MétaCan
Menu
Back to cohort
Record W4310908189 · doi:10.18280/jesa.550506

Thermal Design Developing for Steam Power Plants by Using Concentrating Solar Power (CSP) Technologies

2022· article· en· W4310908189 on OpenAlexvenueno aff
Fawaz Sultan Abdullah, Rasha A. Mohammed, Farah I. Hameed

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2022
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsThermal power stationConcentrated solar powerProcess engineeringThermalSteam turbineWork (physics)Solar energyEnvironmental scienceThermal efficiencyThermal energyExploitSteam-electric power stationPower (physics)Environmental pollutionTurbineWaste managementMechanical engineeringComputer scienceEngineeringChemistryCombined cycleElectrical engineeringMeteorologyThermodynamics

Abstract

fetched live from OpenAlex

The main purpose of this study is to discuss the possibility of the development of thermal power plants to produce electric power with conventional steam to work as a semi-joint system to exploit an array of solar collectors concentrated type parabolic cylindrical in processing the amount of thermal energy for steam turbine units. The effectiveness of various designs for linking Matrix complexes' solar concentration in the thermal design of the plant steam has been studied in this investigation. As a result of the use of matrix compounds, the CSP study showed the economic efficiency and environmental design of the proposed terms of the amount of savings the lowest in the amount of fuel consumed in the network. The findings show that the decrease in the amount of thermal energy and the amount of environmental pollution from carbon dioxide and nitrogen oxides which are produced in the surrounding medium.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.259
Teacher spread0.222 · 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 designBench or experimental
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 routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicSolar Thermal and Photovoltaic SystemsFrench-language works237,207