-polar compounds. Regarding TAN, it was confirmed that even though there is a predominant contribution of naphthenic acids (CcH2n-zO2) to the total acidity of a crude oil, it was noticed that different species (CcHhNO2, CcHhSO2 CcHhO3 CcHhO4) can noticeably affect this property. EXPERIMENTAL SECTION Eighteen South American crudes characterized through density and TAN (mg KOH/g) according to ASTM D287 and ASTM D664 methods, respectively, were used in this work (table 1). Samples were chosen to get a significant

variability in TAN values. Analytical grade toluene and methanol (Merck) were used as solvents for sample preparation. FT-ICR MS analyses were performed using a 15 T SolariX FT-ICR mass spectrometer from Bruker Daltonics (Billerica, MA). Nitrogen was used as drying and nebulizing gas. Argon was used in the collision cell and the prepared samples were directly injected with a syringe pump (Harvard, Holliston, MA). Stock sample solutions were prepared by diluting the samples to a concentration of 10 mg/mL in toluene. For ESI analysis, 0.1-0.2 mg/mL solutions in methanol: toluene (50:50) were used and spiked with 1 % (V/V) ammonium hydroxide before sample loading, to improve ion detection in negative ion ESI. The operating parameters for ESI analyses were: sample flow rate of 450 µL/h, drying gas temperature of 200 °C and spray voltage of 4500 V; -60 V for skimmer voltage and a time of flight window of 0.7 ms. In the case of positive mode APPI, stock solutions were diluted with toluene to a final concentration of 0.2 mg/mL. Samples were injected at a flow rate of 200 µL/h, drying gas temperature of 200°C and spray voltage of -2500 V, skimmer voltage was set to 25 V and 0.8 ms was used as the time of flight window. Data were acquired in broadband mode using 4 megaword data sets, by the accumulation of 100 scans. Internal spectral calibration was performed using a homologous series CcHhO2 DBE 3 for (-) ESI and CcHhS DBE 8 for (+) APPI using Data Analysis version 4.0 (Bruker Daltonics). Peaks with relative abundance higher than 10 times the S/N were exported to excel in .acs format. Compositional assignment was done using Composer software version 1.0.6 (Sierra Analytics, Modesto, CA, USA) with 0.5 ppm tolerance. Supporting information shows the distribution of error graphs for the classes detected (Figures S1 and S2). RESULTS AND DISCUSSION Samples. Table 1 lists the samples and shows their physicochemical properties. Both acidity (TAN) and density (°API) show a good variability of the data. Table 1. List of crudes and their TAN and density (°API) values. Sample

TAN (mg KOH/g)

°API

SF

0.15

8.2

CH TK TK-62

0.37 the O2 compounds are confined to DBE < 10. It can be noticed that while TAN is lower (less than 0.27), it becomes more complicated to find a trend between the molecular features for O2 compounds and acidity by considering that crudes such as CU, AN, and C80, whose TAN is reported as < 0.1 mg KOH/g (see Table 1), present a significant contribution of DBE > 10 compounds. It demonstrates the limited sensibility of the measurement of TAN for very low acid crudes (< 0.1 mg KOH/g). Considering AC, CU and CH plots, AC probably is near to TAN zero, while CH might be near 0.1 mg KOH/g. Compounds with DBE > 10 can be highly aromatic bi-phenols and/or ketones that most likely do not contribute to acidity. Thus, in samples with TAN < 0.5 all O2 compounds might be erroneously assumed acidity contributors. For that reason, some works have claimed that their correlations do not go well for samples with low acidity.30,31 Further studies ought to be focused in figuring out what the real contribution of O2 compounds on the acidity of low TAN samples is. Figure 5b lumps together acid crudes (TAN ≥ 0.5) and shows distributions restricted to DBE from 1 to 10 but varying significantly in carbon number. While in average NU had higher carbon number than SN, this last crude had higher acidity. So, no correlation can be established on carbon number and TAN. According to this, the contribution of O2 compounds in high TAN samples is mainly due to compounds with DBE 1 to 10.

Finally, an important aspect not discussed so far is the assumption that all O2 compounds correspond to naphthenic acids. Taking into account that O2 class contributes the most with the acidity, its contribution to TAN is analyzed. Figure 5 shows the O2 class contour plots of Double Bound Equivalent

Figure 5. Contour plots DBE vs. CN for O2 class detected for all samples. (a) Samples with TAN < 0.5; (b) samples with TAN ≥ 0.5

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ACKNOWLEDGMENTS

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CONCLUSIONS Although the existing quality parameters and price differentials for crude oil are based on robust physico-chemical measurements such as density, sulfur content and TAN, the mass resolution and accuracy offered by modern analytical methodologies such as (-) ESI and (+) APPI FT-ICR MS, compel developing a molecular level understanding of these parameters. Regarding TAN, in the present work it was confirmed that there is a predominant contribution of naphthenic acids (CcHhO2) to the total acidity of a crude oil. However, it was noticed that different species (CcHhNO2, CcHhSO2 CcHhO3 CcHhO4) detected through (-) ESI can noticeably affect this property, based on the fact that the correlation between TAN and %RA of O2 class increased from 0.260 to 0.579, when other classes such as NO2, SO2, O3 and O4 were considered. However, since this correlation was too low, it was proposed a correction of (-) ESI using (+) APPI. It was demonstrated that this correction can reach an advantageous approach for semi-quantify the acid species comprised in crude oils. Based on RA % of the classes detected by (+) APPI, a polarity index #   was identified allowing optimizing the ##$  !!"

correlations between the acid classes detected by (-) ESI and TAN, affording a correlation coefficient of 0.986. From elsewhere, the analysis of contour plots DBE vs CN for O2 compounds points out that possibly not all the compounds comprising the O2 class necessarily correspond to acidic species. In the crude oils with TAN ≥ 0.5 mg KOH/g, the acidic species were restricted to DBE < 10, while in low acidity samples (TAN < 0.5 mg KOH/g) O2 compounds were detected up to DBE 25. These highly aromatic molecules may correspond to phenols o ketones, but the substantiation of this assertion is out of the scope of the present work. Finally, all these results offer detailed information regarding crude oils and could support an optimal crude selection, defining their quality and price.

ASSOCIATED CONTENT

The authors would like to thank Ecopetrol S.A - Instituto Colombiano del Petróleo (ICP) for their technical and economic support along the course of this investigation, and to Convenio Marco de Cooperación Tecnológica & Científica UIS–Ecopetrol S.A No. 5222395. REFERENCES 1.

2.

3.

4.

5. 6. 7.

8. 9. 10.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Figure S1 and S2 show the error distributions for the main classes detected by (-) ESI and (+) APPI for all crude oil samples, respectively. Figure S3 displays the different correlations obtained before and after correcting data for all samples by multiplying the relative abundances of each acid species with the proposed polarity index, as well as the corresponding correlation coefficients (R2) with TAN.

11.

12. 13. 14.

AUTHOR INFORMATION Corresponding Author *

[email protected]

15.

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

16.

17.

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