Production Low Toughness Case Histories When Manufacturing Low Alloy Steel Pressure Equipment in the Petrochemical Industry
Bibliographic record
Abstract
Abstract Low alloy steels containing chromium and molybdenum (CrMo steels) are widely used in pressure equipment in the petrochemical industry. However, if their manufacturing and production welding are not properly controlled, or if they are operating beyond their design conditions, these steels can exhibit brittle behavior at higher than expected temperatures. There are recommendations and requirements in the industry for controlling key parameters during welding. Before they are performed, production weld procedures must be reviewed against the test results from mock-ups to meet certain requirements such as impact toughness, X-bar, and J-factor. However, there is not much industry guidance for assuring the production welding occurs as per the reviewed parameters. This paper discusses three case histories of CrMo alloyed pressure equipment. One of the case histories is for a reactor, and the second case history is for a heat exchanger. The final case history discussed is for piping material. Details of manufacturing, production welding, and testing are discussed in the case histories. For some of the case histories, this includes finding lower impact toughness during production test welding than was expected based on the procedure qualification record. Finally, the paper discusses the importance of additional steps that could be added to the inspection test plan to improve the monitoring of production welding.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".