Application of ATR-FTIR spectroscopy and chemometrics for the discrimination of furnace oil, gas oil and mazut oil

Yousefinejad, Saeed; Aalizadeh, Leila; Honarasa, Fatemeh

doi:10.1039/c6ay00051g

Cited by 14 publications

(9 citation statements)

References 39 publications

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“…In order to address the performance of each spectrometer at different time scales and compare their spectral response, a series of ambient samples, polystyrene standard, field blanks, and reference samples is considered. Ambient samples are routinely collected in the IMPROVE monitoring network by pulling a nominal air volume of 32.8 m 3 (24 h sampling time) through a 25 mm polytetrafluoroethylene (PTFE) filter. Samples are collected at nearly 160 pristine sites across the U.S. every third day.…”

Section: Methodsmentioning

confidence: 99%

“…Fourier transform infrared (FT-IR) spectroscopy, which captures structural information associated with molecular composition in a short amount of time, is an attractive analytical technique for the routine monitoring of process controlled operations in food, 1,2 fuel, 3 and pharmaceutical industries. 4 Besides speed, FT-IR is nondestructive, requires little sample preparation, does not involve hazardous solvents, and is relatively robust, making it applicable to indoor and outdoor applications.…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Strategy for Assessing Carbonaceous Particulate Matter Concentrations from Multiple Fourier Transform Infrared (FT-IR) Instruments: Influence of Spectral Dissimilarities on Multivariate Calibration Performance

et al. 2018

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Matching the spectral response between multiple spectrometers is a mandatory procedure when developing robust calibrations whose prediction is independent of instrument-related signal variations. A viable alternative to complex calibration transfer methods consists of matching the instrument spectral response by controlling a set of key instrumental and environmental parameters. This paper discusses the applicability of such an approach to three Fourier transform infrared (FT-IR) spectrometers used for the routine assessment of carbonaceous particulate matter concentrations in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) speciation network. The effectiveness of the proposed matching procedure is evaluated by comparing the spectral response for each individual instrument in order to characterize the extent, and nature, of the remaining inter-instrument spectral dissimilarities. Instrument-related contributions to the signal were determined to be small compared with the spectral variability induced by the filter type used for sample collection. The impact of spectral differences on prediction was addressed through the comparison of model performance derived from multiple calibration scenarios. A hybrid model yielding accurate and homogeneous prediction regardless of the instrument was proposed for organic carbon (OC) and elemental carbon (EC), two major constituents of atmospheric particulate matter. Coefficients of determination of 0.98 (OC) and 0.90 (EC) with median biases not exceeding 0.20 µg (OC) and 0.07 µg (EC) are reported. The long-term stability, assessed from weekly measurements of reference samples, shows a deviation in predicted concentrations of less than ±5% over a 2.5-year period for most of the data collected. Extending OC and EC hybrid models to the prediction of ambient samples collected during the two subsequent years provides satisfactory performance. The proposed instrument matching procedure coupled with the relative simplicity of the hybrid model is an alternative to computationally advanced calibration transfer methodologies for the characterization of carbonaceous particulate matter using multiple FT-IR instruments.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long-Term Strategy for Assessing Carbonaceous Particulate Matter Concentrations from Multiple Fourier Transform Infrared (FT-IR) Instruments: Influence of Spectral Dissimilarities on Multivariate Calibration Performance

et al. 2018

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“…Feng et al [26] used the least square support vector machine (LS-SVM) and PLS, and NIR spectroscopy for analysis of six diesel fuel properties (i.e., boiling point, cetane number, density, freezing temperature, total aromatics, and viscosity). Yousefinejad et al [27] classified three types of oil with the use chemometric methods and attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy. Da Silva et al [28] used near infrared (NIR) and medium infared (MIR) spectra of distillation residue to classify gasoline as with or without additives using PLS, PCA, and linear discriminant analysis.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Rocha

Schantz

Sheen

et al. 2017

Fuel

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As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.

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“…The term furnace oil refers to any dark, viscous residual fuel obtained by blending heavy residues from a distillation unit with lighter residues and clarified oil from a fluid catalytic cracker unit. Therefore, furnace oils cover a wide range of crude oil products and blends, from relatively light number 2 (bunker A) fuel oils to heavy number 6 (bunker C) fuel oils. − Little is known on the molecular composition of the furnace oil spilled in the Sundarbans region in 2014 or how long the oil will persist in the region. , Marzan et al isolated and characterized hydrocarbonoclastic bacteria from the contaminated site and determined the potential for degradation, but little is known about the oil chemical composition . A multinational response team organized by the United Nations Environment Programme (UNEP) and the Government of Bangladesh recovered a sample of the cargo oil from the tanker shortly after the accident.…”

Section: Introductionmentioning

confidence: 99%

“…8−12 Little is known on the molecular composition of the furnace oil spilled in the Sundarbans region in 2014 or how long the oil will persist in the region. 13,14 Marzan et al isolated and characterized hydrocarbonoclastic bacteria from the contaminated site and determined the potential for degradation, but little is known about the oil chemical composition. 15 A multinational response team organized by the United Nations Environment Programme (UNEP) and the Government of Bangladesh recovered a sample of the cargo oil from the tanker shortly after the accident.…”

Section: ■ Introductionmentioning

confidence: 99%

Detailed Compositional Characterization of the 2014 Bangladesh Furnace Oil Released into the World’s Largest Mangrove Forest

et al. 2018

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On December 9, 2014, ∼94 000 gallons of furnace oil spilled into the Shela River in Bangladesh, a designated World Heritage Site by the United Nations Educational, Scientific and Cultural Organization. It was the largest recorded oil spill in the Sundarbans region. Visually, furnace oil appears similar to heavy fuel oil, but little is known about its composition even though it is heavily utilized worldwide. A shift in global oil production to heavier, less well-known feeds (i.e., heavy oil and bitumen) requires molecular-level knowledge for efficient response, damage assessment, and restoration in the event of any oil spill. However, little is known about the chemical composition of furnace oil in chronic and acute releases. For the first time, we catalog the molecular-level composition of a relatively unknown furnace oil collected immediately after the 2014 Bangladesh spill and compare it to a well-characterized intermediate fuel oil (IFO) spilled in Texas City, Texas (U.S.A.) in March 2014. Through a combined technique approach, we apply comprehensive two-dimensional gas chromatography (GC×GC) analysis and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to contrast the unknown furnace oil to IFO. Combined, these techniques capture the continuum of oil components and access the less volatile, highly complex non-GC amenable compounds. GC×GC analysis provides biomarker signatures that suggest the furnace oil likely originated in the Middle East and is a refined product. We further compared the furnace oil with the Arabian light crude from Middle East origin (WP681) and revealed remarkable similarities between the two oils. Simulated distillation for the furnace oil showed that 42% of the oil mass is not volatile below 478 °C (equivalent to C40; the upper limit for GC-based techniques), whereas the IFO contained 38% of the total mass >C40. Furthermore, FT-ICR MS extends the carbon number range and unlocks the molecular composition of non-GC amenable compounds. Atmospheric pressure photoionization (APPI) and electrospray ionization (ESI) FT-ICR MS resolve and identify tens of thousands of molecular formulas in each oil and report furnace oil composition similar to whole heavy crudes. To the best of our knowledge, this is the first report of the detailed compositional characterization of any furnace oil.

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Application of ATR-FTIR spectroscopy and chemometrics for the discrimination of furnace oil, gas oil and mazut oil

Abstract: In this work, ATR-FTIR spectroscopy was utilized for the classification of 264 fuel oil samples from three types of oil (furnace oil, gas oil and mazut oil) with the aid of some chemometric methods.

Cited by 14 publications

References 39 publications

Long-Term Strategy for Assessing Carbonaceous Particulate Matter Concentrations from Multiple Fourier Transform Infrared (FT-IR) Instruments: Influence of Spectral Dissimilarities on Multivariate Calibration Performance

Long-Term Strategy for Assessing Carbonaceous Particulate Matter Concentrations from Multiple Fourier Transform Infrared (FT-IR) Instruments: Influence of Spectral Dissimilarities on Multivariate Calibration Performance

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Detailed Compositional Characterization of the 2014 Bangladesh Furnace Oil Released into the World’s Largest Mangrove Forest

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