Detailed Microkinetics for the Oxidation of Exhaust Gas Emissions through Automated Mechanism Generation

Abstract

Emissions from vehicles contain a variety of pollutants that must be either oxidized or reduced efficiently in the catalytic converter. Improvements to the catalyst require knowledge of the microkinetics, but the complexity of the exhaust gas mixture makes it challenging to identify the reaction network. This complexity was tackled by using the “Reaction Mechanism Generator” (RMG) to automatically generate microkinetic models for the oxidation of combustion byproducts from stoichiometric gasoline direct injection engines on Pt(111). The possibilities and the limitations encountered during the generation procedure are discussed in detail. A combination of first-principles-based mechanism construction and top-down parameter refinement allows a description of experimental results obtained by kinetic testing of a Pt/Al2O3 monolith under stoichiometric conditions. The study can serve as a blueprint for the usage of RMG for other challenging heterogeneously catalyzed reactions.

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties

“…After the self-consistent field calculation, it is possible to apply the Bayesian error estimate to perturb the parameters of the exchange-correlation functional and obtain an ensemble of 2,000 non-self-consistent energies based on the uncertainty in the training of the functional. An ab-initio database of adsorbates on Pt(111) [15,19] was extended within the present work to include 115 C x H y O * z adsorbates containing up to 4 heavy atoms (see SI for QM details). We used the method of Blöndal et al [15] to derive formation enthalpies from the DFT energies, which involves the usage of an adsorption reaction in combination with an isogyric reaction to determine the enthalpy of formation of the gas-phase precursor.…”

“…Software can be employed to overcome this hurdle, which automatically constructs complex microkinetic models without any bias considering all possible pathways at the operating conditions of the process. The Reaction Mechanism Generator (RMG) [10][11][12] is such an open-source tool that has been successfully employed for gas-phase chemistry [13,14] and heterogeneously catalyzed reactions [15][16][17][18][19] .…”

Ensemble Effects in Adsorbate–Adsorbate Interactions in Microkinetic Modeling

“…It is clear that the slope of the differential adsorption energy is higher than that of the average adsorption energy. It should be noted that the adsorbate–adsorbate interactions can be fitted with other functional forms, such as exponential or polynomial , functions. The differential adsorption energy can also be calculated from the internal energy ( E int ) according to E int = E 0 θ + α θ 2 = θ E avg E diff = d E int d θ = E 0 + 2 α θ This analysis shows that the differential adsorption energy should have a slope twice that of the average adsorption energy.…”

Automating the Optimization of Catalytic Reaction Mechanism Parameters Using Basin-Hopping: A Proof of Concept

“…The initial parameters were determined via RMG from precompiled databases and estimates for 132 reversible reactions. Out of these, parameters for five reactions, including the rate-determining step, were further fitted to match experimental data based on their uncertainty range . Additionally, there exist several challenges in the optimization of kinetic parameters due to the nonlinearity of the models, the necessity of gradients calculation of complex objective functions, and the possibility of getting stuck in a local minimum.…”

“…Out of these, parameters for five reactions, including the ratedetermining step, were further fitted to match experimental data based on their uncertainty range. 23 Additionally, there exist several challenges in the optimization of kinetic parameters due to the nonlinearity of the models, the necessity of gradients calculation of complex objective functions, and the possibility of getting stuck in a local minimum. In order to find the global minimum, Rangarajan et al presented a sequential optimization framework using a multistart approach to explore the solution space and showcased the framework with methanol synthesis through hydrogenation of CO and CO 2 on a Cu-based catalyst.…”