Developing predictive empirical filtration models for advanced tailings handling
Bibliographic record
Abstract
There has been a significant improvement in tailings dewatering techniques over recent years. However, the effects of tailings properties on the filtration process have not been vastly investigated. The different properties of tailings cause significant effects on the feasibility of filtration. Modifying or optimising filtration equipment for different tailings mixtures often requires testing procedures that involve high expenses. Therefore, an increasing demand has arisen to improve the prediction of tailings properties for efficient thickening and filtration using the available mineralogy and particle size data. Developing a suitable solution to do so is the focus of this paper. Experimental studying of tailings and their fractions helps to understand the empirical relationship of the physical properties of tailings, such as particle size distribution (PSD) and air permeability, to the filterability properties like average cake porosity and cake resistance. Also, it is vital to study how the changes of fine particle concentration affect these parameters of the tailings mixture. An algorithm developed based on the filterability of different particle size fractions of chosen minerals should be able to predict the filtration rate for a user-defined tailings blend. This type of model will be useful in evaluating the performance and economics of different tailings treatment models and studying the feasibility to produce tailings disposal solutions. It was discovered that separating fine fractions from tailings, significantly improves the filterability of the remaining portion. This opens up several further possibilities for advanced tailings handling systems. One possibility is to perform cost-efficient tailings filtration for the coarser fractions of the tailings and keep the fine fractions as slurry, which then mix with the filtration cake to form paste for surface disposal or backfill. This approach potentially allows mining companies to achieve paste rheology at lower opex and capex compared to the conventional paste thickener technology. During the study a set of laboratory experiments were conducted to fraction the tailings and determine the empirical relationships between the physical and filterability properties of each fraction and their different mixtures. Development of a filterability parameter prediction model with the PSD data and known parameters of original tailings fractions allows the possibility of predicting new, untested materials. The accuracy of the predictions depends on the degree of similarity between the new material and the original tailings material used for empirical study in this stage. By inputting the filterability parameters, suspension properties and operation conditions, the developed filtration models are able to predict the filtration process parameters such as total filtration time, final filtration volume and final cake thickness, etc. The outcome of a validated predictive filtration model can be utilised to trade-off between filtered, thickened, paste and combined treatment of tailings by modifying the tailings feed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".