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.

This study examines the modeling of hydrocarbon pyrolysis in a Ni 0.27 Bi 0.73 molten metal alloy reactor. The model is executed in two stages. The first stage investigates the effect of the physical properties of the gas and molten liquid on the bubble-size distribution, and determines the Sauter mean bubble diameter in the Ni 0.27 Bi 0.73 column. In this stage, a population-balance-based model using the Euler−Euler approach is coupled with nonreactive computational fluid dynamics in the ANSYS Fluent V17.2 software package. After estimating the Sauter mean diameter, the next stage computes the overall decomposition kinetics of hydrocarbons (gas phase + melt interface) and couples them with an existing hydrodynamic model to determine the final H 2 output and selectivity. The Sauter mean diameter was found to increase with increasing superficial gas velocity (or flow rate), liquid density, surface tension, column diameter, and decrease with increasing liquid viscosity. The hydrogen selectivity improved when the model included the surface kinetics, and the hydrogen selectivity of higher hydrocarbons was comparable to (or even higher than) that of pure methane at 1000 °C.

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Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The nonlinearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 -C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions. The mechanism has been applied to validate the measured data across a wide range of temperature, pressure, equivalence ratio (φ), and RON ranges. In addition to Ignition delay times (IDT) and Flame speed (FS), the model is used to validate species concentration analysis in the premixed flames and flow reactor as well as on coupling with CFD. The model is also used to validate HCCI combustion of PRF and TPRF mixtures in CFR engine and the reactive spray simulations for n-heptane and PRF's in constant volume chamber Spray A simulations according to ECN recommendations.

The study of the ignition quality of alcohol blends with petroleum fuels is a subject of practical interest. It is well known that the ignition delay time (IDT), as well as the octane number (ON), increases when gasoline fuels are blended with ethanol. This study focuses on the impact on inverse ignition delay time (IDT–1) when alcohols, such as n-propanol and n-butanol, are blended with gasoline fuels. A nonlinear decrease in the IDT–1 of the blends was observed. Predicting the extent of nonlinearity in blends is complicated because it involves unknown intermolecular interactions between base fuel components and the blended components. The purpose of this study is to establish the dependence of base fuel composition (in terms of functional groups) on observed nonlinearity. Gasoline fuel contains hundreds of compounds (predominantly hydrocarbons), making it a challenge to understand observed nonlinearity when they blend with other components. In this study, the IDTs of primary reference fuels (PRFs, a binary mixture of iso-octane and n-heptane) and FACE (fuel for advanced combustion engines) gasolines blended with two alcohols (n-propanol and n-butanol) were obtained with an ignition quality tester (IQT) following ASTM D6890 standards. A mole-based Gaussian fit was used to model the blending effects of alcohol with gasoline. The synergistic effect of the different mixtures tested in this study was investigated by analyzing the Gaussian parameters. A multiple linear regression model was formulated to provide information about the impact of the structural composition (functional group) on the synergistic blending effects of gasoline–alcohol mixtures. Constant-volume homogeneous batch reactor simulations were also conducted, using Chemkin-Pro for alcohols blended with a FACE J surrogate mixture to provide kinetic information about the blending effects observed in the IQT measurements.