Project description

Catalytic gas-solid reactors play an important role in the chemical industry, making the development of novel catalysts and reactor designs as well as the optimization of existing units indispensable. Gas-solid reactors typically consist of one or more tubes filled with catalytic active particles, open foams, or structured inserts. Sophisticated reactor models can enhance the understanding of the complex processes within these reactors and enable computer-aided optimization of operating parameters, catalysts, and reactor design. Flow, heat, and mass transfer, as well as chemical reactions in catalytic gas-solid reactors, are strongly coupled and multiscale in both time and space. An ideal multiscale model includes a detailed description and coupling of all scales ranging from the reaction at the catalytic active surface to the industrial used reactor. However, such a model would require significantly more computing resources than are currently available.

Therefore, the long-term objective of the project is to develop a multiscale modeling framework that utilizes computationally efficient surrogate models, informed by high-resolution simulations of representative sections and time periods based on fundamental physics, to bridge various scales. The current work focuses on the following primary research fields: (i) spatially resolved simulations of transport and reaction within porous catalyst materials using nano-tomography scans, (ii) computational fluid dynamics (CFD) to describe macroscopic flow coupled with transport and reaction within the porous catalyst, (iii) pseudo-continuum or pore-network models to simulate industrial-scale reactors, and (iv) effective integration of microkinetic models into reactor models. In addition, multiscale modelling will be combined with advanced optimization techniques, including machine learning, to determine the ideal catalyst and reactor design.