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Record W4281694721 · doi:10.5539/eer.v12n1p26

A Novel Integrated Design for Heat and Water Recovery from Exhaust Flue Gas of Bandar Abbas Power Plant

2022· article· en· W4281694721 on OpenAlexvenueno aff
Erfan Ghamati, Javad Mirrezaie Roudaki

Bibliographic record

VenueEnergy and Environment Research · 2022
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsFlue gasDew pointEnvironmental scienceWater vaporBoiler (water heating)Latent heatHeat exchangerCombined cyclePower stationThermal power stationVapor-compression evaporationHeat recovery ventilationSensible heatDewWaste managementCondensationMaterials scienceThermodynamicsChemistryPower (physics)EngineeringPhysics

Abstract

fetched live from OpenAlex

This study concerns a theoretical design of a condensing heat exchanger for a 320 MW unit of Bandar Abbas thermal power plant in the south of Iran. A film theory in conjunction with heat and mass transfer analogy is used as the theoretical basis of the design. The condensing unit is used for heat and mass recovery from the natural gas-fired boiler flue gases. The assumed condensing unit includes 4 equal capacity condensing heat exchangers, each of which is supposed to reduce the flue gas temperature from 160 ℃ to 53℃. Decreasing the flue gas temperature to below the dew point temperature of its water vapor causes condensation (latent) and sensible heat transfer. The analysis was done for 13%, 15%, and 17% of the water vapor volume fraction in the flue gases, and based on the 17% water vapor fraction, 52.8 tons/hr of water was recovered. This recovered water could be used as the cooling tower makeup, and accordingly, almost 14% of water consumption is saved. The recovered heat by the condensing unit is also being used as the heat source of an ORC cycle, and up to 2.8 MW power is estimated to be generated depending on the evaporation temperature.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.476

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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