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Record W2136759507 · doi:10.2118/163110-pa

Novel Leak Detection Method for Sulfur Recovery Unit Condensers

2013· article· en· W2136759507 on OpenAlex
Timothy Cheung, Michael Scheck, Adam Goodmurphy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOil and Gas Facilities · 2013
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsHydrogen sulfideSulfurSour gasWaste managementCondenser (optics)Natural-gas processingLeakTube (container)Natural gasChemistryEnvironmental sciencePetroleum engineeringEngineeringMaterials scienceMetallurgyEnvironmental engineering

Abstract

fetched live from OpenAlex

Summary At a Shell sour-gas processing facility in Alberta, Canada, hydrogen sulfide contained within the natural gas is converted into elemental sulfur by means of a sulfur recovery unit (SRU). Tube leaks present in a water-cooled SRU condenser can lead to a variety of process issues, including corrosion and the oxidative formation of acidic species. Leak indicators, such as loss of sulfur flow in the rundown and a frothy sulfur appearance, were observed. This work devised a novel method to verify such leaks within a SRU condenser. Using basic pump equipment and an inexpensive commodity chemical tracer, lithium hydroxide, a leak was diagnosed without the shutdown of the unit and with a minimal of expenditures and hazards to operators. The unit was inspected and the tube leak plugged, which enabled the resumption of normal operations.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.593
Threshold uncertainty score0.303

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 head0.017
GPT teacher head0.202
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