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Record W4383736258 · doi:10.3390/mining3030024

Potential of Salt Caverns for Hydrogen Storage in Southern Ontario, Canada

2023· article· en· W4383736258 on OpenAlexaffabout
Shasha Hui, Shunde Yin, Xiongqi Pang, Zhuoheng Chen, Kanyuan Shi

Bibliographic record

VenueMining · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsGeological Survey of CanadaUniversity of Waterloo
Fundersnot available
KeywordsHydrogen storageRenewable energyEnvironmental scienceHydrogenEnergy storageSalt (chemistry)Waste managementChemistryEngineeringPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

Salt caverns produced by solution mining in Southern Ontario provide ideal spaces for gas storage due to their low permeability. Underground hydrogen storage (UHS) is an important part of the future renewable energy market in Ontario in order to achieve global carbon neutrality and to fill the gap left by retiring nuclear power plants. However, large-scale hydrogen storage is still restricted by limited storage space on the ground’s surface. In this study, hydrogen’s physical and chemical properties are first introduced and characterized by low molecular weight, high diffusivity, low solubility, and low density. Then, the geological conditions of the underground reservoirs are analyzed, especially salt caverns. Salt caverns, with their inert cavity environments and stable physical properties, offer the most promising options for future hydrogen storage. The scales, heights, and thicknesses of the roof and floor salt layers and the internal temperatures and pressures conditions of salt caverns can affect stabilities and storage capacities. Finally, several potential problems that may affect the safe storage of hydrogen in salt caverns are discussed. Through the comprehensive analysis of the influencing factors of hydrogen storage in salt caverns, this study puts forward the most appropriate development strategy for salt caverns, which provides theoretical guidance for UHS in the future and helps to reduce the risk of large-scale storage design.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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 designObservational
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

Citations21
Published2023
Admission routes2
Has abstractyes

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