Plant‐wide optimization based on interval number for beneficiation and metallurgy
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Considering the difficulty of accurate online‐measurement of some key variables in the plant‐wide process of beneficiation and metallurgy, the quantitative models of some procedures are difficult to establish and the plant‐wide optimization control based on the quantitative models is difficult to realize. To address the problem, a plant‐wide optimization method based on interval numbers is proposed. Firstly, through analyzing the plant‐wide process of beneficiation and metallurgy, the framework of plant‐wide optimization based on interval number is given. Secondly, according to expert knowledge and the site workers' experience, the fuzzy qualitative model of the flotation process is established. Combining the quantitative model of the subsequent metallurgy process with the qualitative model of the flotation process, the plant‐wide optimization problem of beneficiation and metallurgy is established with the maximum economic benefits as the objective. Thirdly, for each output mode of the fuzzy qualitative model, interval number is used to represent the key variables that cannot be measured online, and combining the optimization algorithm based on interval number with the hierarchical decomposition optimization method, an optimization method is proposed to realize the plant‐wide optimization of beneficiation and metallurgy. Finally, compared with the conventional plant‐wide optimization method and the original hierarchical decomposition optimization method, the simulation results show that the proposed optimization method has a wider applicability, especially with its advantages in solving optimization problems with uncertainty.
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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