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Record W2946448591 · doi:10.1115/1.4043748

Exergetic Performance Investigation of Varying Flashing From Single to Quadruple for Geothermal Power Plants

2019· article· en· W2946448591 on OpenAlexaff
Osamah Siddiqui, İbrahim Dinçer

Bibliographic record

VenueJournal of Energy Resources Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsFlashingGeothermal gradientExergyRenewable energyGeothermal powerGeothermal energyEnvironmental scienceSteam turbinePower (physics)Exergy efficiencyFlash (photography)TurbineNuclear engineeringProcess engineeringWaste managementEngineeringMaterials scienceThermodynamicsMechanical engineeringElectrical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Geothermal power plants are considered important renewable energy resources for clean energy production. Flash steam type plants constitute a significant portion of worldwide geothermal power. In this study, single, double, triple, and quadruple flash steam geothermal power plants are investigated with reinjection options. The optimal operating points are determined specifically through optimal flashing pressures. The turbine power outputs, energy efficiencies, and exergy efficiencies are further studied. A rise in the flashing stages from single to double is found to increase the power outputs considerably. However, when the flashing stages are increased from double to triple and triple to quadruple, the increase in turbine power outputs is found to drop significantly. Also, both exergy efficiency and energy efficiency are found to reduce with increasing number of flash stages. The energy efficiencies are obtained as 28%, 25.5%, 24.2%, and 23.5% for single, double, triple, and quadruple plants, respectively. Furthermore, the exergy efficiencies are found to be 72.6%, 70.9%, 70.2%, and 69.8% for these plants, respectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.472

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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 designBench or experimental
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

Citations9
Published2019
Admission routes1
Has abstractyes

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