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Record W4401632053 · doi:10.22215/etd/2024-16036

Technoeconomic Analysis of a Solar Thermal Driven Adsorption Chiller Space Conditioning System for Canadian Multi-Unit Residential Buildings

2024· dissertation· en· W4401632053 on OpenAlexafffundabout
John Ward

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGreenhouse gasFossil fuelEnvironmental scienceSolar energyEnvironmental engineeringUnit (ring theory)EngineeringAir conditioningWaste managementSolar air conditioningCivil engineeringProcess engineeringArchitectural engineeringAutomotive engineeringMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Building energy demands and associated greenhouse gas emissions are increasing in Canada. A potential pathway to counteract this trend involves replacing conventional space conditioning technologies with more sustainable solar based technologies. This study evaluates the technoeconomic and environmental performance of a solar-driven adsorption-based system used to meet the space conditioning and domestic hot water demands of a low-energy multi-unit residential building in Canada. A transient numerical model of the building and proposed system is developed. Results from annual simulations in 12 Canadian locations show that the proposed system can achieve solar fractions greater than 0.8 and can reduce annual greenhouse gas emissions by up to 62.7% and 99.8% relative to heat pump and natural gas reference systems, respectively. While the levelized cost of energy for the proposed system is high, a compelling economic argument can be made in jurisdictions with fossil fuel dependent electrical grids.

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 categoriesMeta-epidemiology (narrow)
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 score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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.

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
Published2024
Admission routes3
Has abstractyes

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