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Record W2399100496 · doi:10.1115/1.4033625

Assessment and Evolutionary Based Multi-Objective Optimization of a Novel Renewable-Based Polygeneration Energy System

2016· article· en· W2399100496 on OpenAlex
Rami S. El‐Emam, İbrahim Dinçer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Energy Resources Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyRenewable energyProcess engineeringSolid oxide fuel cellEnvironmental scienceBiomass (ecology)ElectricityStack (abstract data type)Thermal energySolar energyAutomotive engineeringEngineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, a renewable-based integrated energy system is developed, analyzed, and optimized to achieve better performance. The present system is designed to be driven by concentrated solar thermal and biomass energies. Biomass fuel is used as the backup source of energy when the solar energy is not available. The system is designed to produce electricity, cooling, and hydrogen. The power output of the system is provided by solar-driven regenerative helium gas turbine during day time and from biomass gasification driven solid oxide fuel cell (SOFC) unit at night time. The fuel cell stack number is estimated as to provide the same net power. The system operates at energy and exergy efficiencies of 39.99% and 27.47%, respectively, at the optimal point selected based on the optimization analysis. The parametric studies on performance and environmental impact assessment are performed to investigate the effects of several operating parameters on the system performance.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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