Enhanced kinetic model identification for gas–solid reactions through Computational Fluid Dynamics

Abstract

Gas–solid reactions often play key roles in chemical engineering applications. To understand and design processes featuring such heterogeneous reactions, kinetic models are crucial. One way to identify kinetic models is via thermal analysis experiments. Even if those experiments are carried out meticulously, there will be some deviation between nominal reaction conditions and the actual reaction conditions directly at the reaction site. For situations, where these deviations are not negligible, we propose a new approach to compute the reaction conditions directly at the sample, based on the experimental data. A key feature of our approach is that no kinetic model is required for the simulation. For this reason, the enhanced data can be used for kinetic model identification. Though, a kinetic modeling method that can process arbitrary data is required, because the enhanced kinetic data will not obey the idealized assumptions of constant temperature or constant heating rate. To showcase our approach, we applied it to the reaction system CuO/Cu₂O. Kinetic models with nominal and simulated values are derived with the TensorNPK method, showing the influence of the enhanced kinetic data on the identified reaction kinetics.