Optimizing Lime-Based Cemented Paste Backfill for Potash Mines: A Comprehensive Loop Test Study on Slurry Transportation Characteristics
Bibliographic record
Abstract
Utilizing potassium salt aggregates and waste brine to produce underground cemented filling materials can address the waste storage issue. However, it is essential for the backfill materials to meet specific transport characteristics. This paper examines the transportation characteristics of lime-cemented mine backfill for a potash mine. The parameters were optimized for the cemented backfill process of potash mines through loop experiments and model simulations. Results indicate that the slump and fluidity of the backfill slurry diminished with increasing lime content and solid concentration. Additionally, the growth rate of pressure loss at the bent pipe and the pressure loss per unit distance in a horizontal pipe increased rapidly over transportation time, indicating a decline in the flowability of the backfill slurry. The lime dosage and solid concentration must align with the backfill requirements. When the lime dosage is 0.5%, the solid content is 70–75%; conversely, with a lime dosage of 0.7% and solid content of 65%, the maximum pumpable time extends to 1 h. The compressive strength of the cured backfill material after 28 days exceeds 1.01 MPa, meeting the transportation requirements for 300 m vertical pipes and 5000 m horizontal pipes. In the case study, the actual flow rate of backfill slurry surpasses the calculated critical flow rate. The estimated and measured values of on-site pressure loss per unit distance in a horizontal pipe exhibit a strong correlation. As the pressure loss per unit distance in a horizontal pipe rises, the discrepancy between the calculated and measured values also increases. When the solid content exceeds 65%, the loop test slightly enhances the compressive strength of the lime-cemented backfill. The findings from this article can aid in determining the on-site backfill process parameters with lime as a binder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".