Data Reconciliation Among PIONA, GC-FIMS, and SimDis Measurements for Petroleum Fractions

Ha, Harry Z.; Ring, Zbigniew; Liu, S.

doi:10.1080/10916460701867655

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…Basically, the composition is reconstructed by interpolation between the samples in the training set using, for example, an artificial neural network 33,34 or other empirical correlations. 2,42 Generally, these reconstruction methods are faster than those of the first type since they are computationally less demanding. A disadvantage is that, because the size of the employed training set is evidently finite, the application range of these methods will be determined by the latter.…”

Section: Reconstruction Methodsmentioning

confidence: 99%

“…28 In an effort to eliminate the use of time-consuming analytical techniques, current research focuses on the development of numerical methods that reconstruct the molecular composition of a mixture based on a number of average properties or so-called commercial indices. [29][30][31][32][33][34][35][36][37][38][39][40][41][42] Although they are not representative of all the chemical and structural variety that such mixtures can contain, process feedstock is generally identified and distinguished by these commercial indices rather than by its molecular composition. 28 These indices, e.g., the average molecular weight of the mixture, some points of a boiling point distillation curve, the specific density, the global PIONA weight fractions, etc., are determined by means of relatively simple and standardized analytical procedures.…”

Section: Introductionmentioning

confidence: 99%

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Molecular reconstruction of complex hydrocarbon mixtures: An application of principal component analysis

Pyl¹,

Reyniers²,

Marin³

2010

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com).Three methods for reconstruction of the detailed molecular composition of complex hydrocarbon mixtures, based on their global properties, are compared: a method based on the Shannon entropy criterion, an artificial neural network and a multiple linear regression model. In spite of the broad range of naphthas included in the training set, the application range of the last two methods proved to be limited. Principal component analysis allowed to identify their three-dimensional ellipsoidal application range. In this subspace, the artificial neural network is more accurate than the multiple linear regression model and the Shannon entropy method. However, outside its application range, the performance of the neural network, as well as the regression model, decreases drastically. In contrast, the performance of the Shannon entropy method is not influenced by the characteristics of the considered naphtha, but rather depends on the number of available commercial indices. The Shannon entropy method yields comparable results to the artificial neural network, provided that a sufficient amount of distillation data is available to supply information on the carbon number distribution. Combining the reconstruction methods with a fundamental simulation model illustrates the necessity of having accurate feedstock reconstruction methods since they allow to capture the full power of fundamental simulation models for the simulation of industrial processes. V V C 2010 American Institute of Chemical Engineers AIChE J, 56: 3174-3188, 2010

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Section: Reconstruction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Molecular reconstruction of complex hydrocarbon mixtures: An application of principal component analysis

Pyl¹,

Reyniers²,

Marin³

2010

AIChE Journal

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“…HTSD is a GC-based technique that utilizes flame ionization detection (FID) to determine the weight percent of an oil as a function of boiling point . This method provides the mass fraction that is volatile within a boiling point range (cut point interval) and can be readily applied to determine the distribution of boiling point cuts in a crude oil (up to ∼C108). − …”

Section: Experimental Methodsmentioning

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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“…161 To investigate the compositions of diesel fuels, Ogawa et al have made an effort in optimum instrumental parameters for the FIMS analysis. 160 Using GC-FIMS, Ha et al 440 developed different methods for delumping detailed analytical information for the determination of the detailed molecular composition of middle-distillates. The authors have integrated PIONA and GC-FIMS results and determined an accurate and complete hydrocarbon-type distribution profile.…”

Section: Gc-field Ionization Mass Spectrometry (Gc-fims)mentioning

confidence: 99%

Analytics Driving Kinetics: Advanced Mass Spectrometric Characterization of Petroleum Products

et al. 2021

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The current state-of-the-art in analysis techniques for petroleum fractions has progressed substantially during the last decade. This has helped to further improve the lumping procedures and modeling approaches of these complex systems. Recent advances in gas chromatography (GC), GC-field ionization mass spectrometry (GC-FIMS), and comprehensive gas chromatography (GC × GC) have made it possible to determine the compositions of fractions with up to 45 carbon atoms and in some cases up to C80. The combination of MS techniques with other selective detectors and reversed-phase column combinations has made it possible to quantify even traces of heteroatomic compounds in these complex hydrocarbon matrices. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), in some cases combined with GC × GC for the lighter part, has pushed the characterization of larger macromolecules in particular asphaltenes. Matrix-assisted laser desorption/ionization (MALDI) is also used widely for this purpose but has the disadvantage that quantification is not obvious. The development of more detailed characterization techniques has not remained unnoticed in the petrochemical society, and more recently in the petrochemical kinetic modeling society. More detailed characterization of petrochemical fractions has made the implementation of detailed kinetic models for simulation and optimization possible including more and more molecular detail. Additionally, advances in photo-ionization mass spectrometry (PI-MS) have allowed the detection of reactive intermediates and direct kinetic measurements in time-resolved experiments. It can only be expected that this trend will continue and that the application field will move from now primarily petrochemistry, (from catalytic cracking, over hydrotreating and hydrocracking, to pyrolysis, combustion, and steam cracking) to larger-scale chemical recycling and biomass conversion processes.

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Data Reconciliation Among PIONA, GC-FIMS, and SimDis Measurements for Petroleum Fractions

Cited by 9 publications

References 9 publications

Molecular reconstruction of complex hydrocarbon mixtures: An application of principal component analysis

Molecular reconstruction of complex hydrocarbon mixtures: An application of principal component analysis

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

Analytics Driving Kinetics: Advanced Mass Spectrometric Characterization of Petroleum Products

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