Harnessing sediment voids of low-grade salt mines for compressed air energy storage: Experimental and theoretical insights
Bibliographic record
Abstract
Renewable energy storage technologies are critical for transitioning to sustainable energy systems, with salt caverns playing a significant role in large-scale solutions. In water-soluble mining of low-grade salt formations, insoluble impurities and interlayers detach during salt dissolution and accumulate as sediment at the cavern base, thereby reducing the storage capacity and economic viability of salt cavern gas storage (SCGS). This study investigates sediment formation mechanisms, void distribution, and voidage in the Huai’an low-grade salt mine, introducing a novel self-developed physical simulation device for two butted-well horizontal (TWH) caverns that replicates compressed air injection and brine discharge. Experiments comparing “one injection and one discharge” and “two injections and one discharge” modes revealed that (1) compressed air effectively displaces brine from sediment voids, (2) a 0.5 MPa injection pressure corresponds to a 10.3 MPa operational lower limit in practice, aligning with field data, and (3) sediment voidage is approximately 46%, validated via air-brine interface theory. The “two injections and one discharge” mode outperformed in both discharge volume and rate. Additionally, a mathematical model for brine displacement via compressed air was established. These results provide foundational insights for optimizing compressed air energy storage (CAES) in low-grade salt mines, advancing their role in renewable energy integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".