An Observer to Detect Infrequently-Occurring Disturbances in Grinding Operations
Bibliographic record
Abstract
Changes in ore properties create challenges for the control and optimization of comminution operations because they are generally difficult to measure in real time and have significant impacts on the grinding process and downstream operations. The effectiveness of a multi-model observer to detect and estimate step changes in the particle size distribution of the ore feed to a semi-autogenous grinding (SAG) mill using noisy measurements of the product particle size, is evaluated using a simulation model of the process. The observer maintains multiple hypotheses about the disturbance until their likelihood given the measurements can be determined and used to estimate the disturbance and the true process output. The results demonstrate that the multi-model observer has lower overall estimation errors than a single Kalman filter because it responds to changes in the output quickly without a compromised sensitivity to noise during steady-state. Real-time estimation of changes in ore feed properties in grinding operations could have significant benefits, however, more work is needed to characterize these disturbances, to determine if the process and disturbance models can be identified in practice, and to estimate the potential benefits.
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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 | 0.001 |
| 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".