In this talk, we propose a hierarchical multiscale simulation framework for development of microkinetic models. The idea of hierarchical multiscale modeling and simulation is to start with the simplest possible “sound” model at each scale and identify the important scales and (‘active') model parameters at each scale. Once this is accomplished, one assesses the model accuracy by comparison with data and potentially improves the model of the important scale(s) and the associated active parameters using a higher-level model or theory. For example, the simplest identification tool employed extensively and successfully in chemical kinetics is local sensitivity analysis. Upon improvement of models and parameters, another iteration is taken until convergence is achieved, i.e., the important scales and parameters do not change between successive iterations. We will demonstrate the power of the approach with the specific examples of water gas shift, preferential oxidation, and steam reforming reactions on noble metals.
In order for these models to become useful, we discuss multiscale model-based design of experiments to optimize the chemical information content of a reaction mechanism in order to improve the fidelity and accuracy of reaction models. Extension of this framework to catalyst design will be touched upon. We illustrate this approach using the examples of ammonia decomposition on Ru, to produce hydrogen for PEM fuel cells, and the water-gas shift reaction on Pt, for converting syngas to hydrogen.