Bibliographic record
Abstract
Abstract: This paper presents a review of the research on CO2 capture by lime-based looping cycles undertaken at CanmetENERGY’s (Ottawa, Canada) research laboratories. This is a new and very promising technology that may help in mitigation of global warming and climate change caused primarily by the use of fossil fuels. The intensity of the anticipated changes urgently requires solutions such as more cost-effective technologies for CO2 capture. This new technology is based on the use of lime-based sorbents in a dual fluidized bed combustion (FBC) reactor which contains a carbonator—a unit for CO2 capture, and a calciner—a unit for CaO regeneration. However, even though natural materials are cheap and abundant and very good candidates as solid CO2 carriers, their performance in a practical system still shows significant limitations. These limitations include rapid loss of activity during the capture cycles, which is a result of sintering, attrition, and consequent elutriation from FBC reactors. Therefore, research on sorbent performance is critical and this paper reviews some of the promising ways to overcome these shortcomings. It is shown that reactivation by steam/water, thermal pre-treatment, and doping simultaneously with sorbent reforming and pelletization are promising potential solutions to reduce the loss of activity of these sorbents over multiple cycles of use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.683 | 0.577 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".