Evaluation of automatic polymer dosing control to optimise the performance of belt presses
Bibliographic record
Abstract
Increasing environmental, regulatory and social scrutiny has necessitated a change in tailings management governance across the mining industry. The sector is trending towards dry disposal of tailings to reduce environmental impact, tailing dam risk exposure and to safeguard the sustainability of operations. Common technologies employed for tailings dewatering and water recovery include thickeners and filters. A key challenge for sites employing belt press filters and gravity drainage decks is to optimise belt performance and polymer dosing control whilst treating variable ore types and clays that are difficult to dewater. Polymer addition is critical to the efficacy of the dewatering process with dosing typically adjusted by the filter operator based on visual inspection. The practice of high polymer addition is common to achieve stable filtration over extended periods and to reduce the level of operator supervision. However, the polymer dose may not be sufficient to account for changes in sludge conditions such as flow rate and density. This may lead to a drop in cake dryness, blinding of the belt, reduced reliability and stability of the belt, increase frequency of overspills and ultimately reduced plant productivity and increased treatment costs. Enhanced polymer dose control requires relevant, accurate and timely monitoring. Focus has been placed on developing a continuous measurement and control system, which detects the topography of the sludge on a belt and accurately adjusts the polymer dose, via proprietary algorithms, to optimise the belt performance amid changing sludge conditions. This paper will present the advantages of automatic control compared to traditional manual techniques and corroborate these advantages by case studies.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".