Computational Methods for Analysis of Complex Catalytic Reaction Networks

Complicated catalytic reaction networks are pervasive in many different chemical processes and are therefore extensively studied in both academic and industrial settings. Computational methods are often utilized to support and direct experimental efforts for these types of systems, as they provide a strong theoretical basis for experimental design and have the potential to reduce the experimental time required to fully explore operating conditions required for successful activity. Microkinetic, or mechanistic, modeling, quantum mechanical (QM) calculations, and group additivity methods are all examples of computational methods that are applied to complex reaction networks in order to support experimental efforts and glean a better understanding of reaction mechanisms and catalytic performance under various reaction conditions.;In this work, various computational methods are used to investigate and analyze complex catalytic reaction networks. Microkinetic modeling is used with rate parameter estimation techniques and experimental observations to identify a reaction mechanism describing catalytic oxidation on a novel heterogenous organometallic complex. QM calculations are used to calculate enthalpies of formation for carbenium ions, leading to group additivity values that facilitate calculation of necessary rate coefficients for automatically generated reaction networks. QM methods are also utilized to conduct a study of atomic adsorption energies on multiple monometallic and bimetallic transition metal catalysts, generating a database of values to be used with rate parameter estimation methods. These results are applied for methane conversion to methanol, a complicated reaction scheme with significant conversion and selectivity challenges. Microkinetic modeling is used again with estimated rate parameters to study methane conversion, selectivity to methanol, and dominant surface reactions, resulting in recommendations for the most promising catalyst candidates for successful conversion of methane to methanol. These contributions add to current understanding of catalytic behavior as well as extend the computational methods for future research opportunities on new systems.