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Record W2558227522 · doi:10.1115/pvp2016-63015

Considerations in the Capacity Uprating of an EOR HRSG

2016· article· en· W2558227522 on OpenAlexaff
John J. Aumuller, Carlos F. Lange, Michael Humphries

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHeat recovery steam generatorPipingBoiler (water heating)Steam turbineSteam-electric power stationHeat recovery ventilationFlue gasEnergy recoveryEconomizerSteam drumThermal power stationCorrosionSteamingNuclear engineeringCombined cycleEngineeringEnvironmental sciencePetroleum engineeringTurbineWaste managementSuperheated steamMaterials scienceMechanical engineeringHeat exchangerEnergy (signal processing)MetallurgyChemistry

Abstract

fetched live from OpenAlex

In enhanced oil recovery operations, the steaming capacity of a heat recovery steam generating unit is limited by the available energy provided from the gas turbine generator, however, there are a number of other component parameters that limit the thermal and mechanical performance of any specific heat recovery steam generator. These additional parameters reside on both the flue gas side and water / steam side of the heat recovery steam generator. Repetitive failures of steam generator components are evidence of damage mechanisms that are active during operation. Some of these damage mechanisms are explored to determine the immediate impact in up-rating the steam generators to higher capacity throughput and also, on longer term reliability. A resulting finding of this study examines the efficacy of using P22 low alloy piping in lieu of carbon steel piping to address flow accelerated corrosion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.038
GPT teacher head0.253
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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