The influence of temperature, H2O, and NO2 on corrosion in CO2 transportation pipelines
Bibliographic record
Abstract
The expansion of Carbon Capture, Utilization, and Storage (CCUS) highlights the growing need for carbon dioxide (CO 2 ) pipeline transportation. While pure CO 2 is non-corrosive, impurities such as H 2 O and NO 2 create a corrosive environment that risks pipeline integrity. This study investigates how H 2 O and NO 2 concentrations, along with temperature, influence corrosion under CO 2 pipeline conditions. The investigation was performed in an autoclave setup emulating a linear velocity of 0.96 m/s at 100 bar and temperatures of 5 ∘ C and 25 ∘ C, testing X52 and GR70, and a more corrosion-resistant 9Cr alloy. The results indicated that the presence of NO 2 elevated the corrosion rate compared to scenarios without. Low H 2 O concentration led to a corrosion rate of up to five times higher at 5 ∘ C, compared to at 25 ∘ C, in the presence of NO 2 . Low to moderate corrosion was observed for the carbon steels without NO 2 and with 70 ppmv H 2 O at both temperatures. Reducing the H 2 O concentration below 70 ppmv and removing NO 2 , while SO 2 and O 2 are present, will only result in low to moderate corrosion in the carbon steel CO 2 pipeline. The corrosion rate for X52 and GR70 was 0.065 mm/y and 0.016 mm/y higher or 5 and 3 times greater, respectively, at 5 ∘ C compared to 25 ∘ C. The study concludes that H 2 O should be maintained below 70 ppmv and NO 2 should be eliminated to prevent severe corrosion. Emphasizing the importance of CO 2 specification compliance and the need for further research into CO 2 compositions that align with the specifications.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".