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Record W4244159896 · doi:10.32920/ryerson.14654391.v1

Energy modelling and field testing of natural gas absorption heat pump system in cold climate - Canada

2021· preprint· en· W4244159896 on OpenAlexafffundabout
Kajen Ethirveerasingham

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTRNSYSNatural gasEnvironmental scienceNuclear engineeringGreenhouse gasHeat pumpDeratingLiquefied natural gasWaste managementMaterials scienceEnvironmental engineeringEngineeringProcess engineeringMeteorologyMechanical engineeringThermalElectrical engineeringPhysicsEcology

Abstract

fetched live from OpenAlex

The feasibility of a Natural Gas Absorption Heat Pump (GAHP) was investigated through the use of a technology screening tool developed in Excel and TRNSYS simulations that used experimentally evaluated performance curves using 50% propylene glycol (PG)/water solution. The Excel tool was used for cost and greenhouse gas (GHG) reductions analysis and indicated that the GAHP was cost effective compared to an Air-Source Heat Pump (ASHP) in locations where the primary heating fuel was natural gas and had significantly lower GHG emissions compared to a 90% efficient natural gas furnace. During the heating season, it was found that the system only had more than 100% heating Gas Utilization Efficiency (GUE) at -1.5°C or above and would go as low at 66% GUE at nominal flow and return temperatures. Part load performance was analyzed using experimental data and implemented in a TRNSYS model. The TRNSYS model shows significant losses from part load performance and 50% PG/water derating for both heating and cooling season due to excessive cycling.

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 score0.057
Threshold uncertainty score0.841

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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

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