Post-Combustion CO<sub>2</sub> Capture Using Solid Sorbents: A Review
Bibliographic record
Abstract
Post-combustion CO 2 capture from the flue gas is one of the key technology options to reduce greenhouse gases, because this can be potentially retrofitted to the existing fleet of coal-fired power stations. Adsorption processes using solid sorbents capable of capturing CO 2 from flue gas streams have shown many potential advantages, compared to other conventional CO 2 capture using aqueous amine solvents. In view of this, in the past few years, several research groups have been involved in the development of new solid sorbents for CO 2 capture from flue gas with superior performance and desired economics. A variety of promising sorbents such as activated carbonaceous materials, microporous/mesoporous silica or zeolites, carbonates, and polymeric resins loaded with or without nitrogen functionality for the removal of CO 2 from the flue gas streams have been reviewed. Different methods of impregnating functional groups, including grafting techniques and modifying the support materials, have been discussed to enhance the performance of the sorbents. The performance characteristics of the solid sorbents are assessed in terms of various desired attributes, such as their equilibrium adsorption capacity, selectivity, regeneration, multicycle durability, and adsorption/desorption kinetics. The potential of metal-organic frameworks (MOFs) is also recognized to determine whether these novel materials provide better CO 2 adsorption capacity under low CO 2 partial pressure. A comprehensive critical review and analysis of the literature on this subject has been carried out to update the recent progress in this arena. A comparison of different solid sorbents at different stages is made. It also includes a brief review on techno-economic analysis and design aspects of sorbent bed contactor configuration. Finally, a few recommendations have been proposed for further research efforts to progress post-combustion carbon capture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".