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Record W4311089321 · doi:10.1016/j.ijft.2022.100262

Energy assessment of an integrated hydrogen production system

2022· article· en· W4311089321 on OpenAlex

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

VenueInternational Journal of Thermofluids · 2022
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHeliostatHydrogen productionParabolic troughRenewable energySolar energyHigh-temperature electrolysisThermochemical cycleProcess engineeringPhotovoltaic systemNuclear engineeringThermal energyHydrogenEnvironmental scienceRankine cycleHydrogen fuelElectrolysisChemistryEngineeringElectrical engineeringThermodynamicsPower (physics)Physics

Abstract

fetched live from OpenAlex

Hydrogen is believed to be the future energy carrier that will reduce environmental pollution and solve the current energy crisis, especially when produced from a renewable energy source. Solar energy is a renewable source that has been commonly utilized in the production process of hydrogen for years because it is inexhaustible, clean, and free. Generally, hydrogen is produced by means of a water splitting process, mainly electrolysis, which requires energy input provided by harvesting solar energy. The proposed model integrates the solar harvesting system into a conventional Rankine cycle, producing electrical and thermal power used in domestic applications, and hydrogen by high temperature electrolysis (HTE) using a solid oxide steam electrolyzer (SOSE). The model is divided into three subsystems: the solar collector(s), the steam cycle, and an electrolysis subsystem, where the performance of each subsystem and their effect on the overall efficiency is evaluated thermodynamically using first and second laws. A parametric study investigating the hydrogen production rate upon varying system operating conditions (e.g. solar flux and area of solar collector) is conducted on both parabolic troughs and heliostat fields as potential solar energy harvesters. Results have shown that, heliostat-based systems were able to attain optimum performance with an overall thermal efficiency of 27% and a hydrogen production rate of 0.411 kg/s, whereas, parabolic trough-based systems attained an overall thermal efficiency of 25.35% and produced 0.332 kg/s of hydrogen.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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