Mineral liberation - The key to unlock the optimisation problem of separation processes
Bibliographic record
Abstract
Industrial plant operators recognise the benefits of process control to reduce the variability of key process variables. However, the actual financial value for the operation often remains elusive. There are no clear answers to apparently simple questions like what standard deviation can be tolerated on a flotation feed rate or more broadly, what is the cost of variability? A similar vague understanding reigns among metallurgists about optimisation. Is it better to maximise the plant feed rate or the recovery? Stage objectives can hardly make sense without considering the global performance, but even then, defining the optimal plant target raises questions. Quantifying the effect of the grinding product attributes on the separation process performance indicators has also posed significant practical challenges. The recent technological developments of automated quantitative mineralogy introduced new capabilities to solve this conundrum. As the mineral liberation distribution determines the ultimate grade and recovery curve, it seems natural to consider it for process control and optimisation applications. This paper examines the introduction of mineral liberation in the quest to generalise the solution of the process control and optimisation problem of separation plants. It reviews the advances of the last decade, focusing on model, simulation, control and optimisation developments. It also provides a prospective outlook of promising research work and technologies that will bring closer the materialisation of plant-wide or even mine-wide control.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".