Research on the fluid characteristics of cemented backfill pipeline transportation of mineral processing tailings
Bibliographic record
Abstract
With the deepening of underground resource exploitation, the application of cemented backfill pipeline transportation of mineral tailings has become the best option to reduce the risk of deep ground pressure and solid waste pollution. Research in this field is mainly centred on slurry fluidity experiments. However, the slurry transportation parameters, particle characteristics and complexity of the pipeline all cause uncertainty in the calculation of backfill pipeline transportation parameters. The conventional backfill loop test is expensive. Combining structural fluid tests with particle flow models, this paper presents a method to optimize backfill pipeline transportation parameters. The H-B model is employed to analyse the transportation resistance of backfill slurry along the line to establish the relation function between the resisting force and the parameters. Adoption of a custom function improves the accuracy of the inter-phase drag model and the erosion effect. This paper analyses the flow state of the high-concentration solid-liquid dense phase fluid in backfill gravity transportation to obtain optimized transportation parameters. The research results improve the accuracy of the calculation of the backfill pipeline transportation parameters, which can be effectively applied in the optimal design of high-concentration slurry backfill pipeline transportation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".