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Record W4312567502 · doi:10.1115/pvp2022-84863

Case History of Hydrotreater Prefeed Heater Fire Recovery

2022· article· en· W4312567502 on OpenAlexaff
Jorge Penso, Neil Park, Mitul Dalal, Alexandra Hosack

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsTube (container)Materials scienceHydrogenConvectionRadiant heatNuclear engineeringComposite materialEngineeringChemistryMeteorology

Abstract

fetched live from OpenAlex

Abstract A prefeed heater in a hydrotreater unit experienced a fire while being prepared for shutdown during a turnaround. The prefeed hydrotreater heater was a mixed operation heater that typically operated in mixed mode with hydrogen and hydrocarbon bitumen, but at the time of the incident was operating in hydrogen only mode in order to hot hydrogen strip the hydrotreater. The heater had been in operation since 2011 and had a design fluid temperature of 425 °C (797 °F), a tube metal temperature of 570 °C (1058 °F) and design pressure of 16,800 kPag (2437 Psig). The normal operating pressure was 15,000 kPag (2176 Psig). The radiant and convection tubes were fabricated from Type 316Nb stainless steel (UNS31640). There was a tube rupture in the radiant section that caused the fire and the subsequent failure investigation established that the temperature in the radiant section at the rupture location had reached in excess of 850 °C (1562 °F) for over a 15-minute period prior to rupturing. The following paper outlines the failure analysis and root causes which caused the incident, while detailing the recovery and construction of the heater, including the fitness-for-service methods and inspections conducted. The heater was partially rebuilt with the convection section tube material being recovered and the radiant section being rebuilt in a different metallurgy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.991

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)0.0100.000

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 head0.016
GPT teacher head0.182
Teacher spread0.166 · 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.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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