Parameter estimation is a crucial step for successful microkinetic modeling in catalysis. However, the large number of parameters to be optimized in order to match the experimental data is a bottleneck. In this regard, the global optimization algorithm Basin-Hopping is utilized to automate the typically time-extensive and error-prone task of manual fitting of kinetic parameters for a heterogeneous catalytic system. The stochastic approach of the Basin-Hopping algorithm to explore the kinetic parameter solution space coupled with local search methods makes it possible to screen the high-dimensional space for an optimal set of kinetic parameters giving the least residual between the simulated and the experimentally measured catalytic performance data. Our approach also ensures that only thermodynamically consistent solution candidates are explored at each optimization step. We utilize two example case studies in heterogeneous catalysis, namely, methane oxidation over a palladium catalyst and carbon monoxide methanation over a nickel catalyst, with corresponding detailed kinetic models to illustrate the applicability of the algorithm to efficiently fine-tune detailed kinetic models.
Traceability between samples, devices and data is of great importance to the catalysis community. Adacta is a new research data management (RDM) system designed to create a traceable digital twin of a testing environment, not only storing data, but also creating a readily retrievable time-accurate record of the critical components used to measure catalyst performance. Future developments include extending Adacta to interact with electronic laboratory notebooks and to spawn simulations directly using data and measurements stored in the database.
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