Developing simple regressions for predicting gold gravity recovery in grinding circuit
Bibliographic record
Abstract
Determining whether or not a gold gravity circuit should be installed in a gold plant requires a prediction of how much gold will be recovered. This has always been a difficult task because recovery takes place from the grinding circulating load, in which gold's behavior must be described. A population-balance model (PBM) to predict gold gravity recovery was developed at McGill University in 1994 (Laplante et al, 1995). The objective of this research was to make this PBM user friendly. This was achieved in two different ways. First, the behavior of gravity recoverable gold (GRG) in secondary ball mills and hydrocyclones was described by two parameters, tau and R-25mum, and these parameters were linked to the circulating load of ore and the fineness of the grinding circuit product, for easy estimation. Second, the database of simulations produced by the PBM was represented by two multilinear regressions (one for coarse GRG, the other for fine GRG) linking the predicted GRG recovery to the natural logarithm of tau, R-25mum , the size distribution of the GRG and the recovery effort (Re ), defined as the proportion, in %, of the GRG in the circulating load recovered by gravity. Re was found to be the most significant parameter, tau the least. The GRG size distribution, represented either by two (coarse GRG) or three (fine GRG) points on the cumulative passing curve, has a significant impact on recovery. A total of twenty different GRG size distributions were used to generate the simulation database. The multilinear regressions were tested on four case studies, and found to predict GRG recovery well within the precision with which the GRG content can be measured, a relative 5%. Whenever size-by-size recovery data are available, the PBM itself would be used; if not, the simpler regressions would be preferred.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".