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Record W2153665374 · doi:10.1002/maco.200503900

Hot erosion wear and carburization in petrochemical furnaces

2006· article· en· W2153665374 on OpenAlexfundno aff
R.L. Deuis, A.M. Brown, S. A. Petrone

Bibliographic record

VenueMaterials and Corrosion · 2006
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsMaterials scienceMetallurgyCoatingMicrostructureAlloyErosionPetrochemicalCorrosionComposite materialWaste managementEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.182
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2006
Admission routes1
Has abstractyes

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