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Record W2078205452 · doi:10.3390/su3112115

Toward a Heat Recovery Chimney

2011· article· en· W2078205452 on OpenAlexaff
Dan Zhang, Yuelei Yang, Min Pan, Zhen Gao

Bibliographic record

VenueSustainability · 2011
Typearticle
Languageen
FieldEngineering
TopicSolar Energy Systems and Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsWaste heatElectricityHeat exchangerCondenser (optics)Environmental scienceWaste heat recovery unitHeat recovery ventilationCogenerationRenewable energyChimney (locomotive)Waste managementAirflowElectricity generationEnergy recoveryEngineeringEnvironmental engineeringMechanical engineeringInletEnergy (signal processing)

Abstract

fetched live from OpenAlex

The worldwide population increase and subsequent surge in energy demand leads electricity producers to increase supply in an attempt to generate larger profit margins. However, with Global Climate Change becoming a greater focus in engineering, it is critical for energy to be converted in as environmentally benign a way as possible. There are different sustainable methods to meet the energy demand. However, the focus of this research is in the area of Waste Heat Recovery. The waste heat stored in the exiting condenser cooling water is delivered to the air flow through a water-air cross flow heat exchanger. A converging thermal chimney structure is then applied to increase the velocity of the airflow. The accelerated air can be used to turn on the turbine-generator installed on the top the thermal chimney so that electricity can be generated. This system is effective in generating electricity from otherwise wasted heat.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0190.006

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 head0.019
GPT teacher head0.198
Teacher spread0.180 · 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 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

Citations9
Published2011
Admission routes1
Has abstractyes

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