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Record W4251045543 · doi:10.5383/ijtee.12.01.006

Performance Analysis of a New Waste Heat Recovery System

2015· article· en· W4251045543 on OpenAlexaffvenue
Sa’ed A. Musmar, Nasim Razavinia, F. Mucciardi, Iskander Tlili

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsWaste heatHeat fluxWaste heat recovery unitHeat pipeHeat recovery ventilationEnergy recoveryWaste managementHeat energyMaterials scienceNuclear engineeringHeat sinkHybrid heatEnvironmental scienceThermodynamicsMechanical engineeringEnergy (signal processing)Heat transferEngineeringHeat exchangerPhysics

Abstract

fetched live from OpenAlex

The overall theme of this research is to capture, concentrate and convert some of the waste heat generated at industrial plants to a valuable form of energy. A new system for heat recovery from low grade energy has been built and tested based on a modified heat pipe technology. A single heat pipe used in this research was able to extract 2 kW of energy from waste heat of 250 oC. However a heat pipe can extract 11.5 kW/m2 heat fluxes. The maximum energy extraction by such system from low grad energy can be up to 3 kW. While a heat pipe regardless of its size can have heat flux up to 16.5 kW/m2 from waste heat flow at 250 oC and 12.3 m/s velocity. Also, the system can extract about 1 kW heat or 6.5 kW/m2 heat flux at temperatures as low as 150 oC. However, the system doesn’t function properly at temperatures lower than 150 oC.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.189
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 designObservational
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

Citations7
Published2015
Admission routes2
Has abstractyes

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