Impacts of Impurities on Corrosion of Supercritical CO2 Transportation Pipeline Steels
Bibliographic record
Abstract
Abstract Carbon dioxide (CO2) is the primary notorious greenhouse gas which is being increasingly emitted to our ecosystem as a result of various human activities. Carbon capture and storage (CCS) is the most promising technology available today for utilizing fossil fuels as reliable energy resources while significantly reducing CO2 emissions and protecting the climate. Pipeline transportation is recognized as the most cost-effective and relatively safe solution in the context of CCS, as it can transport large amounts of CO2 under predetermined and controlled conditions. Depending on CO2 sources and applied capture/separation technologies, however, the transported sc- CO2 stream always contains some aggressive impurities that could lead to extensive corrosion of pipe steels as well as cracking. The effects of impurities on corrosion are far from clear because of very limited field experience, scarce laboratory corrosion data and somewhat conflicting published results. This paper re-examines most public and in-house corrosion data on the effects of six typical impurities to advance the fundamental understanding of how pipeline steels corrode in sc- CO2 environments and identify knowledge gaps for further investigations. It is anticipated to advance the commercial deployment of CCS technology in a cost-effective manner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".