Plant‐wide optimization based on interval number for beneficiation and metallurgy
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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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".