CO2 capture for refineries, a practical approach
Bibliographic record
Abstract
This paper evaluates the opportunities and associated costs for post-combustion capture at a world-scale complex refinery. It is concluded that it is technically feasible to apply post-combustion capture at such a refinery. The CO 2 source most suited for capture appears to be a combined stack, but there are a number of other sources that may be targeted at comparable costs. In total these sources may form about 40% of the overall refinery emissions. Our evaluations show the costs of capture from such sources based on available amine technology will be about 3–4 times higher than the current carbon trading values. The capture of CO 2 from a large amount of smaller CO 2 sources will bring along even much higher costs. A high-level study of the CO 2 emissions profile of a number of Shell refineries shows that, typically, up to 50% of the emitted CO 2 may be captured at costs comparable to those found at the reference refinery. About 10–20% of concentrated CO 2 associated with hydrogen manufacturing may be captured at lower costs. The remainder of emitted dilute CO 2 will bring along significantly higher costs. Based on this study, it is concluded for the justification of the implementation of post-combustion capture at refineries, either a significant increase in carbon trading values, mandatory regulations, or a major technological break-through is required.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".