Alloy Carburization at Temperatures of 1,200 - 2,100°F (650 - L,150°C)
Bibliographic record
Abstract
Abstract A large body of knowledge on the carburization of engineering alloys used in high-temperature petrochemical processing equipment is being developed. The capability includes: the facility to perform thermochemical assessments of carbide formation on complex alloys in diverse conditions and the capability to predict carburization for various exposure conditions. The alloys considered are widely used, commercial and based upon mixtures of Fe-Cr-Ni-Co. The gaseous exposure conditions considered contain CH4-H2-H2S-H2O. The thermochemical data models have been prepared to predict phase formation of potential corrosion products. They are based upon extensive analyses of all available thermochemical data for all possible solid and liquid compounds and solutions based upon all combinations of Fe-Cr-Ni-Co-S-C-O-N and for all possible gaseous species containing S-C-O-H-N. As well, the alloying elements Al, Mo, Nb, Ti, V, W, Mn and Si are fully included in the alloy and carbonitride solution models. Comprehensive solution models are used in Gibbs free energy minimization calculations to assess the interactions of multiple species in variable composition solid and liquid phase alloys, sulfides, oxides, carbides, and nitrides. This capability is used to predict the most stable corrosion product formation, which is then used to infer the dominant corrosion mechanism, in complex conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".