A model-free sparse approximation approach to robust formal reaction kinetics

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Accurate and transferable models of reaction kinetics are of key importance for chemical reactors on both laboratory and industrial scale. Usually, setting up such models requires a detailed mechanistic understanding of the reaction process and its interplay with the reactor setup. We present a data driven approach which analyzes the influence of process parameters on the reaction rate to identify locally approximated effective rate laws without prior knowledge and assumptions. The algorithm we propose determines relevant model terms from a polynomial ansatz employing well established statistical methods. For the optimization of the model parameters special emphasize is put on the robustness of the results by taking not only the quality of the fit but also the distribution of errors into account in a multi-objective optimization. We demonstrate the flexibility of this approach based on artificial kinetic data sets from microkinetic models. This way, we show that the kinetics of both the classical HBr reaction and a prototypical catalytic cycle are automatically reproduced. Further, combining our approach with experimental screening designs we illustrate how to efficiently explore kinetic regimes by using the example of the catalytic oxidation of CO. • Data driven approach for robust kinetic modeling. • Algorithm automatically determines functional form of model to avoid bias. • Effective kinetic models do not require mechanistic understanding. • Automatic discovery of kinetic regimes.