TY - JOUR
T1 - Carbon dioxide hydrogenation to methanol
T2 - Process simulation and optimization studies
AU - Francis, Angel
AU - M.S., Ramyashree
AU - Priya, S. Shanmuga
AU - Kumar, S. Harish
AU - Sudhakar, K.
AU - Fan, Wei Keen
AU - Tahir, Muhammad
N1 - Funding Information:
We extend our sincere gratitude to the MAHE, Manipal, India, for the financial support of the research entitled "Carbon capture and conversion to methanol using the metal-organic framework as an adsorbent material” (Grant ID: 00000191/2019) and VGST, Karnataka, India for the project fund entitled "Photocatalytic conversion of carbon dioxide to methanol using ZIF-8/BiVO 4 /GO and rGO/CuO nanocomposites as an adsorbent material” (2020) (Ref No: VGST/RGS-F/GRD-918/2019–20/2020-21/198).
Publisher Copyright:
© 2022 Hydrogen Energy Publications LLC
PY - 2022/10/22
Y1 - 2022/10/22
N2 - This work investigates process simulation and optimization as an efficient approach to mitigate global warming using carbon dioxide hydrogenation to methanol. Modeling and simulation of hydrogenation to methanol were studied using Aspen Plus V8. Cu/ZnO/Al2O3 catalyst is used to optimize parameters to enhance the reduction of CO2 to methanol. The effect of temperature, pressure, and the feed flow rate on CO2 conversion and CH3OH yield was reported. Response surface methodology (RSM) is used to analyze the chemical equilibrium of the CH3OH production process to obtain an optimal way of assuring a relatively higher CO2 conversion and CH3OH production rate. It helps to evaluate the optimum temperature, pressure, andH2/CO2 molar ratio to achieve maximum CO2 conversion and CH3OH yield. The impact of conversion and CH3OH yield was evaluated using surface plots. The RSM studies show optimized conditions for conversion and CH3OH yield at a temperature of 210 °C, a pressure of 55 bar, and a H2/CO2 concentration of 1:5. The anticipated CO2 conversion and CH3OH yield were 87.56% and 11.22%, respectively, whereas the simulation gave CO2 conversion of 87.65% and CH3OH yield of 11.39%. The generated quadratic model accurately predicts carbon dioxide conversion to methanol. The applicability of the model to forecast CO2 conversion and CH3OH yield is supported by the agreement between the simulated and expected results. This work can be considered a possible solution to overcome the thermodynamic difficulty by providing a higher CO2 conversion and would be beneficial for further investigation in industrial process.
AB - This work investigates process simulation and optimization as an efficient approach to mitigate global warming using carbon dioxide hydrogenation to methanol. Modeling and simulation of hydrogenation to methanol were studied using Aspen Plus V8. Cu/ZnO/Al2O3 catalyst is used to optimize parameters to enhance the reduction of CO2 to methanol. The effect of temperature, pressure, and the feed flow rate on CO2 conversion and CH3OH yield was reported. Response surface methodology (RSM) is used to analyze the chemical equilibrium of the CH3OH production process to obtain an optimal way of assuring a relatively higher CO2 conversion and CH3OH production rate. It helps to evaluate the optimum temperature, pressure, andH2/CO2 molar ratio to achieve maximum CO2 conversion and CH3OH yield. The impact of conversion and CH3OH yield was evaluated using surface plots. The RSM studies show optimized conditions for conversion and CH3OH yield at a temperature of 210 °C, a pressure of 55 bar, and a H2/CO2 concentration of 1:5. The anticipated CO2 conversion and CH3OH yield were 87.56% and 11.22%, respectively, whereas the simulation gave CO2 conversion of 87.65% and CH3OH yield of 11.39%. The generated quadratic model accurately predicts carbon dioxide conversion to methanol. The applicability of the model to forecast CO2 conversion and CH3OH yield is supported by the agreement between the simulated and expected results. This work can be considered a possible solution to overcome the thermodynamic difficulty by providing a higher CO2 conversion and would be beneficial for further investigation in industrial process.
UR - http://www.scopus.com/inward/record.url?scp=85138794023&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138794023&partnerID=8YFLogxK
U2 - 10.1016/j.ijhydene.2022.08.215
DO - 10.1016/j.ijhydene.2022.08.215
M3 - Article
AN - SCOPUS:85138794023
SN - 0360-3199
VL - 47
SP - 36418
EP - 36432
JO - International Journal of Hydrogen Energy
JF - International Journal of Hydrogen Energy
IS - 86
ER -