Exploring pyrolysis of the aromatics in shale oil by experimental study and kinetic modelling

Wang, Yanwen; Han, Xiangxin; Jiang, Xiumin

doi:10.1016/j.energy.2023.127998

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…Among the C–H and C–C bond cracking of aromatic side chains, the identified easier cracking of β-C–C branch bonds (RxC 18) and α-C–H branch bonds (RxC 9, 33) over that of α -C–C branch bonds (RxC 17) are supported by the BDEs calculated using the CBS-QB3 method. 65,66…”

Section: Resultsmentioning

confidence: 99%

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Generating a skeleton reaction network for reactions of large-scale ReaxFF MD pyrolysis simulations based on a machine learning predicted reaction class

Yang,

Li,

Zheng

et al. 2024

Phys. Chem. Chem. Phys.

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Section: Resultsmentioning

confidence: 99%

“…The slow progressive aromaticity destruction may lead to the aryl ring opening gradually in RP-3 pyrolysis. 65,67…”

Section: Resultsmentioning

confidence: 99%

Generating a skeleton reaction network for reactions of large-scale ReaxFF MD pyrolysis simulations based on a machine learning predicted reaction class

Yang,

Li,

Zheng

et al. 2024

Phys. Chem. Chem. Phys.

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“…The major differences between the pyrolysis of cyclic and chain structures of fuel components are the pyrolysis reaction classes relevant to ring opening and ring formation. The reaction classes for cyclic structure pyrolysis were defined based on both pyrolysis reaction knowledge − and the reaction observations from ReaxFF MD simulations of realistic hydrocarbon fuel pyrolysis. The reaction classes relevant to ring structures are mainly defined as Group VIII–XVI.…”

Section: Results and Discussionmentioning

confidence: 99%

“…To create a set of reaction classes as universal as possible that is applicable in the automatic classification of pyrolysis reactions of various hydrocarbon fuels obtained from ReaxFF MD simulations, the reaction classification knowledge of both the cheminformatics methods for reaction description and reaction pattern extraction, ,,,,, as well as the available mechanism of hydrocarbon fuel pyrolysis and combustion, − was referenced in the reaction class definition. Particular attention was focused on the reaction class granularity and uniqueness of the reaction site structures.…”

Section: Methodsmentioning

confidence: 99%

Creating a Reaction Data Set Labeled with Reaction Class for Automated Reaction Classification for ReaxFF Molecular Dynamics Simulations of Realistic Fuel Pyrolysis

Yang,

Li,

Zheng

et al. 2024

Energy Fuels

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Pyrolysis chemistry is important in both engine combustion and industrial utilization of various fuels. Understanding pyrolysis chemistry is challenging due to the large number of reactions involved and the explosion of intermediate species structures in the radical-driven process. Since the bond changes reflect the very core information on a reaction, automatic reaction classification based on reaction centers can be useful to peak at a simplified reaction view of a complex pyrolysis process. This work proposes and implements a scheme to build a reaction data set labeled with the reaction class for reactions from reactive molecular dynamics simulations using ReaxFF (ReaxFF MD) in generating global reactions in pyrolysis of realistic fuel mixtures. The major steps include the automated conversion of reactions into elementary-like reactions with a pseudosingle reaction center, automatic extraction of extended reaction centers, reaction class defining, and manual labeling. There are 46 reaction classes defined in total based on both pyrolysis reaction knowledge and reaction observations from ReaxFF MD simulations of realistic hydrocarbon fuel pyrolysis. With the effort to have as adequate number of reactions as possible labeled for each reaction class defined, 7862 reactions were manually labeled with reaction classes for the data set of 26,881 elementary-like reactions that cover major pyrolysis reaction classes of typical hydrocarbon fuel components of nparaffins, iso-paraffins, olefins, cycloparaffins, and aromatics. The reaction data set has been used in the scheme of SRG-Reax to build a semisupervised machine learning model of tri-training to predict the reaction classes of pyrolysis reactions. Through automated reaction classification, 30 major reaction classes involved in a total of 3479 pyrolysis reactions of real RP-3 fuel containing 45 components unravel the overall pyrolysis reaction characteristics of the fuel system. With additional reaction classes defined and reaction data labeled, the approach can be used for various fuels.

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Indene as an alternative hydrogen carrier: Performance of supported noble metal catalysts for indene and indane hydrogenation

Díaz,

Rapado-Gallego,

Ilkaeva

et al. 2024

Chemical Engineering Journal

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Exploring pyrolysis of the aromatics in shale oil by experimental study and kinetic modelling

Cited by 4 publications

References 42 publications

Generating a skeleton reaction network for reactions of large-scale ReaxFF MD pyrolysis simulations based on a machine learning predicted reaction class

Generating a skeleton reaction network for reactions of large-scale ReaxFF MD pyrolysis simulations based on a machine learning predicted reaction class

Creating a Reaction Data Set Labeled with Reaction Class for Automated Reaction Classification for ReaxFF Molecular Dynamics Simulations of Realistic Fuel Pyrolysis

Indene as an alternative hydrogen carrier: Performance of supported noble metal catalysts for indene and indane hydrogenation

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