| 초록 |
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 multi-stage reactor together with an inter-stage 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. |
