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Record W2902540458 · doi:10.1115/pvp2018-85070

Sulfidation Rate Prediction on Tube-to-Tubesheet Joints in a Waste Heat Boiler in a Sulphur Plant

2018· article· en· W2902540458 on OpenAlexaff
Feng Ju, A.J. Miller, Simon Yuen, Brian Tkachyk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsSulfidationWeldingBoiler (water heating)Materials scienceMetallurgyHigh-temperature corrosionJoint (building)CorrosionCarbon steelStructural engineeringWaste managementEngineeringSulfur

Abstract

fetched live from OpenAlex

Sulfidation corrosion of the carbon steel tubes at the tube-to-tubesheet joint often governs the life of waste heat boilers in sulphur recovery plants. Conventional tube joints typically have a welded joint located at the hot-side face of the tubesheet. An alternative design involves welding the tubesheet joint at the cold-side face of the tubesheet, close to the boiler feed water. The alternative design also employs stainless steel cladding on the tubesheet face and a tube-hole sleeve selectively at high-temperature locations. Finite element heat transfer analysis is used to establish the thermal profiles of the conventional and the alternative designs. From the worked example, the alternative design provided a lower metal temperature by approximately 80 °F at the joint, as compared to the conventional tube joint. Sulfidation rate prediction based on a sample gas composition using ASSET (Alloy Selection System for Elevated Temperatures) Software predicts that the alternative design can reduce the sulfidation rate by 35% because of the lower metal temperature.

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 score0.179
Threshold uncertainty score0.660

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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