This work addresses the modeling and multi-objective optimization of methanol synthesis to efficiently utilize CO2 from the CO2 emissions and economics perspectives. Kinetic reactors for reforming and methanol synthesis reactions were used in the process simulator for modeling the entire process, and multi-objective optimization was conducted using the developed process model to maximize CO2 reduction and the economic profit. The feed composition, operating temperature and pressure of the reformer, and utility temperature of the methanol synthesis reactor were considered as arguments in the non-dominated sorting genetic algorithm (NSGA II) method with the net change of CO2 and economic profit as the objective elements, and the Pareto front showed a trade-off between CO2 reduction and economic profit. When the amount of CH4 in the feed was fixed at 500 kmol/h, CO2 reduction was 11,588 kg/h, whereas the profit was -5.79 million dollars per year. Meanwhile, a maximum profit of 20 million dollars per year resulted in CO2 emissions of 7,201 kg/h. The feed composition had the most significant influence on both objective elements (net change of CO2 and economics); as CO2 in the feed increased, CO2 reduction increased and profit decreased, while the increase of H2O in the feed increased CO2 emissions and profit.

Methanol Synthesis Process Reforming Multi-objective Optimization Economic Analysis CO2 Reduction

Meylan FD, Moreau V, Erkman S, J. CO2 Utilization, 12, 101 (2015)
Otto A, Grube T, Schiebahn S, Stolten D, Energy Environ. Sci., 8, 3283 (2015)
Ma J, Sun N, Zhang X, Zhao N, Xiao F, Wei W, Sun Y, Catal. Today, 148, 221 (2009)
Schaub T, Paciello RA, Angew. Chem.-Int. Edit., 50, 7278 (2011)
Xiang X, Guo L, Wu X, Ma X, Xia Y, Environ. Chem. Lett., 10, 295 (2012)
Nikolaidis P, Poullikkas A, Renew. Sust. Energ. Rev., 67, 597 (2017)
Huang CH, Tan CS, Aerosol Air Quality Res., 14, 480 (2014)
Park N, Park MJ, Baek SC, Ha KS, Lee YJ, Kwak G, Park HG, Jun KW, Fuel, 115, 357 (2014)
Graaf GH, Stamhuis EJ, Beenackers AACM, Chem. Eng. Sci., 43, 3185 (1988)
Park N, Park MJ, Ha KS, Lee YJ, Jun KW, Fuel, 129, 163 (2014)
Zhang C, Jun KW, Gao R, Kwak G, Park HG, Fuel, 190, 303 (2017)
Alizadeh A, Mostoufi N, Jalali-Farahani F, Int. J. Chem. React. Eng., 5, A19 (2007)
Atsonios K, Panopoulos KD, Kakaras E, Int. J. Hydrog. Energy, 41, 2202 (2016)
Van-Dal ES, Bouallou C, J. Clean Prod., 57, 38 (2013)
Sinnott RK, Chemical engineering design, Second edition. edn. Pergamon, Oxford (1993).
Walas SM, Chemical process equipment: Selection and design, Elsevier Science & Technology Books, Place of Publication Not Identified (1988).
Commodity Markets.
Douglas JM, Conceptual design of chemical processes, International edn. McGraw-Hill, New York, London (1988).
Home - Methanol Market Services Asia.
Peters MS, Plant design and economics for chemical engineers, McGraw-Hill, New York (1958).
Seider WD, Seader JD, Lewin DR, Product and process design principles: Synthesis, analysis and evaluation, Rev. of: Process design principles, 1999, Wiley, New York (2004).
Homepage - U.S. Energy Information Administration (EIA).
CAMEO Chemicals | NOAA.
Deb K, Pratap A, Agarwal S, Meyarivan T, Ieee T Evolut Comput, 6, 182 (2002)
Bao C, Xu L, Goodman ED, Cao L, J. Comput. Sci., 23, 31 (2017)
GitHub - edgarsmdn/Aspen_HYSYS_Python: Aspen HYSYS - Python connection.
Blank J, Deb K, IEEE Access, 8, 89497 (2020)