Machine Learning Surrogate Models for Mechanistic Kinetics: Embedding Atom Balance and Positivity.

An ultrafast multi-zone HCCI model with Autoignition, Global reaction and Interpolation (AGI) for achieving comparable accuracy to detailed chemical kinetics models

Stress testing of active pharmaceutical ingredients (API) is an important tool used to gauge chemical stability and identify potential degradation products. While different flavors of API stress testing systems have been used in experimental investigations for decades, the detailed kinetics of such systems as well as the chemical composition of prominent reactive species, specifically reactive oxygen species, are unknown. As a first step toward understanding and modeling API oxidation in stress testing, we investigated a typical radical "soup" solution an API is subject to during stress testing. Here we applied ab initio electronic structure calculations to automatically generate and refine a detailed chemical kinetics model, taking a fresh look at API oxidation. We generated a detailed kinetic model for a representative azobis(isobutyronitrile) (AIBN)/H2O/CH3OH stress-testing system with a varied cosolvent ratio (50%/50%-99.5%/0.5% vol water/methanol) for 5.0 mM AIBN and representative pH values of 4-10 at 40 °C that was stirred and open to the atmosphere. At acidic conditions, hydroxymethyl alkoxyl is the dominant alkoxyl radical, and at basic conditions, for most studied initial methanol concentrations, cyanoisopropyl alkoxyl becomes the dominant alkoxyl radical, albeit at an overall lower concentration. At acidic conditions, the levels of cyanoisopropyl peroxyl, hydroxymethyl peroxyl, and hydroperoxyl radicals are relatively high and comparable, while, at both neutral and basic pH conditions, superoxide becomes the prominent radical in the system. The present work reveals the prominent species in a common model API stress testing system at various cosolvent and pH conditions, sets the stage for an in-depth quantitative API kinetic study, and demonstrates the usage of novel software tools for automated chemical kinetic model generation and ab initio refinement.

The chapter is devoted to the numerical modeling of detonation initiation in the plane channel with the profiled end wall of the elliptic shape. Mathematical model is based on two-dimensional Euler equations supplemented by the chemical kinetics model to describe the combustion of the hydrogen–oxygen mixture under the low pressure. Both the global kinetic model and the detailed Petersen-Hanson model are applied. The computations are performed on unstructured triangular grids using the numerical method of the second approximation order. The mechanism of detonation initiation by the incident shock wave with the Mach number 2.7 is described. The results of initiation of the detonation wave using two models of chemical kinetics are compared with each other with the identification of similarities and differences in the process.KeywordsComplex kineticsDetonation waveNumerical modelingUnstructured grid

Machine learned compact kinetic models for methane combustion

Development of detailed chemical kinetic models for advanced petroleum-based and nonpetroleum based fuels is a difficult challenge because of the hundreds to thousands of different components in these fuels and because some of these fuels contain components that have not been considered in the past. It is important to develop detailed chemical kinetic models for these fuels since the models can be put into engine simulation codes used for optimizing engine design for maximum efficiency and minimal pollutant emissions. For example, these chemistry-enabled engine codes can be used to optimize combustion chamber shape and fuel injection timing. They also allow insight into how the composition of advanced petroleum-based and non-petroleum based fuels affect engine performance characteristics. Additionally, chemical kinetic models can be used separately to interpret important in-cylinder experimental data and gain insight into advanced engine combustion processes such as HCCI and lean burn engines. The objectives are: (1) Develop detailed chemical kinetic reaction models for components of advanced petroleum-based and non-petroleum based fuels. These fuels models include components from vegetable-oil-derived biodiesel, oil-sand derived fuel, alcohol fuels and other advanced bio-based and alternative fuels. (2) Develop detailed chemical kinetic reaction models for mixtures of non-petroleum and petroleum-based components to represent real fuels and lead to efficient reduced combustion models needed for engine modeling codes. (3) Characterize the role of fuel composition on efficiency and pollutant emissions from practical automotive engines.

Thermal explosions result when local temperature-dependent heat generation exceeds heat loss via conduction. The temperature dependence of the heat source term is directly related to the material’s chemical kinetics, and hence the chemical kinetics has a direct impact on the thermal explosion times of a material. Much success has been gained in past work to accurately model thermal explosions in various explosives using multi-step Arrhenius chemical kinetics models. However, the generation of these kinetics models is time consuming and complex. Therefore, a methodology has been developed that allows for calibration of a single-reaction global kinetics model using One Dimensional Time to Explosion (ODTX) experimental data, which combines an iterative approach with a steepest descents optimization. This methodology has been applied to calibrate kinetic parameters for the widely-used explosives RDX (1, 3, 5-trinitrohexahydro-striazine), HMX (octahydro-1, 3, 5, 7-tetranitro-1, 3, 5, 7-tetrazocine), LX-10 (95% HMX, 5% Viton binder), and PBXN-109 (64% RDX, 20% Al, 16% binders). The average error between experimental and simulated ODTX and STEX data using this technique is approximately equivalent to that using the traditional multi-step models, and the time required for calibration of the global kinetics model has been reduced from months to hours.

This study examines the combustion characteristics of a binary mixture surrogate for possible future diesel fuels using both a single-cylinder research engine and a homogeneous reactor model using detailed chemical reaction kinetics. Binary mixtures of a normal straight-chain alkane (pure n-hexadecane, also known as n-cetane, C16H34) and an alkyl aromatic (toluene, C7H8) were tested in a single-cylinder research engine. Pure n-hexadecane was tested as a baseline reference, followed by 50%, 70%, and 80% toluene in hexadecane blends. Testing was conducted at fixed engine speed and constant indicated load. As references, two conventional petroleum-based fuels (commercial diesel and US Navy JP-5 jet fuel) and five synthetic Fischer-Tropsch-based fuels were also tested. The ignition delay of the binary mixture surrogate increased with increasing toluene fraction and ranged from approximately 1.3 ms (pure hexadecane) to 3.0 ms (80% toluene in hexadecane). While ignition delay changed substantially, the location of 50% mass fraction burned did not change as significantly due to a simultaneous change in the premixed combustion fraction. Detailed chemical reaction rate modeling using a constant pressure, adiabatic, homogeneous reactor model predicts a chemical ignition delay with a similar trend to the experimental results, but shorter overall magnitude. The difference between this predicted homogeneous chemical ignition delay and the experimentally observed ignition delay is defined as the physical ignition delay due to processes such as spray formation, entrainment, mixing, and vaporization. On a relative basis, the addition of 70% toluene to hexadecane causes a nearly identical relative increase in both physical and chemical ignition delay of approximately 50%. The chemical kinetic model predicts that, even though the addition of toluene delays the global onset of ignition, the initial production of reactive precursors such as HO2 and H2O2 may be faster with toluene due to the weakly bound methyl group. However, this initial production is insufficient to lead to wide-scale chain branching and ignition. The model predicts that the straight-chain alkane component (hexadecane) ignites first, causing the aromatic component to be consumed shortly thereafter. Greater ignition delay observed with the high toluene fraction blends is due to consumption of OH radicals by toluene. Overall, the detailed kinetic model captures the experimentally observed trends well and may be able to provide insight as to the relationship between bulk properties and physical ignition delay.

Learning What Makes Catalysts Good

An advanced model of fluid dynamics and nonequilibrium vibrational-chemical kinetics in high-temperature viscous flows along the stagnation line is proposed. The present model takes into account detailed state-to-state kinetics and state-dependent transport properties. Fluid dynamics equations are self-consistently coupled to vibrational kinetics, and state-dependent transport terms are properly incorporated in the governing equations. As an example, vibrational kinetics, macroscopic flow parameters, and heat transfer in a N 2 /N mixture are calculated for different flow conditions. A comparison with thermal equilibrium and vibrational frozen flows is presented, showing the important role of detailed kinetics coupled to fluid dynamics. Several models of chemical and vibrational kinetics are assessed and a strong dependence of the flow parameters and surface heat flux on the chemical model is demonstrated.

The role and importance of chemical kinetics in heterogeneous catalysis at the present stage of development are examined andthe concept of kinetic models, in the exact, reliable, complete, and well-founded form, as well as the usefulness of combinedkinetic investigations for the determination of these models are discussed. The effectiveness of combining different methodsfor devising informative models of this kind, examples of which are quoted, is demonstrated. The problems involved in settingup non-steady-state kinetic models, which are some of the most important in chemical kinetics, are briefly described. Theoptimum characteristics of kinetic models and the relation between the properties of the latter and the characteristics ofreaction selectivities are analysed. The kinetic aspects of tests of the activities and selectivities of catalysts of complex reactionsare mentioned in conclusion. The bibliography includes 202 references.

Kinetic model for modern double-layered Pd/Rh TWC as a function of metal loadings and mileage

Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: A review

Reduction and applicability of chemical kinetic model for ethylene detonation simulation

Nonthermal plasma (NTP) presents a promising pathwayfor sustainableammonia synthesis under mild conditions, enabling activation of nitrogenwithout the need for high thermal input. While most studies to datehave focused on plasma ammonia synthesis under ambient pressure, thepotential benefits of elevated pressure, such as improved thermodynamicfavorability and enhanced compatibility with downstream ammonia separationtechnologies, remain underexplored. In this work, we investigate plasma-drivenammonia synthesis under elevated pressure by combining experimentalmeasurements with detailed plasma chemical kinetics modeling. A dielectricbarrier discharge plasma reactor was employed, with the system pressurecontrolled up to 3 bar using a high-pressure regulator. Contrary tothermodynamic expectations, the experimental results reveal that increasingpressure suppresses ammonia yield in the plasma environment, primarilydue to a reduction in the reduced electric field (E/N), which diminishesthe energy of electrons available for molecule activation. The underlyingreaction mechanism was elucidated using in situ optical diagnosticsand chemical kinetics simulations. Path flux analysis confirms thatN2 is dissociated by energetic electrons into N and excitedN­(2D) species, which are subsequently hydrogenated to formNH and NH2 radicals. These intermediates recombine viaNH2 + H­(+M) → NH3(+M) and NH + H2 + M → NH3 + M to form ammonia. Notably,elevated pressure does not alter the dominant reaction pathways butsignificantly influences the reaction rates and plasma characteristics.Sensitivity analysis highlights that the electron-impact dissociationof N2 [e + N2 → e + N + N­(2D)] is the rate-limiting step and has the greatest promoting effecton ammonia formation. These insights offer guidance for optimizingplasma operating conditions and advancing the practical applicationof plasma-assisted ammonia synthesis under pressurized conditions.

Methane Dry Reforming over Ni/Al2O3 Catalyst in Spark Plasma Reactor: Linking Computational Fluid Dynamics (CFD) with Reaction Kinetic Modelling