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Record W2953280172 · doi:10.11575/prism/35948

Pre-feasibility Study On A Fuel Cell-based District Energy System In Calgary

2015· article· en· W2953280172 on OpenAlexaboutno aff
Isabella Tarasco

Bibliographic record

VenuePRISM (University of Calgary) · 2015
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsFuel cellsEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Calgary is an urban hub, and its population is on the rise, with a projected growth of 300,000 residents by the year 2018 (City of Calgary, 2014). This increase in population will also contribute to an increase in energy demand and consumers will continue to rely on energy companies to meet their power needs. Power producers are subsequently faced with the dilemma of providing urban establishments with a reliable source of energy while reducing greenhouse gas (GHG) emissions. District energy (DE) combined heat and power (CHP) systems, also known as cogeneration systems, have proved successful around the world (Berta et. al., 2006; Grigor et.al. 2015; Hite, 2009; Verbruggen et.al., 2013), and recently, Calgary has an increasing amount of district energy generation projects being sought out. This research proposes a solution to this problem and ultimately answers the question: Is a fuel cell based district energy system feasible in Calgary’s urban developments? The research looks at Alberta’s energy and natural gas development, technology of fuel cells and district energy systems, along with environmental and socioeconomic issues. The research methods and data collections utilize previous studies. The research presents many limitations and assumptions, however ultimately leads to a recommendation for further research into this initiative followed by government incentives and subsidies to help lessen the financial burden that a fuel cell based DE system poses.

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: none
Teacher disagreement score0.529
Threshold uncertainty score0.956

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 head0.011
GPT teacher head0.183
Teacher spread0.172 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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