Predictive Modelling Of Pilot Spiral Continuous Circuit Using Rougher Batch Spiral Data and Laboratory Shaking Table Data as Input
Bibliographic record
Abstract
The mineral processing industry has been using shaking tables as a benchmark to assess if certain ores will be amenable for processing on spiral concentrators for decades.The results obtained from a single shaking table cannot be replicated by a single spiral.A continuous closed circuit spiral flowsheet is required to replicate the results from laboratory shaking table data.To determine the spiral circuit, there was a need to develop a predictive model that can predict achievable closed-circuit spirals grades and recoveries based on batch (rougher) spiral data and laboratory-scale shaking table data.Experimental data from spirals was used to model the separation behaviour of iron ores using Rao's stochastic model that describes grade, size, and density to the probability of particles reporting to either the concentrate, middlings, or tailings.Product particle size was not taken into consideration since its effect on bulk products is minimal compared to density at a finer size distribution.A modified version of Rao's stochastic model using density as a single attribute was used to fit experimental data to generate model parameters for five spiral stages, rougher, cleaner, scavenger, recleaner, and re-recleaner to determine the mass balances to predict the grade of the concentrate at various stages.The aim of the model is to predict a spiral flowsheet that matches the density or grade distribution produced from shaking table data as the ideal upgrade potential.It was observed that the proposed model successfully fits the experimental data from each spiral stage.The goal is to relate the models from each stage and predict the density distribution or the partition curve produced by the shaking table.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".