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Record W4409903825 · doi:10.3390/en18092258

Compressed Air Energy Storage in Salt Caverns Optimization in Southern Ontario, Canada

2025· article· en· W4409903825 on OpenAlexafffundabout
Jingyu Huang, Shunde Yin

Bibliographic record

VenueEnergies · 2025
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Waterloo
FundersMitacs
KeywordsCompressed air energy storageCompressed airEnergy storageSalt (chemistry)Environmental scienceEnergy (signal processing)Waste managementGeologyEngineeringChemistryMechanical engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Energy storage systems are gaining increasing attention as a solution to the inherent intermittency of renewable energy sources such as solar and wind power. Among large-scale energy storage technologies, compressed air energy storage (CAES) stands out for its natural sealing properties and cost-efficiency. Having abundant salt resources, the thick and regionally extensive salt deposits in Unit B of Southern Ontario, Canada, demonstrate significant potential for CAES development. In this study, optimization for essential CAES salt cavern parameters are conducted using geological data from Unit B salt deposit. Cylinder-shaped and ellipsoid-shaped caverns with varying diameters are first simulated to determine the optimal geometry. To optimize the best operating pressure range, stationary simulations are first conducted, followed by tightness evaluation and long-term stability simulation that assess plastic and creep deformation. The results indicate that a cylinder-shaped cavern with a diameter 1.5 times its height provides the best balance between storage capacity and structural stability. While ellipsoid shape reduces stress concentration significantly, it also leads to increased deformation in the shale interlayers, making them more susceptible to failure. Additionally, the findings suggest that the optimal operating pressure lies between 0.4 and 0.7 times the vertical stress, maintaining large capacity and minor gas leakage, and developing the least creep deformation.

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 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)0.000
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 score0.122
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.003
GPT teacher head0.162
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 teacher head, 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
Published2025
Admission routes3
Has abstractyes

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