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Record W7062515388

Utilization of natural gas, optimization of cogeneration/combined cycle applications in campus environment

2019· dissertation· en· W7062515388 on OpenAlexfundno aff

Bibliographic record

VenueOpenMETU (Middle East Technical University) · 2019
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersUniversity of TorontoUniversity of OregonUniversity of MissouriUniversity of Northern IowaUniversity of LethbridgeUniversity of WashingtonUniversity of OklahomaKwangwoon UniversityUniversity of Northern ColoradoUniversity of North Carolina at Chapel HillUniversity of South FloridaUniversity of Notre DameVanderbilt University
KeywordsCogenerationNatural gasCombined cyclePower stationGas turbinesElectricitySteam turbineElectricity generationThermal power stationPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

A computer program, called أCogeneration Design" is developed using Visual Basic 6.0, for conceptually designing cogeneration power plants. Design is focused on power plants to be built in university campuses, where there is mainly heating, hot water, electricity and sometimes cooling demands. Middle East Technical University campus is considered as the primary working area. Before the conceptual design study, detailed information regarding description of the campus, infrastructure, annual electric, water and heat demand covering last 10 years, properties of existing heat plant including natural gas expenses and specifications of the steam distribution pipes and electricity grid are collected and examined in detail. Throughout the thesis, eight different natural gas fired cogeneration power plant designs are developed regarding different gas turbine and steam turbine configurations, for METU Campus, considering the Campus' properties described above, by using the "Cogeneration Design" program. Then, by means of a thermoeconomic optimization process, cost summary reports are prepared and the feasibility of the designed cogeneration power plants are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.764

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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
Published2019
Admission routes1
Has abstractyes

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