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Record W4255858143 · doi:10.32920/ryerson.14663511.v1

Feasibility of Residential Combined Cooling, Heating, and Power Generation System in Canada

2021· preprint· en· W4255858143 on OpenAlexaboutno aff
Navid Ekrami

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCogenerationStirling engineChillerTRNSYSAccumulator (cryptography)Absorption refrigeratorEngineeringCoefficient of performanceWaste heat recovery unitWaste managementWaste heatEnvironmental scienceChiller boiler systemElectricity generationNuclear engineeringAutomotive engineeringHeat exchangerHeat pumpMechanical engineeringWater chillerThermalPower (physics)MeteorologyRefrigerationThermodynamicsComputer science

Abstract

fetched live from OpenAlex

In order to investigate the feasibility of a combined heating, cooling, and power generation system in the residential sector, an integrated system was designed and installed at the Archetype Sustainable House (ASH) of the Toronto and Region Conservation Authority (TRCA). A Stirling engine based cogeneration unit was used to produce the thermal energy for a thermally driven chiller. The engine supplies hot water up to 95°C. The overall efficiency of up to 90% is determined for the cogeneration system. A thermo-chemical accumulator provided by the ClimateWell AB, was installed and connected to the cogeneration unit. The experimental coefficient of performance (COP) of this chiller during the test period was less than 0.4. Since the ClimateWell chiller rejects heat during both charging and discharging processes, a heat recovery system using three cascade tanks and an outdoor fan coil was designed and installed to utilize the waste heat, for domestic hot water production. A complete TRNSYS model of the tri-generation system was used to verify the experimental results.

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)0.001
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: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.219
Teacher spread0.208 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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