Speaker
Description
The activity and selectivity of catalysts is strongly dependent on operating conditions like feed composition, pressure or temperature. Understanding and controlling the influence of these reaction parameters is crucial in various fields of research ranging from organic synthesis to large scale reactor engineering. Detailed kinetic models are generally capable of capturing the complex dependencies on the operating conditions. However, to set up these models a detailed mechanistic understanding of the underlying elementary processes is required. Further, these models often rely on a large number of parameters that are experimentally hardly accessible. Here, making uninformed assumptions can introduce systematic deficiencies into the resulting model.
To overcome these limitations, we present a design-of-experiment inspired data-driven approach, which analyzes the influence of process parameters on the reaction rate to identify effective rate laws without prior knowledge and assumptions [1]. The proposed algorithm determines relevant model terms from a polynomial ansatz employing well established statistical methods. For the optimization of the model parameters special emphasis 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. The flexibility of this approach is demonstrated based on synthetic kinetic data sets from microkinetic models. It can be shown that the kinetics of both the classical HBr reaction and a prototypical catalytic cycle are automatically reproduced.
Building on this algorithm, we explore how approximate rate laws can be utilized to create a kinetic fingerprint in order to map out regions of distinct kinetic behavior based on empirically observed data. Combining this local information with concepts from unsupervised learning and support vector classification models, multi-regime kinetic phase diagrams can be recovered without any prior knowledge about the reaction mechanism. Importantly, this is achieved without making any assumptions about the chemical nature of the catalyst surface nor its active sites. In this respect, this type of top-down approach is highly complementary to bottom-up first-principles microkinetic modeling. The latter can equally establish multi-regime kinetic phase diagrams, but relies for this necessarily on detailed insight into the active sites, which may not be available for operando evolving catalysts.
[1] F. Felsen, K. Reuter, and C. Scheurer, Chem. Eng. J. 433, 134121 (2022).
| Abstract Number (department-wise) | TH 19 |
|---|---|
| Department | TH (Reuter) |
