Multiscale model and informatics-based optimal design of experiments: Application to the catalytic decomposition of ammonia on ruthenium

Fundamental multiscale models are increasingly being used to describe complex systems. Microkinetic models, which consider a detailed surface reaction mechanism containing all relevant reactions, are a prototypical multiscale model example. The computational effort in calculating all parameters of a multiscale model for real systems from first principles is prohibitive, and parameter uncertainty still linuts full quantitative capabilities of these models. This motivates the development of rational model-based techniques in order to refine uncertain parameters and assess the global (in the entire experimental parameter space) model robustness. Herein we describe physics-aided methods (sensitivity, partial equilibrium, and most abundant reactive intermediate analyses) and statistics-based methods (A, D, and E optimal designs) for the design of experiments. While our methods are fairly general, we demonstrate them for the catalytic decomposition of ammonia on ruthenium to produce hydrogen. A global Monte Carlo method is used to search the operating space to generate possible optimal operating conditions for experiments. Our analysis illustrates that the D optimal and sensitivity-based designs are most promising and generate conditions that delineate important chemistry. It is shown that a standard design around the D optimal point may not be useful for highly nonlinear problems. Instead, informatics methods are proposed to identify optimal regions of the operating space. It is found that the experiments conducted within these regions have a high probability of providing useful kinetics information. It is also shown that the overall direction of the reaction (ammonia decomposition vs synthesis) and the macroenvironment (type of reactor) significantly affect the optimal design. This demonstrates for the first time the effect of macroscopic scales on microscopic ones with important implications for optimal design and product design.