A functional-group-based approach to modeling real-fuel combustion chemistry – I: Prediction of stoichiometric parameters for lumped pyrolysis reactions

Zhang, Xiaoyuan; Yalamanchi, Kiran K.; Sarathy, S. Mani

doi:10.1016/j.combustflame.2020.10.038

Cited by 23 publications

(12 citation statements)

References 49 publications

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“…The present work utilizes the lumped fuel chemistry FGMech developed by Zhang et al − to determine the stoichiometric parameters of primary pyrolysis intermediates. This is considered to be the first step toward modeling real fuels in an LMBR.…”

Section: Methodsmentioning

confidence: 99%

“…The FGMech and THERM files for 76 fuels consisted of 50 neat fuels, 14 surrogates, and 12 real fuels, included in the Supporting Information. More details on the calculation of stoichiometric parameters, the MLR regression model, and lumped fuel chemistry FGMech can be found in refs − .…”

Section: Methodsmentioning

confidence: 99%

“…These stoichiometric parameters were obtained experimentally with the help of real fuel pyrolysis speciation data of CH 4 and C 2 H 4 in shock tube experiments , and C 3 H 6 , C 4 H 8 , C 6 H 6 , and C 7 H 8 (toluene) in flow reactor experiments. − Here, e d , b d , e a , and b a are dependent upon the rest of the stoichiometric parameters by carbon and hydrogen mass balance. Very recently, Zhang et al − developed a functional-group-based approach (FGMech) to determine the stoichiometric parameters for surrogate and real fuel mixtures. In this method, the relationship between the stoichiometric parameters and the functional groups present in a fuel mixture was developed by performing multiple linear regression (MLR).…”

Section: Introductionmentioning

confidence: 99%

“…The present work adopts the kinetics and hydrodynamic model of methane decomposition developed by Catalan et al and the lumped fuel chemistry, FGMech, of Zhang et al − to model pyrolysis of any hydrocarbon fuel in an LMBR. The real fuels, with unique functional group descriptors, exhibited distinctive pyrolysis chemistry.…”

Section: Introductionmentioning

confidence: 99%

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Hydrogen Evolution from Hydrocarbon Pyrolysis in a Simulated Liquid Metal Bubble Reactor

et al. 2021

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The evolution of hydrogen from methane decomposition in a liquid metal bubble reactor (LMBR) has become a recent subject of interest; this study examines a novel approach to hydrogen production from pyrolysis of complex hydrocarbon fuels. Modeling hydrocarbon fuel decomposition in an LMBR is executed in two stages of pyrolysis: First, primary pyrolysis intermediates are simulated using a functional-group-based kinetic model (FGMech). Then, a detailed high temperature mechanism (AramcoMech 1.3 + KAUST PAH + 5 solid carbon chemistry) is applied to simulate secondary pyrolysis of intermediates. The quantities of major products of the secondary pyrolysis simulation (CH4, H2, Cs, C6H6) are approximated by simplified regression equations. Further decomposition of smaller hydrocarbons (until exiting the reactor) is simulated using a coupled kinetic and hydrodynamics model that has been reported in the literature. The mixing effects of bubble coalescence and breakup are investigated in a comparative study on homogeneous and non-homogeneous reactors. Finally, a qualitative relationship between H2 yield per mass of fuel, functional group, and other factors such as temperature, pressure, and residence time is analyzed. In general, the H/C ratio and cyclic/aromatic content are the main features influencing total conversion to H2.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hydrogen Evolution from Hydrocarbon Pyrolysis in a Simulated Liquid Metal Bubble Reactor

et al. 2021

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“…In addition to the surrogate fuel strategy, a functional-group-based mechanism (FGMech) development approach was recently proposed by this group to model real fuel combustion. , This approach develops relationships between the functional groups of real fuels and the model parameters, i.e., stoichiometric parameters and kinetic rate constants of lumped reactions and thermodynamic and transport data of real-fuel molecules. This makes it possible to construct the submechanism of real fuels, based on the functional group characterization of the real fuels.…”

Section: Introductionmentioning

confidence: 99%

High-Temperature Pyrolysis and Combustion of C₅–C₁₉ Fatty Acid Methyl Esters (FAMEs): A Lumped Kinetic Modeling Study

Zhang

Sarathy

2021

Energy Fuels

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In the effort to mitigate the depletion of fossil fuels and climate change, biodiesels are considered to be one of the most suitable substitutes for petro-diesel in compression ignition engine applications. As a follow up to prior modeling studies for gasoline and jet surrogate fuel components (Zhang, X.; Mani Sarathy, S. Fuel, 2021, 286, 119361), this work proposes a lumped kinetic model for both saturated and unsaturated C5–C19 fatty acid methyl esters (FAMEs) based on the same methodology. The present lumped model includes 52 FAME fuel components, covering a wide range of biodiesel surrogate fuel components, as well as components typically found in biodiesels. This methodology decouples the combustion of FAME fuels into two stages: the pyrolysis of fuel molecules and the oxidation of pyrolysis intermediates. Lumped reaction steps are used to describe the (oxidative) pyrolysis of each fuel molecule, while a detailed model (Aramcomech 2.0) is adopted as the base mechanism to describe the subsequent conversion of these key intermediates. Rate rules adopted for all the FAME fuels are consistent. The present lumped model is validated against experimental data from 20 pure FAMEs and six diesel/biodiesel surrogates, including around 130 sets of validation data. In general, the present lumped model satisfactorily captures most of these validation targets. This lumped model performs comparably with the detailed models developed in the literature under combustion conditions. Combined with the lumped model for 50 hydrocarbon fuels developed in previous work by this group, the lumped kinetic model for FAME fuels developed here can be used to predict the pyrolysis and combustion chemistry of diesel/biodiesel surrogates in CFD simulations after necessary model reduction for the base model. Also, the stoichiometric parameters of the lumped reactions for various pure FAMEs can be used as the database for data science study in FGMech development for real biodiesels.

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Machine Learning for Combustion Chemistry

Echekki

Farooq

Ihme

et al. 2023

Lecture Notes in Energy

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Machine learning provides a set of new tools for the analysis, reduction and acceleration of combustion chemistry. The implementation of such tools is not new. However, with the emerging techniques of deep learning, renewed interest in implementing machine learning is fast growing. In this chapter, we illustrate applications of machine learning in understanding chemistry, learning reaction rates and reaction mechanisms and in accelerating chemistry integration.

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A functional-group-based approach to modeling real-fuel combustion chemistry – I: Prediction of stoichiometric parameters for lumped pyrolysis reactions

Cited by 23 publications

References 49 publications

Hydrogen Evolution from Hydrocarbon Pyrolysis in a Simulated Liquid Metal Bubble Reactor

Hydrogen Evolution from Hydrocarbon Pyrolysis in a Simulated Liquid Metal Bubble Reactor

High-Temperature Pyrolysis and Combustion of C₅–C₁₉ Fatty Acid Methyl Esters (FAMEs): A Lumped Kinetic Modeling Study

Machine Learning for Combustion Chemistry

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A functional-group-based approach to modeling real-fuel combustion chemistry – I: Prediction of stoichiometric parameters for lumped pyrolysis reactions

Cited by 23 publications

References 49 publications

Hydrogen Evolution from Hydrocarbon Pyrolysis in a Simulated Liquid Metal Bubble Reactor

Hydrogen Evolution from Hydrocarbon Pyrolysis in a Simulated Liquid Metal Bubble Reactor

High-Temperature Pyrolysis and Combustion of C5–C19 Fatty Acid Methyl Esters (FAMEs): A Lumped Kinetic Modeling Study

Machine Learning for Combustion Chemistry

Contact Info

Product

Resources

About

High-Temperature Pyrolysis and Combustion of C₅–C₁₉ Fatty Acid Methyl Esters (FAMEs): A Lumped Kinetic Modeling Study