Development and Assessment of Catalytic Microkinetic Models for Rational Catalyst Design
Date
2009-05
Authors
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Publisher
University of Delaware
Abstract
Catalysts design for practical applications has traditionally been a loosely
directed random search among the many available parameters for modern catalysts,
such as material composition, structure, preparation methods, etc. Meanwhile, advances in theoretical and experimental methods have led to an understanding of
individual surface reactions under idealized conditions. This work investigates both
the uncertainties involved in state of the art full reactor scale models and methods
to reduce them. Previously published microkinetic models for the decomposition of
ammonia were used to predict the ideal atomic binding energies a catalyst surface.
The effect of reactor conditions, such as gas composition, temperature, and pressure,
was found to weakly impact these conditions. However, model choice played a large impact on model predictions, suggesting the refinement of model parameters.
Once improved parameter estimation procedures were used, changes to parameters
on the order of the estimated uncertainty had only small effects. Catalyst optimizations were then demonstrated for Preferential Oxidation Chemistry, where multiple objectives (conversion and selectivity) are required. Improved methods for fitting microkinetic models from transient experimental data were implemented and included separately. Examples are given for the ammonia decomposition chemistry.