Comprehensive two-dimensional gas chromatography in combination with pixel-based analysis for fouling tendency prediction

Abrahamsson, Victor; Ristic, Nenad; Franz, Kristina

doi:10.1016/j.chroma.2017.04.021

Cited by 10 publications

(14 citation statements)

References 62 publications

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“…10 However, understanding of the different fouling mechanisms and associated rates is limited, and very few attempts to relate the composition of wide boiling point range fractions with coke formation are available in the open literature. 11 Steam cracking fouling, that is, coke formation, is a complex phenomenon studied both theoretically 12,13 and experimentally. 14−17 Three mechanisms contribute to the deposition of a coke layer: a heterogeneous catalytic, a heterogeneous noncatalytic, and a homogeneous noncatalytic mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the major challenges are lower light olefin yields and higher pyrolysis fuel oil (PFO) yields. , In addition to this, higher fouling rates in the convection, radiant, and especially transfer line heat exchanger (TLE) sections of a steam cracker can also negatively affect the process economics . However, understanding of the different fouling mechanisms and associated rates is limited, and very few attempts to relate the composition of wide boiling point range fractions with coke formation are available in the open literature …”

Section: Introductionmentioning

confidence: 99%

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Crude to Olefins: Effect of Feedstock Composition on Coke Formation in a Bench-Scale Steam Cracking Furnace

Geerts

Ristic

Djokic

et al. 2020

Ind. Eng. Chem. Res.

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A novel experimental unit has been designed, allowing the examination of the fouling tendency in all relevant sections of a steam cracking furnace, that is, dry feed preheater (DFP), dilute feed preheater I & II (DFPH I & II), radiant section, and transfer line exchanger in a single experiment, using among others an electrobalance. Large differences in coke deposition have been observed in each of these sections when cracking a wide range gas oil (WRGO) and a naphtha fraction (NF). WRGO results in fouling rates which are 20, 86, 253, and 10 times higher in the DFP, DFPH I, DFPH II, and transfer line heat exchanger sections compared to the NF, whereas the fouling rate in the radiant section is 56% lower. The standard deviations are 13, 18, and 7% for DFP, DFPH I, and DFPH II, respectively. Online effluent analysis reveals that significantly less valuable olefins and more Pygas and pyrolysis fuel oil (PFO) are formed during WRGO cracking compared to NF cracking, that is, 16.2 wt % versus 21.9 wt % ethylene, 12.1 wt % versus 14.1 wt % propylene, 24.8 wt % versus 19.6 wt % Pygas, and 14.1 wt % versus 11.3 wt % PFO. This unit can thus provide vital information to develop single step crude to olefin process by examining possible heavy feedstocks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crude to Olefins: Effect of Feedstock Composition on Coke Formation in a Bench-Scale Steam Cracking Furnace

Geerts

Ristic

Djokic

et al. 2020

Ind. Eng. Chem. Res.

Self Cite

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“…In these cases, internal validation (e.g., cross-validation) is well suited for the approximation of error for similar samples obtained using the same instrumentation that are not included in the sample set . Thus, leave-one-out cross-validation (LOOCV) is often implemented with PLS. ,,, However, with a large data set, LOOCV would result in excessively long computation time and potential overfitting of the data. Therefore, the PLS model construction implemented an alternative cross-validation method known as Venetian blinds cross-validation to test the overall predictive ability of the models and to determine the correct number of latent variables. , For Venetian blinds cross-validation, the data for N total samples are split s times into groups of m samples, if N is evenly divisible by s .…”

Section: Methodsmentioning

confidence: 99%

“…49 Thus, leave-one-out cross-validation (LOOCV) is often implemented with PLS. 19,20,30,37 However, with a large data set, LOOCV would result in excessively long computation time and potential overfitting of the data. Therefore, the PLS model construction implemented an alternative cross-validation method known as Venetian blinds crossvalidation to test the overall predictive ability of the models and to determine the correct number of latent variables.…”

Section: Introductionmentioning

confidence: 99%

“…Another important output obtained from PLS modeling is the linear regression vectors (LRVs), which indicate how the variables of the X-block (chromatographic and mass spectral information) relate to the Y-block (physical properties). PLS has been used in many applications with GC × GC instrumental systems ,,− to relate the chemical information to physical properties of interest. Additional details on PLS methods are available in the literature. − …”

Section: Introductionmentioning

confidence: 99%

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Predictive Modeling of Aerospace Fuel Properties Using Comprehensive Two-Dimensional Gas Chromatography with Time-Of-Flight Mass Spectrometry and Partial Least Squares Analysis

et al. 2020

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Increasingly stringent requirements for aerospace propulsion system performance, reliability, and operability motivate quantitative connections between fuel composition, physical characteristics, and system performance. Chemically accurate assessment of aviation turbine fuels (Jet-A, JP-8, etc.) and kerosene-based rocket propellants (RP-1 and RP-2) is requisite to mature these models. Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC–TOFMS) is an excellent analytical tool for measuring detailed chemical information contained in complex fuels. Additionally, multivariate data analysis methods, referred to as chemometrics, are ideally suited to relate detailed chemical information contained within the GC × GC–TOFMS data to fuel properties and performance in a predictive manner. Herein, we apply these techniques to a chemically diverse set of 74 distillate and multicomponent aerospace fuels, resulting in an improved understanding of the chemical compositional basis for physical and thermochemical behavior. Informed by GC × GC–TOFMS data, highly reliable partial least squares (PLS) models are developed and employed in the prediction of physical properties (measured separately using conventional test methods). Root-mean-square errors of cross-validation (RMSECV) were relatively low: values of 0.0450 cSt, 41.3 Btu/lbm, 0.130 mass %, and 0.0064 g/mL were obtained for viscosity, heat of combustion, hydrogen content, and density, respectively. The corresponding normalized root-mean-square errors of cross-validation (NRMSECV) were 6.01, 10.3, 8.71, and 7.12%, respectively. Investigation of the linear regression vectors (LRVs) provides valuable insight into the relationship between the chemical composition and physical properties, enabling, in principle, the model-informed selection of fuel chemical composition to achieve desired performance criteria.

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Advanced data handling in comprehensive two-dimensional gas chromatography

Berrier

Prebihalo

Synovec

2020

Separation Science and Technology

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Comprehensive two-dimensional gas chromatography in combination with pixel-based analysis for fouling tendency prediction

Cited by 10 publications

References 62 publications

Crude to Olefins: Effect of Feedstock Composition on Coke Formation in a Bench-Scale Steam Cracking Furnace

Crude to Olefins: Effect of Feedstock Composition on Coke Formation in a Bench-Scale Steam Cracking Furnace

Predictive Modeling of Aerospace Fuel Properties Using Comprehensive Two-Dimensional Gas Chromatography with Time-Of-Flight Mass Spectrometry and Partial Least Squares Analysis

Advanced data handling in comprehensive two-dimensional gas chromatography

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