Computational optimization of flotation time in Cu–Pb sulphide processing: A multi‐algorithm analysis of kinetic models
Bibliographic record
Abstract
Abstract This study presents a computational framework for optimizing flotation time in bulk Cu–Pb sulphide processing by integrating kinetic modelling with advanced optimization algorithms. Three flotation kinetic models—first‐order, second‐order, and Agar's—were parameterized using both traditional nonlinear estimation and three metaheuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimization (GWO). Theoretical optimum flotation times derived from these models were validated against experimental incremental grade (IG) data. The first‐order and Agar's models exhibited superior convergence stability, yielding nearly identical parameters across all optimization methods. Agar's kinetic model demonstrated the lowest SSE and RMSE values, providing the best fit and most accurate prediction of optimum flotation time (7.32–7.45 min), closely matching the IG‐derived value of 7.35 min. In contrast, the second‐order model displayed higher sensitivity to algorithmic conditions and occasional local convergence by GA. The integration of global optimization algorithms with kinetic modelling enhances the precision and reproducibility of flotation time estimation, establishing a robust methodological framework for improving flotation process efficiency in complex sulphide systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".