Heavy-Section 2[fraction one-quarter]Cr-1Mo Steel for Hydrogenation Reactors
Bibliographic record
Abstract
2 1 / 4 Cr-1Mo steel has been widely used for high-temperature, high-pressure hydrogenation reactors such as hydrodesuliurizing reactors. Material degradations that have been found to occur during the service life of such equipment are hydrogen damage, creep embrittlement, and temper embrittlement. A brief review is made of these embrittlements, among which the latter has been considered the most serious. A study of the effect of chemical composition on temper embrittlement showed that susceptibility of 21/4Cr-1Mo steel to temper embrittlement can be well expressed by the J-factor [(Si + Mn) (P + Sn) × 10 4 ] and therefore can be reduced by lowering the J-factor value. 21/4Cr-1Mo steel with very low J-factor is easily obtained by lowering silicon content by applying the vacuum carbon deoxidation (VCD) process Temper embrittlement susceptibility of the weld heat affected zone can be also minimized by the same compositional countermeasures as in the base metal. Temper embrittled steel was found to be further deteriorated by the presence of hydrogen so as to exhibit extremely low fracture toughness. Such hydrogen embrittlement can be reduced by minimizing temper embrittlement susceptibility. Seeking the scale merit of units and to meet the future requirements of coal liquefaction processes, reactors of considerable size (much larger than now exist) are being considered. From this viewpoint, availability of material for heavy-section pressure vessels is discussed based on the heat-treatment characteristics of 2 1 / 4 Cr-1Mo steel. The formation of polygonal ferrite must be avoided in order to ensure adequate mechanical properties of 2 1 / 4 Cr-1Mo steel. This was found possible for a thickness up to about 450 mm when accelerated cooling in water was applied. Mechanical properties of a heavy-section, low-silicon 2 1 / 4 Cr-1Mo steel with thicknesses of 450 and 300 mm were also studied, and sufficient and homogeneous properties were confirmed.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".