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Record W2295674263

Operation and Monitoring of Parabolic Trough Concentrated Solar Power Plant

2015· article· en· W2295674263 on OpenAlexfundno aff
Harsha Vardhan Amba

Bibliographic record

VenueDigital Commons - University of South Florida (University of South Florida) · 2015
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsParabolic troughPower stationProcess engineeringSolar energyConcentrated solar powerSolar powerFossil fuelPhotovoltaic systemElectricity generationEnvironmental scienceRenewable energyEngineeringSolar ResourceGrid parityWind powerDistributed generationPower (physics)Waste managementElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The majority of the power generated today is produced using fossil fuels,emitting carbon dioxide and other pollutants every second. Also, fossil fuels will eventually run out. For the increasing worldwide energy demand, the use f reliable and environmentally beneficial natural energy sources is one of the biggest challenges. Alongside wind and water, the solar energy which is clean, CO2-neutral and limitless, is our most valuable resource.\nConcentrated solar power (CSP) is becoming one of the excellent alternative sources for the power industry. The successful implementation of this technology requires the efficient design of tracking and operation system of the CSP solar plants. A detailed analysis of components needed for the design of cost-effective and optimum tracker for CSP solar systems is required for the power plant modeling, which is the primary subject of this thesis. A comprehensive tracking and operating system of a parabolic trough solar power plant was developed focusing primarily on obtaining optimum and cost effective design through the simplified methodology of this work. This new model was implemented for a 50 kWe parabolic trough solar power plant at University of South Florida, Tampa.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.025
GPT teacher head0.196
Teacher spread0.171 · 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 designQualitative
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
Published2015
Admission routes1
Has abstractyes

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