Optimization Methods for Efficient Solution of Reformulated Microkinetic Models.

摘要

Microkinetic models, combined with experimentally measured reaction rates and orders, play a key role in elucidating detailed reaction mechanisms in heterogeneous catalysis and have typically been solved as systems of ordinary differential equations (ODEs) or differential algebraic equations (DAEs). In the present work, we demonstrate a new approach to fitting those models to experimental data. For a small model of methanol synthesis by CO/CO 2 hydrogenation over a supported-Cu catalyst in a continuous stirred tank reactor (CSTR), we achieved a 1000-fold increase in solution speed by reformulating the microkinetic model from a system of ODEs to a system of nonlinear equations (NLP). The reduced computational cost allows a more systematic search of the parameter space, leading to better fits to the available experimental data.;We applied this approach to the full methanol synthesis network and identified over 200 good fits to the experimental data. We analyzed the fits to provide insight into the nature of the active site and reaction mechanism. We took the best of the fits obtained and optimized the experimental variables (pressure, temperature, feed rate and feed composition) to identify the conditions which predict a maximum production for methanol, the maximum production of methanol from CO2, or maximum conversion of CO or CO2 to methanol. We also performed optimizations to identify the best conditions to identify reactive intermediates on the catalyst surface.;Finally we took the first steps to extend our approach to plug flow reactors (PFRs) (which are described by systems of DAEs) using collocation on finite elements to transform the DAEs into an NLP. We used our transformed model to consider the optimal conditions for the water gas shift reaction on Cu.