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Record W1977244949 · doi:10.1115/gt2005-68121

Bottoming Organic Cycle for Gas Turbines

2005· article· en· W1977244949 on OpenAlexaboutno aff
Lucien Y. Bronicki, Daniel N. Schochet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsGas compressorOrganic Rankine cycleRankine cycleDegree RankineCombined cycleGas turbinesEngineeringPipeline transportProcess engineeringCompressor stationWaste managementEnvironmental sciencePetroleum engineeringWaste heatMechanical engineeringHeat exchangerPower (physics)

Abstract

fetched live from OpenAlex

Organic Rankine Cycle (ORC) systems are not new; prototypes have been tested for about a century. The theoretical investigation and practical applications in the past are briefly presented and referenced. This paper presents the applications of this technology, which matured mainly in geothermal applications, to the recovery of exhaust heat of simple cycle gas turbines driving compressors on gas pipelines and gas processing plant. Most of the compressor stations have a capacity below 50 MW and operate basically unattended. The complexity and the necessity of an operator prevents the use of bottoming steam systems (combined cycle) on this size of plant. In these applications, which are mainly retrofits, the ease of operation of the ORC made its use possible where steam turbines were unsuccessful. Two applications are described: the Enterprise Products’ Neptune plant in Louisiana, and the Gold Creek gas compressor station in Alberta, Canada.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 designSimulation or modeling
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

Citations13
Published2005
Admission routes1
Has abstractyes

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