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Record W4285009567 · doi:10.22215/etd/2022-14999

Assessment of Zero Carbon Hydrogen/Ammonia Based Energy Systems for Northern and Remote Communities in Canada

2022· dissertation· en· W4285009567 on OpenAlexaffabout
Chris Rawling

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsCarleton University
Fundersnot available
KeywordsRenewable energyCost of electricity by sourceMicrogridWind powerEnvironmental scienceEnergy carrierGrid energy storageElectricityHydrogenAmmoniaEnvironmental engineeringEnvironmental economicsElectricity generationDistributed generationEngineeringChemistryElectrical engineeringPhysicsEconomicsPower (physics)

Abstract

fetched live from OpenAlex

A techno-economic study is conducted to calculate the levelized cost of energy for a renewable energy driven zero-carbon energy system that utilizes hydrogen as the primary energy carrier and ammonia as a hydrogen carrier for seasonal storage.The case study is for the off-grid Northern & Remote Community of Rankin Inlet located in the Canadian territory of Nunavut.The study is novel as it includes all energy sectors: heating, electricity, and transportation, with a comparison of two different configurations -one with hydrogen/ammonia production on-site, and the second with imported ammonia, both driven by wind energy.The lowest levelized cost of energy is obtained where ammonia is imported and where there is a high penetration of wind energy in the community microgrid.The results demonstrate that such a zero-carbon energy system is economically feasible and is a viable option for those off-grid Communities that benefit from a high wind energy potential.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.224
Teacher spread0.214 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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