A novel pre-combustion CO2 capture process using methyl diethanolamine with piperazine (MDEA/PZ) was investigated for the production of blue H2 from a steam methane reformer (SMR). A sensitivity analysis was performed at various operating parameters (such as MDEA or PZ concentration, flash drum pressure, CO2 loading in a lean-amine solvent, and CO2 removal efficiency) using a validated rate-based model. The energy consumption to capture CO2 from SMR gas at 21 bar was evaluated, including the compression energy (up to 30 bar) for the dehydration of captured CO2. For 90% and 95% CO2 removal efficiency, reboiler duties were 1.318 GJ/tonCO2 and 1.364 GJ/tonCO2, and CO2 compression works were 11.673 kW/molCO2 and 11.615 kW/molCO2. The results indicated more than 40% lower reboiler duty than the energy consumption of conventional CO2 capture processes in post-combustion. Subsequently, artificial neural network model (ANN)-based optimization using the differential evolution method was performed. The developed ANN-based optimization suggested the possibility of additional 0.3 % reduction in the equivalent work at a low computational cost. The results indicated that the developed pre-combustion CO2 capture for a SMR was highly competitive in industrial applications. Moreover, the H2 mixture produced at 21 bar is beneficial for a H2 recovery unit because of no need for additional compression energy. Therefore, the MDEA/PZ-based absorption process can effectively contribute to centralized or semi-central blue H2 production from a SMR. This study provides a guideline for the feasible optimization and control of the CO2 absorption process at high feed pressures.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) and funded by the Ministry of Science and ICT ( 2019K1A4A7A03113187 ); and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20214710100060 ).
© 2022 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Nuclear Energy and Engineering
- Fuel Technology
- Energy Engineering and Power Technology