Methanol production via direct CO2 hydrogenation is one of the most promising means of utilizing greenhouse gases owing to the significant market for methanol and the potential to simultaneously reduce CO2 emissions. However, the practical applications of this process still suffer from high production costs owing to the expensive raw materials required and the severe operating conditions. Herein, we propose an economically attractive methanol production process that also works to sequester CO2, developed through technoeconomic optimization. This economically optimized process design and the associated operating conditions were simultaneously obtained from among thousands of possible configurations using a superstructure optimization. A modified machine learning-based optimization algorithm was also employed to efficiently achieve this complex superstructure optimization. The optimum process design involves a multistage reactor together with an interstage product recovery system and substantially improves the CO2 conversion to greater than 52%. Consequently, the revenue obtained from methanol production changes from a $4.3 deficit to a $2.5 profit per ton. In addition, the proposed process is capable of generating the same amount of methanol with only half the CO2 emissions associated with conventional methanol production methods. A comprehensive sensitivity analysis is also provided along with the optimum process design to identify the influence of various technoeconomic parameters.
- Bayesian optimization
- technoeconomic optimization