Economic MPC of deep cone thickeners in coal beneficiation
Why this work is in the frame
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Bibliographic record
Abstract
The thickener is an important operating unit in a coal handling and preparation plant (CHPP). The optimal control and operation of the thickener is critical for the efficiency and economics of a CHPP. In this work, we apply a computationally efficient economic model predictive controller (EMPC) to a deep cone thickener and compare its performance to a proportional‐integral controller and a regular model predictive controller (MPC). Based on a detailed model of the deep cone thickener that is appropriate for controller design purpose, the three control designs are compared on different aspects, including average water recovery rate and controller execution time, via extensive simulations. The robustness of the EMPC is also investigated in the presence of random disturbances in feed flow rate. The results demonstrate that the EMPC has the potential to significantly improve water recovery rate compared to the proportional‐integral control and the regular MPC.
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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 it