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Record W3071975349 · doi:10.29019/enfoqueute.v11n3.653

Análisis termoenergético del sistema de generación de vapor de una central térmica de 49 MW

2020· article· es· W3071975349 on OpenAlexaff
Yoalbys Retirado–Mediaceja, Héctor Luis Laurencio Alfonso, Andres Adrian Sánchez Escalona, Yanán Camaraza-Medina, Marcelo Fabián Salazar Corrales, Marbelis Lamorú Urgelles, Ever Góngora-Leyva

Bibliographic record

VenueEnfoque UTE · 2020
Typearticle
Languagees
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsNickel Institute
Fundersnot available
KeywordsHumanitiesPhysicsCalderaGeographyGeologyPhilosophy

Abstract

fetched live from OpenAlex

En el trabajo se realiza un análisis termoenergético integral del sistema de generación de vapor de una termoeléctrica de 49 MW, lo cual no ha sido estudiado con rigor desde el punto de vista energético. En un algoritmo se sintetiza la metodología para el cálculo de los rendimientos térmicos brutos y exergéticos de las calderas que lo integran, aspectos que no se han interrelacionado en estudios precedentes. Los resultados evidencian un elevado grado de aprovechamiento de la energía térmica y una baja capacidad de utilización de la exergía disponible en las instalaciones, provocados por el deterioro de algunos de sus parámetros de operación y por irreversibilidades inherentes al proceso de transformación del agua en vapor. Los rendimientos, térmico y exergético del sistema, ascendieron a 90.106 y 45.491 %, respectivamente. El algoritmo propuesto prevé el cálculo y la comparación de los parámetros termoenergéticos reales de las calderas con los nominales y las acciones científicas, y técnico-organizativas, a desarrollar para lograr rendimientos termoexergéticos superiores. Su implementación futura tendrá en cuenta el análisis de los rendimientos térmicos netos, los indicadores económico-ambientales y la optimización energético-operacional de las calderas.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.231
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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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Same venueEnfoque UTESame topicThermodynamic and Exergetic Analyses of Power and Cooling SystemsFrench-language works237,207