期刊
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
卷 10, 期 -, 页码 64-73出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijggc.2012.05.018
关键词
CO2 capture; Solvent absorption; Pre-combustion; Potassium carbonate (K2CO3); Process simulation, Boric acid
类别
资金
- Brown Coal Innovation Australia (BCIA)
- Victorian Government ETIS Brown Coal RD Program
- HRL Developments Pty Ltd. (HRL)
- Process Group (PG)
- Australian Government's Cooperative Research Centre program
- Particulate Fluids Processing Centre (PFPC
- Australian Research Council
Pre-combustion capture of carbon dioxide (CO2) from synthesis gas has been demonstrated using a solvent absorption pilot plant. The plant was designed to capture 30-50 kg/h (similar to 1 tonne/day) of CO2 from 300 kg/h of syngas. The solvent used in these trials was a potassium carbonate (K2CO3) solution. Potassium carbonate shows promise as a solvent for CO2 capture because it requires lower energy for regeneration and has a low environmental impact when compared with the traditional amine-based solvents. However, the rate of CO2 absorption in K2CO3 is slow and as such there have been several studies evaluating rate promoters for this process. Boric acid has been identified as one such promoter. The pilot plant in this study was successfully operated on a campaign basis for 16 days using both an un-promoted 30 wt% K2CO3 solution as well as a 30 wt% K2CO3 solution promoted with 3 wt% boric acid. There was no net improvement in the absorption of CO2 observed in the presence of boric acid. This result is attributed to the boric acid having reduced the pH and therefore OH- concentration of the system, which in turn reduced the rate of the controlling kinetic reaction to form potassium bicarbonate (KHCO3) from CO2. Changes in the solvent physical properties, due to interaction with syngas impurities, were found to influence the hydrodynamic performance of the packed columns. Bicarbonate precipitation and vessel level control issues also led to operational difficulties. ASPEN PIus (TM) simulations have been developed to predict the performance of the plant. In general the model predicts the performance of the plant well (to within +/- 5%) and will be important for future process development, design and optimisation. (c) 2012 Elsevier Ltd. All rights reserved.
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