Utilizing modeling tools to design a reactor and a catalyst for dry reforming of methane

Abstract

Dry reforming of methane (DRM) is a catalytic reaction in which two greenhouse gases (CO2 and CH4) are converted to synthesis gas (a mixture of CO and H2), an important precursor to produce various chemical products. DRM is highly attractive due to its ability to convert greenhouse gases; however more research is required to address its process challenges: (a) high energy requirement, (b) low synthesis gas quality, and (c) catalyst deactivation due to carbon formation. Nickel (Ni) catalyst is widely used for methane reforming and thus suitable for DRM as well. However, it is prone to carbon formation due to reactions like methane decomposition and Boudouard reaction causing deactivation. A novel bimetallic nickel-copper (Ni-Cu) catalyst was previously developed in our research group at an atomistic scale using density functional theory (DFT) approach to address the Ni catalyst’s carbon formation challenge. The Ni-Cu catalyst provides significant carbon resistance and superior stability compared to the conventional Ni catalyst. The catalyst’s performance was proven and validated experimentally in our laboratory’s state-of-the-art bench-top reactor. The scope of this thesis is to explore the scalability of the novel Ni-Cu catalyst using a mathematical modeling approach. The approach comprises of utilizing an existing one-dimensional (1-D) pseudo-homogeneous reactor bed model supported by lumped kinetics of a network of complex reactions that take place during DRM. This model was updated by including accountability of carbon formation and advancing further to account for detailed transport properties. The kinetics of Ni to Ni-Cu catalyst were scaled using a novel approach utilizing DFT and results in providing predictions for the bulk-scale kinetics performance in terms of carbon formation rates. The experimental validation of the model was first tested using a conventional monoatomic Ni catalyst then later extended to predict the performance of the Ni-Cu catalyst. The 1-D model yielded results that match within an error margin of 5% with experimental data especially on CH4 conversions. The developed 1-D model serves as a tool to predict the performance of the Ni-Cu catalyst at various reactor scales and to conduct future optimization and process intensification studies for DRM process by maximizing feed conversions.