Effect of Impurity SO2 on Corrosion and Stress Corrosion Cracking of X65 Steel in Supercritical CO2 Streams
Bibliographic record
Abstract
Abstract Pipeline transportation is a critical section in the context of carbon capture and storage (CCS) systems. Transported supercritical CO2 (sc-CO2) usually contains corrosive impurities, particular SO2, that can lead to exacerbated corrosion damage on pipeline integrity. However, there is still no clear image to describe how the impurities affect corrosion due to very limited database and some controversial results. More importantly, there is a serious concern on the stress corrosion cracking (SCC) susceptibility of the pipeline because of the presence of corrosive agents and high operating pressure of sc-CO2 mixtures. Surprisingly, few works have been done on this issue. In this paper, the effects of SO2 on corrosion and SCC susceptibility of X65 steel were investigated in sc-CO2 environment containing impurities SO2, O2 and H2O. The results show that the addition of only 100 ppm SO2 into the sc-CO2 system significantly increases corrosion rate, consistent with some previous results. Under such corrosive condition, however, SCC crack is not found in the steel specimen even heavily strained up to 0.6%. This finding opens a new window on the development of corrosion control strategy of sc-CO2 pipelines. Corresponding corrosion mechanisms in the media are also discussed and proposed.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".