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Record W2157418651 · doi:10.1115/1.4026637

Investigation of a Hybrid Photovoltaic-Biomass System With Energy Storage Options

2014· article· en· W2157418651 on OpenAlexaff
Mehdi Hosseini, İbrahim Dinçer, Marc A. Rosen

Bibliographic record

VenueJournal of Solar Energy Engineering · 2014
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyPhotovoltaic systemEnvironmental scienceBiomass (ecology)Exergy efficiencyEnergy storageRenewable energyWood gas generatorHybrid systemWaste managementProcess engineeringPower (physics)EngineeringThermodynamicsElectrical engineeringComputer sciencePhysicsEcology

Abstract

fetched live from OpenAlex

A hybrid photovoltaic (PV)-biomass system with energy storage options is investigated based on energy and exergy analyses. The hybrid system consists of a photovoltaic system, an electrolyser, and a biomass gasifier, which is integrated with a biomass-based gas turbine. The PV system is accountable for 56% of the annual exergy destruction in the hybrid system, while 38% of the annual exergy destruction occurs in the biomass-gas turbine (GT) system. The overall energy and exergy efficiencies of the hybrid PV-biomass system with energy storage options are 34.8% and 34.1%, respectively. A 29% increase in both energy and exergy efficiencies is reported with an increase in the steam-to-carbon ratio (SC) in the range of 1–3 mol/mol. The related specific carbon dioxide emission reduction is 1441–583 g/kWh. In contrast to SC, an increase in gas turbine inlet temperature results in a negative effect on the overall energy and exergy efficiencies, and it does not make a significant contribution to the reduction in specific carbon dioxide emission.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.166
Teacher spread0.159 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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