A novel material selection method and application in an aqueous environment containing CO <sub>2</sub> , H <sub>2</sub> S, O <sub>2</sub> and SO <sub>2</sub>
Bibliographic record
Abstract
The corrosion in the aqueous environment of CO 2 –H 2 S–O 2 –SO 2 is extremely severe, causing frequent failures of pipeline and posing a challenging issue for material selection. This paper drew on excellent standards and past experiences to provide a suitable method for selecting materials. The method consisted of four components: standard primary selection, corrosion evaluation selection, mechanical property selection, and economic final selection. A case study demonstrated that the applicable materials for the environment (total pressure of 1.5 MPa, temperature of 30–120 °C, H 2 S content of 0.1–1.5%, CO 2 content of 8–55%, SO 2 content of 0–0.00015% and O 2 content of 1–3%) were composed of 2Cr13 or 316L. Additionally, the main control factors for corrosion rate were ranked as follows: O 2 > H 2 S > temperature > CO 2 > SO 2 . And a reduction in mechanical properties was observed for 2Cr13, 316L, and 825 after corrosion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".