MétaCan
Menu
Back to cohort
Record W2961800272 · doi:10.18280/ejee.210212

Exergetic Evaluation and Optimization of Combined Heat and Power (CHP) Plant of 20.7 MW Capacities under Varying Load Conditions: A Case Study

2019· article· en· W2961800272 on OpenAlexvenueno aff
Shrikant Manohar Bapat, Gururaj Gokak

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHeat loadEnvironmental sciencePower stationPower (physics)Waste managementProcess engineeringEngineeringAutomotive engineeringThermodynamicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The main aim of this paper is to find the effect of power to heat ratio on exergy of Combined Heat and power (CHP) systems used in power plants.Lot of Energy Researchers have investigated and published their work in terms of energy efficiency and its parametric characteristics.But from a thermodynamic point of view it is Exergy and not Energy, which reveals a more meaningful performance of CHP system.In the present work a case study of a CHP system of 20.7 MW capacities is considered and analyzed based on varying load conditions as well as based on Exergy, based on experimental data taken from the plant.For 100 % PLF the optimum value of PHR in terms of TExDR and SSC is 0.546.Exergy analysis reveals that with a decrease in the value of PLF (plant load factor) the optimum value of PHR (power to heat ratio) also reduces.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.210
Teacher spread0.202 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Electrical EngineeringSame topicThermodynamic and Exergetic Analyses of Power and Cooling SystemsFrench-language works237,207