Managing Dissimilar Metal Welding in Hydrogen Rich and Low Temperature Petrochemical Applications
Bibliographic record
Abstract
Abstract Dissimilar metal welds (DSMW) have been used for extensive time in hydrogen rich and low temperature petrochemical applications. Frequently they are used when there is a temperature or corrosivity stream transition aiming to optimize initial project costs. Experience shows a mix of successful and failure applications. Industry standards have slowly adding guidance to prevent failures. The wide variety to DSMWs materials combinations and service conditions poses a significant challenge for standardization. Maximizing the lives of dissimilar metal weldments requires early involvement starting with the design and continuing with the fabrication, operation, and inspection phases. Although common goal is to avoid them, in practice there are situations when is no practical to elude them when constructing new units. In this paper in additional to a literature review section, several real examples of ferritic-ferritic, austenitic-austenitic, and ferritic-austenitic dissimilar welds are described along with the steps involved as described above to prevent failures. Butt welds and weld overlays are covered, as well as some failures. Cases covered include dissimilar metals welds in steam methane reformers, hydroprocessing and olefin units at high and low temperatures. Examples of metallurgical characterization and fitness for service assessments are included. Also, a summary of industry code guidance for designing, fabrication and inspection are included. The paper finally includes a summary of recent research activities in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".