Choice of the kinetic model significantly affects the outcome of techno-economic assessments of CO2-based methanol synthesis

Carbon dioxide hydrogenation to methanol is a cornerstone of the CO2 utilization toolkit, and its comparison to fossil-based methanol through techno-economic assessments (TEAs) has helped establish barriers to its commercial feasibility. TEAs are often performed in process simulation software that relies on kinetic models (KMs). The choice of KM could influence the outcome of the TEA, however, their effect has not been quantified earlier. This study quantifies this effect through TEAs performed using three different KMs in Aspen PlusTM. Three KMs are selected for comparison: two of them are commonly used in TEAs while also a third, a recently published model, will be studied herein. The models are first validated in Aspen PlusTM and then compared in a series of sensitivity analyses in a one-pass reactor. Finally, a TEA study is conducted for a large-scale methanol plant to investigate the effects of the KM choice. It was found that the choice of the kinetic model significantly influences the results of TEAs as it can result in a 10% difference in the levelized cost of methanol. This can be mainly attributed to differences in one-pass yield. As CO2 utilization approaches economic viability, understanding such uncertainties will be crucial for successful project planning. Hence, these results suggest that extending a TEA's sensitivity analysis to cover the KM's contribution could increase confidence in the robustness of the TEA.

Automated summary

This study compares the application of different kinetic models in the process of carbon dioxide hydrogenation to methanol and finds that the choice of kinetic model significantly influences the results of techno-economic assessments, with a potential 10% difference in the levelized cost of methanol. These results suggest that understanding and considering such uncertainties are crucial for successful project planning, increasing confidence in the robustness of the assessments.