(661u) Microkinetic Modelling of CO2 Hydrogenation to Methanol on ZrOx Promoted Cu Based Catalysts-a Multi-Site Approach

The increasing CO₂ emissions led to global warming, necessitating new sustainable technologies and processes to mitigate its deleterious impacts. Carbon Capture and Utilization (CCU) will effectively delay the rate of CO₂ accumulation in the atmosphere. This work focuses on converting CO₂ to methanol, a high-value chemical, on the CuO_x/ZnO_x/ZrO_x/Al₂O₃ catalyst. The conversion of CO_{2 ­}and selectivity towards methanol can be optimized by tuning the reactor operating conditions. Here, we combined lab-scale experiments, Density Functional Theory (DFT) and the microkinetic modelling techniques to obtain fundamental insights and develop detailed kinetic models. Reduction of CO₂ carried out in a tubular down-flow fixed-bed reactor using CuO_x/ZnO_x/ZrO_x/Al₂O₃ catalyst, and H₂/CO₂ ratio of 2.98 at different conditions demonstrated the sensitivity of methanol selectivity on the reaction temperature. Mechanistic studies on a Zr₁Zn₂O cluster on the Cu (111) surface representing the inverse catalyst showed that most reactions occur on the Cu-Zn and Cu-Zr interface. A mean-field microkinetic model with more than one type of active site was developed for this system, and simulations are carried out using the ANSYS-CHEMKIN^® software package. Three different pathways for methanol formation were identified - Formate pathway, Carboxyl pathway, Direct CO₂ dissociation pathway. Based on the energetics obtained from the DFT computations, the direct CO₂ dissociation pathway is dominant. CO is the main by-product in the CO₂ hydrogenation to methanol. CO* desorption barrier is observed to be higher (1.73 eV), facilitating CO hydrogenation (0.8 eV) towards methanol formation, enhancing methanol's selectivity. CO* from CO₂ dissociation and HCOO* are expected to dominate the surface during the reaction. This multi-site microkinetic model can identify the optimum reaction conditions to facilitate the reaction in the desired pathway. The experiments and the developed model in combination help us understand the system better for process optimization and catalyst design.