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Record W4241355480 · doi:10.5383/ijtee.12.01.007

Waste Heat Recovery from Gas Turbine Flue Gases for Power Generation Enhancement in a Process Plant

2015· article· en· W4241355480 on OpenAlexvenueno aff
Valérie Eveloy, Peter Rodgers, Adesola Oluwasijibomi Olufade, Yuyao Wang, Ali Al Alili

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsOrganic Rankine cycleFlue gasWaste managementWaste heatRankine cycleWaste heat recovery unitCombined cycleExergyEnvironmental scienceHeat recovery ventilationNatural gasExergy efficiencyThermal efficiencyThermal power stationElectricity generationDegree RankineEngineeringProcess engineeringTurbineHeat exchangerPower (physics)Mechanical engineeringChemistryThermodynamics

Abstract

fetched live from OpenAlex

To improve on-site power generation capacity and efficiency in process facilities, the thermal coupling of an industrial gas turbine cycle with a bottoming organic Rankine cycle for power plant flue gas waste heat recovery in a process facility is investigated. Using 1,1,1,3,3-pentafluoropropane (R245fa) as heat carrier in the Rankine cycle, 5.2 MW of additional electric power is generated, enhancing on-site power generation capacity and energy/exergy efficiency by approximately 23% and 6%, respectively. The overall energy and exergy efficiencies of the waste heat recovery system are estimated at 9% and 24%, respectively. Primary energy savings of approximately 1.3 million standard cubic feet per day (MMSCFD) of natural gas, or net annual operating expenditure savings of 1.6 million USD, could be realized with the proposed flue gas waste heat recovery system based on subsidized industrial electricity tariffs in the UAE, with 457 tons of avoided CO2 emissions per year.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.377

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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.

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

Citations5
Published2015
Admission routes1
Has abstractyes

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