Application of cyclone technology in paste backfill plant design
Bibliographic record
Abstract
Historically cyclone technology has been used for classification and dewatering purposes in mineral processing. It has also been extensively used in the preparation of suitable material for hydraulic backfill. Conversely when it comes to paste backfill plant design, thickening and filtration technology is typically used, and where high backfill strength is required, classified aggregate is often added. These are viable and accepted practices for the preparation of paste backfill; however, in certain applications the use of cyclones within the paste preparation process provides alternatives which can result in lower capital and/or operating cost for the paste backfill system. This paper will present novel applications of cyclone classification and dewatering technologies in the preparation of paste backfill. Specifically, the paper will discuss how cyclones can be used in certain applications to dewater the tailings without the need for thickeners, thereby lowering the capital cost and/or reducing the cement binder requirements and consequently lowering the operating cost. Examples of projects where the use of cyclone classification was considered during the development of the process flow sheet highlights the drivers for its incorporation in the resulting paste backfill systems.
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".