Catalytic reactor modeling.

Numerical modeling of catalytic reactors remains a challenging task due to the complex interplay of transport and reactive processes. The underlying mathematical description, typically based on finite difference, finite volume or finite element discretization, exhibits inherently stiff equations. As such, the modeling of more complex chemo-kinetic networks requires some form of coarse-graining that simplifies the equations involved by which the overall numerical routines remain tractable on modern computational infrastructures. In this project, we aim to explore the different types of coarse graining methodologies to describe reactive events in the catalytic reactor and study the effect of this choice on the overall interplay between transport and reaction.

The stable state of a chemical reactor involves a complex interplay of the convection, diffusion and reaction of the components throughout the reactor as well as the catalytic medium. The conversion and selectivity of the chemical process depends on many influencing factors. It can for example be limited by the rate of which a reactant can access the active site, the ratio of the reactants in the vicinity of the active site, or the rate of heat dissipation. Catalytic reactor modeling is an important aspect targeted at understanding the root causes of such limiting factors and opens up avenues to explore novel catalyst and reactor designs to mitigate these limiting conditions.

In this project, you will contribute to building catalytic reactor models that give rise to any of the above-mentioned limiting conditions and study what the underlying dominant factors are. The focus in this project is to use increasingly more complex models to describe the reactive events. In order of very simple to very complex kinetic models, the following set of candidates will be considered:

  1. Power laws
  2. Langmuir-Hinshelwood expressions
  3. Temperature-dependent Power laws
  4. Temperature-dependent Langmuir-Hinshelwood expressions
  5. Microkinetic models
  6. Artifical neural networks

The most prominent research questions are:

Figure 1: Conceptual idea: From a detailed description at the mesocale to a lumped-sum kinetic expression at the macroscale.

Project-specific Learning goals

General learning goals