Review of Recommended Practices for Removal of Hydrogen through Bakeout for Welded Repairs
Bibliographic record
Abstract
Abstract Welding in accordance with a fabrication code for new construction may produce hydrogen weld cracking but it has largely been mitigated using suitably designed welding procedures and advances in steel processing and ferrous metallurgy. However, when some key elements of a “good” welding practice are omitted, or in some cases not qualified on full-scale welding mock-ups, weld cracking may occur. Weld cracking can occur in both new construction and welded repairs of in-service components. In many cases, failure is attributed to hydrogen introduced into the weld metal and heat affected zone from a combination of the atmosphere, service conditions, and welding. In some cases, it may be necessary to remove a significant portion of the hydrogen in the steel to prevent cracking following a welded repair. The communication presents a summary of hydrogen bakeout history as a means of removing hydrogen from a component, compiles existing recommendations regarding hydrogen bakeout in codes and standards, and reviews the results obtained from an industry survey of energy producers in Western Canada. The paper concludes with a proposed methodology for selecting bakeout parameters based on the hydrogen concentration derived from sour service conditions reported by one of the survey respondents.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".