Self-optimizing control of secondary grinding – coping without particle size monitoring
Bibliographic record
Abstract
In secondary grinding circuits, the product particle size is a key variable influencing downstream performance. However, online particle size analyzers are often too costly or impractical to implement, limiting the ability to reach and maintain production objectives. This paper investigates self-optimizing control (SOC) of the product particle size using readily available instrumentation. The method identifies linear combinations of process variables that remain close to their target values despite disturbances, using a null space approach applied to steady-state data extracted from a dynamic model of the grinding circuit. Simulation results show that SOC can significantly reduce product size fluctuations caused by ore hardness variations, ore feed rate variations, and ore particle size variations. Compared to the baseline strategy, which leads to deviations of up to 9.2% relative to the nominal product size, the best SOC configuration limits fluctuations to less than 2%, using simple control loops.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".