Exploring the impact of thiol collectors system on copper sulfide flotation through machine learning-driven modeling
Bibliographic record
Abstract
Collector selection is a critical step in flotation, as it has a direct impact on product quality, flotation recovery, and selectivity. Collectors can consist of different components, and their effectiveness can vary depending on the type of ore being processed. The general practice in both literature and in industry is to use a mixture of collectors rather than a single collector. However, the use of a collector mixture introduces several complex issues. It is challenging to determine the specific effects of each collector on different minerals, as well as to understand the synergistic effects of mixed collectors in flotation. This study presents a novel investigation focusing on the impact of blends of NAX, AEROPHINE® 3422, and AERO® MX 5149, in varying dosages and combinations, on the flotation performance of Kupferschiefer copper ore. Kinetics flotation tests were conducted using a mechanical flotation cell with various combinations and dosages of listed collectors. For this investigation, different predictive models such as machine-learning (ML) and conventional regression analyses were developed. For model construction, a database including the results of comprehensive experimental results was constructed. The best performing model was selected considering statistical performance indicators and their performance on unseen data. A sensitivity analysis was conducted on the model to justify contributions of collectors on the copper recovery and grade. The results showed that the ML-based models provide compatible results with the expert opinions and have higher statistical performance than conventional modelling tools. According to the experimental results and models’ findings, it has shown that AEROPHINE® 3422 (a blend of isopropyl ethyl thionocarbamate and dithiophosphinate) was the most influential collector for the copper recovery. In addition, two ternary graphs were generated from the modeled data to formulate mixtures for different grades and recovery priorities.
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 | 0.001 |
| 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".