Prediction of Total Acid Number in Distillation Cuts of Crude Oil by ESI(−) FT‑ICR MS Coupled with Chemometric Tools

Terra, Luciana A.; Filgueiras, Paulo R.; Pereira, Roberto; Gomes, Alexandre O.; Vasconcelos, Géssica A.; Tose, Lílian V.; Castro, Eustáquio Vinícius Ribeiro de; Vaz, Boniek G.; Romão, Wanderson; Poppi, Ronei J.

doi:10.21577/0103-5053.20170073

Cited by 10 publications

(12 citation statements)

References 14 publications

(25 reference statements)

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“…Fourier transform ion cyclotron resonance mass spectrometry coupled with electrospray ionization (ESI(±) FT‐ICR MS) is an important analytical technique used for the determination of crude oil composition at the molecular level 1–3 . The ultrahigh resolution can separate mass/charge signals ( m / z ) up to 3.4 milli m / z units, reaching a medium mass error of up to 200 ppb.…”

Section: Introductionmentioning

confidence: 99%

“…that can allow the recognition of isotopic profiles and the assignment of molecular formulae (C c H h N n O o S s ) from the m / z signals. Thus, it is possible to interpret petroleomics data from different types of plots such as heteroatomic compound class, double bond equivalent (DBE) distribution (DBE = C − ( H /2)+( N /2)+1, where C, H, and N correspond to the number of carbon, hydrogen, and nitrogen atoms in the chemical formulae 2,12–14 ), DBEs versus carbon number (CN), and van Krevelen diagrams 10 …”

Section: Introductionmentioning

confidence: 99%

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Estimating the intermediate precision in petroleum analysis by (±)electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry

Folli

Souza

Araújo

et al. 2020

Rapid Comm Mass Spectrometry

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Rationale Electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT‐ICR MS) is an important analytical technique used for the elucidation of crude oil polar compounds at the molecular level, providing thousands of heteroatom compounds in a single analysis. Due to the high resolution, the complexity of data produced, and steps involved in spectra acquisition and processing, it is necessary to estimate its intermediate precision. Methods Intermediate precision was estimated for positive‐ and negative‐ion ionization modes (ESI(±)) using Composer® software for two Brazilian crude oil samples. The analytical parameters evaluated were the class distribution histogram, the double bond equivalent (DBE) distribution, and the DBE versus carbon number. The statistical parameters used to study the intermediate precision were calculated from the average, standard deviation, confidence interval (significance level at 5%), coefficient of variation (CV), intermediate precision limit (ISO 5725), and principal component analysis (PCA). Results Two crude oil samples (A and B) were analyzed, in triplicate, for seven consecutive days by ESI(±) FT‐ICR MS. The assigned class limit by ESI(+) for crude oil A was 0.42% (O2S[H] class) and for crude oil B was 0.04% (N2O2S[H] class). The assigned DBE intensity limits for the two crude oils were 0.04% for ESI(+) and 0.013% for ESI(−). The PCA for ESI(−) and ESI(+) modes presented better precision for crude oils B and A, respectively. Conclusions The most abundant classes and DBE of the majority class (i.e., with the highest intensity) are the parameters produced from the Composer® software that had the highest precision and can be used to estimate crude oil properties. The DBE values presented lower intermediate precision limit values (0.04%) than the assigned class values (0.4%). According to CV and PCA, ESI(+) was more precise for crude oil A (83% precision) and ESI(−) for crude oil B (84% precision).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating the intermediate precision in petroleum analysis by (±)electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry

Folli

Souza

Araújo

et al. 2020

Rapid Comm Mass Spectrometry

Self Cite

View full text Add to dashboard Cite

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“…Lastly, chemometric methods can be used to process compositional data, enabling the recognition of useful patterns and establishment of predictive models. For example, in a study conducted with partial least squares regression to estimate the TAN value of Brazilian crude oils [101,161], most modelled compounds belonged to N and O 2 classes, suggesting that O 2 compounds were the main contributors to the observed TAN values. Such approaches are particularly interesting for highly complex mixtures, providing important information that may be disregarded in univariate analysis due to the high amount of data generated by high-resolution MS.…”

Section: Direct Mass Spectrometry-based Methodmentioning

confidence: 99%

Naphthenic Acids: Formation, Role in Emulsion Stability, and Recent Advances in Mass Spectrometry-Based Analytical Methods

Facanali

Porto

Crucello

et al. 2021

Journal of Analytical Methods in Chemistry

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Naphthenic acids (NAs) are compounds naturally present in most petroleum sources comprised of complex mixtures with a highly variable composition depending on their origin. Their occurrence in crude oil can cause severe corrosion problems and catalysts deactivation, decreasing oil quality and consequently impacting its productivity and economic value. NAs structures also allow them to behave as surfactants, causing the formation and stabilization of emulsions. In face of the ongoing challenge of treatment of water-in-oil (W/O) or oil-in-water (O/W) emulsions in the oil and gas industry, it is important to understand how NAs act in emulsified systems and which acids are present in the interface. Considering that, this review describes the properties of NAs, their role in the formation and stability of oil emulsions, and the modern analytical methods used for the qualitative analysis of such acids.

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“…They concluded that UVE was more appropriate for mass spectrometry data and that this selection reduced the number of variables from 5703 to 143 (2.5% of full spectrum), obtaining a root mean square error of prediction (RMSEP) of 0.32 mg of KOH g –1 . Terra et al 2 also determined TAN in crude oil by competitive adaptive reweighted sampling with partial least square (CARS ‐ PLS) selection. CARS‐PLS selection reduced the variable set from 1610 to 4, with RMSEP of 0.01 mg of KOH g –1 .…”

Section: Introductionmentioning

confidence: 99%

Variable selection in support vector regression using angular search algorithm and variance inflation factor

Folli

Nascimento

Paulo

et al. 2020

Journal of Chemometrics

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Here, we combine angular search algorithm and variance inflation factor (ASA-VIF) with support vector regression (SVR) (ASA-VIF-SVR) to estimate total acid number (TAN), basic nitrogen content (BNC), and sulfur content (SC) in Brazilian crude oils. To prevent the interference of outliers, we further developed a strategy for outlier identification and applied it to nonlinear models based on RMSE (root mean square error). ASA-VIF-SVR was applied to near-and mid-infrared spectroscopy (NIR and MIR) and hydrogen nuclear magnetic resonance (1 H NMR) spectroscopy data available in a range of 93-194 samples. The models were evaluated for accuracy (root mean square error of calibration [RMSEC] and root mean square error of prediction [RMSEP]) and linearity (coefficient of determination, R 2). The removal of outliers increased accuracy and linearity of our models. The ASA-VIF model for TAN, BNC, and SC selected 0.37%, 0.93%, and 0.30% of variables from full NIR spectra; 0.21%, 0.27%, and 0.21% from full MIR; and 0.20%, 0.42%, and 0.15% from full 1 H NMR. In most cases, the best results were obtained with variable selection compared with the full dataset. Also, 1 H NMR generated more accurate and linear models with RMSEP and R 2 p of 0.0071 wt% and 0.86 for BNC and 0.0623 wt% and 0.79 for SC. TAN showed a better MIR result with RMSEP of 0.1426 mg KOH g-1 and R 2 p of 0.47. The most important region for 1 H NMR and MIR was the one with the largest quantity of unpaired electrons (aromatic region).

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Prediction of Total Acid Number in Distillation Cuts of Crude Oil by ESI(−) FT‑ICR MS Coupled with Chemometric Tools

Cited by 10 publications

References 14 publications

Estimating the intermediate precision in petroleum analysis by (±)electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry

Estimating the intermediate precision in petroleum analysis by (±)electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry

Naphthenic Acids: Formation, Role in Emulsion Stability, and Recent Advances in Mass Spectrometry-Based Analytical Methods

Variable selection in support vector regression using angular search algorithm and variance inflation factor

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