A two‐step coordinated optimization model for a dewatering process
Bibliographic record
Abstract
Abstract In actual production processes, the feed mass of a dewatering process is uncertain and a future production state cannot be predicted. This results in improper operation, a substandard production index, and a high energy economic index (EEI). To solve these problems, the authors propose a two‐step coordinated optimization model for the dewatering process based on production data. The prediction model of the dewatering process is first established using the data accumulated during production. A two‐step optimization model is then established to solve the problems existing in the dewatering process. The objective of the optimization is to minimize the EEI in the dewatering process, and the constraints are the ladder electricity price, operation safety, and production index. The genetic algorithm (GA) and gravitational search algorithm‐genetic algorithm (GSA‐GA) are used to solve the two‐step coordinated optimization model, and the computational time can meet the application demand. An offline simulation and a field application showed that the optimization model can be used to improve the production index and reduce the EEI, loss due to the filter cloth, and the frequency of abnormal production.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".