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Record W4220980158 · doi:10.18280/ijdne.170113

Effect of Heavy Fuel Combustion in a Gas Power Plant on Turbine Performance: A Review

2022· review· en· W4220980158 on OpenAlexvenueno aff
Ahmed Nawfal Mustafa, Obed M. Ali, Omar Rafae Alomar

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typereview
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCombustionCombined cycleElectricity generationFuel efficiencyGas turbinesElectric powerPower stationTurbineThermal efficiencyAutomotive engineeringEnvironmental scienceFuel gasWaste managementProcess engineeringEngineeringPower (physics)Mechanical engineeringElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

The current review focuses on the utilization of heavy fuel in operating gas turbine and their effect on the power plant performance. The literature survey includes a comparison of the different studies to reveal the effect of the fuel property on the combustion efficiency and fuel consumption. the most important of which is the generation of electric power by heavy fuels in power stations that use turbines. Gas turbine is becoming increasingly widespread in electric power generation and other branches of industry. It is known that the thermal efficiency of an open gas turbine cycle varies according to the type of fuel used in the plant. Gas turbines are particularly suitable for fuels with materials that have physical and chemical properties that help in continuous combustion and therefore the ease inherent in Fuel injection and mixture preparation.

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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
GenreReview

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

Citations6
Published2022
Admission routes1
Has abstractyes

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