Toward the practical application of direct CO2 hydrogenation technology for methanol production

Hee W. Lee, Kyeongsu Kim, Jin Joo An, Jonggeol Na, Honggon Kim, Hyunjoo Lee, Ung Lee

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish
Pages (from-to)8781-8798
Number of pages18
JournalInternational Journal of Energy Research
Issue number11
StatePublished - 1 Sep 2020

Bibliographical note

Funding Information:
This work was supported by the Next Generation Carbon Upcycling Project (Project No. 2017M1A2A2043134) through the National Research Foundation (NRF) funded by the Ministry of Science and ICT, Republic of Korea. The authors acknowledge the financial support from Korea Institute of Science and Technology (KIST) institutional program.

Publisher Copyright:
© 2020 John Wiley & Sons Ltd


  • Bayesian optimization
  • CO
  • Methanol
  • hydrogenation
  • superstructure
  • technoeconomic optimization


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