This study focuses on model-based optimization to facilitate the process scale-up of CO hydrogenation into sustainable aviation fuel (SAF)-range hydrocarbons via a single-step process. The process initiates with carbon monoxide hydrogenation to methanol over Cu/ZnO/Al₂O₃ catalysts, followed by methanol dimerization over Al₂O₃ in a mixed bed configuration. Dimethyl ether (DME), formed in this step, undergoes further homologation over a Cu/BEA catalyst to produce C₄-C₇ hydrocarbons. Key challenges in C₁ upgrading chemistry arise from significant heat and mass transfer limitations, which constrain process yield. Through model-predicted catalyst design and reactor optimization, it is possible to enhance CO conversion, increase C₄₊ yields, and improve the overall production rate, laying the groundwork for successful scale-up.
A two-dimensional axisymmetric, non-isothermal packed-bed reactor model was developed using COMSOL Multiphysics 6.2 to address these challenges. This model captures heat and mass transfer phenomena across micro, meso, and macro scales, solving heat, mass, and momentum equations at the two-dimensional bed scale while simultaneously incorporating mass and energy equations at the one-dimensional particle scale. This powerful feature differs from a traditional approach and enables direct integration of heat and mass transfer limitations, and thereby enables accurate prediction for process scale-up.
Model validation was initially performed using lab-scale experimental data under isothermal conditions, with catalyst and bed properties (such as porosity, permeability, and catalyst pore size) extracted via computational analysis of X-ray tomography (XCT) data. The validated model was then applied to the design of a pre-pilot-scale reactor (1 kg of catalyst) to assess scale-up challenges. The model revealed strong mass transfer limitations, particularly over the Al₂O₃ catalyst. To mitigate these limitations, smaller catalyst pellets (1-2 mm) and higher porosity (50-60%) were recommended.
Additionally, the process exothermicity introduces heat management challenges. Effective heat removal strategies are essential to avoid thermodynamic limitations and catalyst deactivation, with an optimal reaction temperature of 250 ^oC identified to seamlessly integrate all three reaction steps in a single process. These findings provide critical insights for scaling up this CO hydrogenation process to larger reactor designs and higher throughput applications.

Adam Yonge

Appearances