Economic model predictive control of a high-pressure grinding rolls circuit: energy considerations
Bibliographic record
Abstract
Mineral processing plants remain nowadays infamous for their low energy efficiency. Economic model predictive control (EMPC), by directly considering the energy costs, could possibly help reducing their footprint, but its environmental benefits are yet to be quantified. In an attempt to cast some light on this topic, this manuscript compares EMPC to advanced regulatory control (ARC) in scenarios with identical ore hardness disturbance sequences using the net value and the specific energy as metrics. The simulated circuit comprises a high-pressure grinding rolls (HPGR), a ball mill, and a flotation circuit. It is based on population balance modelling and is an extension of previous work. The ARC system consists of proportional-integral controllers maximizing the plant feed rate with override constraint handling. The results show that 1) The EMPC cost criterion must contain weights on the changes of the manipulable variables to ensure stability, 2) advanced regulatory control can generate the same economic performance as EMPC because the system's constraints define the economic optimum, and 3) the EMPC can minimize the specific energy by saturating the HPGR circuit circulating load with a hybrid criterion that penalizes power draw.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".