Plant‐wide hierarchical optimization based on a minimum consumption model
Bibliographic record
Abstract
Abstract Plant‐wide optimization for a complex process consisting of multiple unit processes is an effective method to achieve production with high efficiency and low consumption. However, due to the complexities of the plant‐wide optimization problem such as high dimensionality, numerous constraints, and other factors, plant‐wide optimization for a complex process is always a challenging problem in research. Therefore, this paper presents a plant‐wide hierarchical optimization method based on a minimum consumption model which can solve the feasibility and optimality of the obtained technical indices. The plant‐wide optimization problem is converted into a two‐level optimization problem, including the procedure‐level optimization and the process‐level optimization. The presented method can simplify the original plant‐wide problem and effectively reduce the computational complexity. Moreover, the hierarchical decision‐making is achieved simultaneously and the efficiency of solving the optimization problem and the global convergence is improved. Finally, the presented plant‐wide hierarchical optimization method is applied to the hydrometallurgy process of a gold smelter. The results verify the effectiveness of this method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.003 | 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".