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Record W4220748025 · doi:10.1080/15435075.2022.2043869

Development of a geothermal plant combined with absorption and Rankine cycles for trigeneration

2022· article· en· W4220748025 on OpenAlexaff
Ali M.M.I. Qureshy, İbrahim Dinçer

Bibliographic record

VenueInternational Journal of Green Energy · 2022
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsEvaporatorAbsorption refrigeratorExergyOrganic Rankine cycleCoefficient of performanceDegree RankineGeothermal gradientGeothermal energyEnvironmental scienceRankine cycleExergy efficiencyParabolic troughRenewable energyNuclear engineeringRefrigerationWaste heatWaste managementThermodynamicsProcess engineeringHeat pumpEngineeringThermalMechanical engineeringPower (physics)Heat exchangerElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

A thermodynamic modeling through energy and exergy approaches is performed on a newly developed system for hot water, cooling and electricity production. The geothermal power plant supplies thermal energy to the Rankine cycle to produce hot water and green electricity. The absorption refrigeration system is integrated with the geothermal plant for hot water and cooling. Various parametric studies are conducted to present the exergetic and energetic losses of each subcomponent. The results of the parametric studies indicate that the maximum useful output power of the turbine is obtained as 5991 kW at a geothermal injection temperature of 460 K. Moreover, the highest heat cooling rate of the evaporator is 4397 kW obtained at the inlet mass flowrate to the pump of 30 kg/s. The highest energetic and exergetic coefficient of performance values at inlet temperature to pump of 30°C are 0.584 and 0.287, respectively. Furthermore, the maximum energy and exergy coefficient of performance values at −5°C of the outlet temperature of the evaporator are found to be 0.58 and 0.231, 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.187

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 head0.008
GPT teacher head0.204
Teacher spread0.197 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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