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Record W6921808723 · doi:10.11575/prism/49412

Waste Heat to Electricity: Techno-Economic Feasibility of the Organic Rankine Cycle at UCalgary’s Central Heating Plant

2024· other· en· W6921808723 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2024
Typeother
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsOrganic Rankine cycleCogenerationPayback periodRankine cycleWaste heatNet present valueTonneElectricityPower stationWaste heat recovery unit

Abstract

fetched live from OpenAlex

This research proposal aims to assess the technical and economic feasibility of implementing an Organic Rankine Cycle (ORC) generator to capture excess thermal energy during summer months at the University of Calgary (UCalgary). The primary focus is on integrating ORC technology into the existing Combined Heat and Power Plant infrastructure to enhance year-round efficiency. By utilizing surplus heat from the cogeneration unit, the project seeks to optimize energy utilization, minimize carbon emissions, and support the University of Calgary's sustainability objectives. This initial feasibility assessment shows that the project would produce an additional 2800 MWh of electricity per year and reduce annual Scope II carbon emissions by 1,320 tonnes of CO2e. Additionally, the financial analysis shows a simple payback period of 8-10 years, with a Net Present Value between 1 million and 2.7 million dollars for a 20-year project life.

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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.238
Teacher spread0.227 · 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
Published2024
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicThermodynamic and Exergetic Analyses of Power and Cooling SystemsFrench-language works237,207