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Record W4221083836 · doi:10.1002/ese3.1114

Development and assessment of renewable hydrogen production and natural gas blending systems for use in different locations

2022· article· en· W4221083836 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

VenueEnergy Science & Engineering · 2022
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNatural gasEnvironmental scienceRenewable energyWaste managementPhotovoltaic systemCapital costFossil fuelHydrogen productionEnvironmental engineeringProcess engineeringHydrogenEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract In this study, a thermoeconomic assessment of renewable energy‐based hydrogen generation and blending it with natural gas in the existing pipeline system is performed for various locations. Selected locations are compared in energy content, environmental impact, and cost. In this regard, solar photovoltaic panels and wind turbines are integrated with electrolyzers along with the reverse osmosis units. The clean hydrogen produced by the electrolyzers is then blended with natural gas and utilized for residential applications in an environmentally benign way. Also, the heat required for a community consisting of 100 houses is provided by a boiler by hydrogen and natural gas blend as fuel. The costs of capital, fuel, operation and maintenance are calculated and comparatively evaluated. The results show that the total net present costs for the integrated systems are calculated to be between $3.01 million and $4.36 million in the selected five different locations. Furthermore, an environmental impact assessment is conducted in terms of carbon monoxide, carbon dioxide, nitrogen oxides, unburned hydrocarbons, and particulate matter. Finally, the CO 2 emissions are calculated to be varying from 443.2 to 491.9 tons/year.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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