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Record W2626139353 · doi:10.5383/ijtee.12.02.007

Solar Powered Electrolysis System for Cooking Applications

2015· article· en· W2626139353 on OpenAlexvenueno aff
Evangelia Topriska, Maria Kolokotroni, Zahir Dehouche, Earle Wilson

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
FundersEuropean Commission
KeywordsTRNSYSProton exchange membrane fuel cellPhotovoltaic systemEnvironmental scienceFortranHydrogen productionSoftwareProcess engineeringComputer scienceAutomotive engineeringSimulationHydrogenEngineeringFuel cellsMeteorologyElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

This paper presents a numerical model for a solar powered Proton Exchange Membrane Electrolyser (PEM) for the generation of hydrogen as fuel for cooking applications. The model was developed in TRNSYS software and includes a novel FORTRAN numerical component to model the operation of the PEM electrolyser, based on operational data from a small-scale experimental rig. The numerical model was calibrated using data from the experimental rig powered by operational data from a photovoltaic panel system in the UK and from Jamaica. The calibrated model was used to develop a case study for a small community in Jamaica with a daily cooking demand of 39.6 kWh or 1.68 kg of H2 gas. Simulation results indicate that the H2 production strategy is satisfactory for the cooking needs of the case-study and appropriate according to the environmental considerations related to cooking

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.186
Teacher spread0.180 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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