Pre-combustion CO2 capture using amine-based absorption process for blue H2 production from steam methane reformer. (15th June 2022)

• Record Type: Journal Article
• Authors: Oh, Hyun-Taek; Kum, Jaesung; Park, Junhyung; Dat Vo, Nguyen; Kang, Jun-Ho; Lee, Chang-Ha
• Is Part Of: Energy conversion and management. Volume 262(2022)
• Publication Date: 2022-06-15
• Subjects: Pre-combustion CO2 capture -- Methyl diethanolamine/piperazine-based absorption -- Steam methane reformer -- Artificial neural network model-based optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115632
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Studied pre-combustion CO2 capture process using blend amine to produce blue H2 . Performed an ANN model-based optimization of CO2 capture process for SMR. Achieved 1.32 GJ/tonCO2 of reboiler duty at 90% CO2 removal efficiency. Over 90% CO2 removal efficiency led to a marginal increase in equivalent work. Suggested valuable references for development of CO2 capture process in SMR. Abstract: 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.