Corrosion of 2Cr Steel in Supercritical CO2 Saturated Saline Water with Cl, S and Br Anions
Bibliographic record
Abstract
Abstract Carbon capture, utilization, and storage (CCUS) is a set of promising technologies developed to meet global sustainable energy production and climate control goals. Among them, the application of supercritical CO2 captured from various industrial emitters for assisting enhanced oil recovery (EOR) is seen as an economic and efficient pathway. Because of their low cost and acceptable mechanical properties, low alloy steels are the primary materials of construction in s-CO2 EOR systems although they are highly susceptible to corrosion in wet s-CO2 environments, especially with the presence of excessive H2O and other aggressive impurities. This paper studied the corrosion of 2Cr steel in s-CO2 saturated aqueous environments with different impurities. The simulated s-CO2 environment was held at 8 MPa and 50 °C for 96 hours with 0.6 M (3.5 wt.%) NaCl in solution; in subsequent tests, 0.05 M of NaBr or Na2S impurities were added to partially replace NaCl to clarify the effects of other anions. Corrosion rates were determined using weight loss measurements. It was found that 2Cr steel showed the highest corrosion rate of about 2.0 mm/y in the Br-containing environment while its best performance occurred in the S-containing environment (around 1.4 mm/y). FeCO3 and chromium oxides were likely the main corrosion products formed in all environments. FeS and potentially Fe2S3 were also detected in the S-containing environment. The effects of Cl-, Br- and S2- on the corrosion behavior of 2Cr steel in the s-CO2 saturated aqueous environments were discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".