A genetic‐algorithm‐based optimal scheduling system for full‐filled tanks in the processing of starting materials for alumina production
Bibliographic record
Abstract
Abstract Due to the instability of mine sources and the uncertainty of the composition of returned lye and waste liquid, there exists a significant fluctuation of raw slurry quality in the blending process of starting materials for sintering. The expected slurry was obtained through the mixing of starting materials in full‐filled tanks. In this article, an optimal scheduling model of full‐filled tanks is developed based on material balance principle and expert experiences subject to technological requirements. To solve such optimization problem, an improved genetic algorithm (IGA) is proposed, in which the intervention strategy is introduced into the random process of population initialization to obtain the well‐proportioned initial population and the probabilities of crossover and mutation are changed according to the difference between the fitness value of the best solution and the average fitness value of the better solutions as well as the difference between the fitness value of the best solution and the average fitness value of the current population to prevent premature convergence. The IGA‐based optimization system was applied to the processing of raw slurry for alumina production and the actual running results show that the composition fluctuation in mixed raw slurry decreased significantly, effectively improving the eligibility rate of the mixed raw slurry and contributing to the stabilization of the subsequent process of alumina production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".