Hot erosion wear and carburization in petrochemical furnaces
Bibliographic record
Abstract
Abstract High temperature alloy stainless steels used in olefins manufacturing furnaces are exposed to extreme environmental degradation processes inclusive of carburization, oxidation and hot erosion wear. A study was undertaken to understand the hot erosion wear phenomenon in relation to substrate composition, atmosphere, temperature, time and the influence of carburization. An erosion wear test rig was designed and constructed to simulate the wear degradation process up to 1200°C. Results have shown a surprising relationship between erosion wear rate and temperature for the most prominent stainless steel alloy used in the industry. A novel coating technology was developed for mitigation that enables the non‐line‐of‐sight application of protective macro‐coatings typically 1 to 5 mm in thickness. Stainless steel coupons treated with these macro‐coatings have exhibited an enhanced resistance to both oxidation and carburization. These macro‐coatings also provide superior hot erosion wear resistance as compared to the uncoated stainless steel. A thorough examination of the microstructure and micro‐mechanical properties of the coatings is presented. Targeted applications include petrochemical furnace fittings (return bends), thermo‐wells and transfer‐line‐exchanger (TLE) surfaces. Commercial furnace trials of the prototype products have been initiated with some prototypes in field trials for over 18 months. Results of both laboratory accelerated testing and field evaluation will be discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".