An adaptive artificial neural network to model a Cu/Pb/Zn flotation circuit
Bibliographic record
Abstract
We describe the planning and development of an artificial neural network model of line 3 of the Copper/Lead/Zinc flotation circuit at Brunswick Mining's concentrator at Bathurst, New Brunswick. The prototype model predicts the copper and lead assays of the concentrate streams of this rougher flotation circuit. In the model, the actual values and rates of change in the main process variables such as head grades, reagent addition, mass flow, density, pH, temperature, cell level and grind size are treated as inputs. The global error in both training and testing of the model is used to indicate the accuracy of the model. The model is fully adaptable, i.e., it can be updated when required to account for ore and/or processing changes that are not currently included in the ANN because of lack of instrumentation or reliability of measurements. The adaptation algorithm is used to select current data to replace records in the existing training and testing datafile. Retraining is conducted whenever the model accuracy declines to a pre-defined target value. The algorithm determines the frequency of retraining. The final system will be expanded to calculate a total of 12 assays using a separate ANN model for each. All models are independently updated. This approach to artificial neural networks provides plant engineers with a process model that is always current and reasonably accurate. Model access provides flexibility in adjusting set-points to achieve increased efficiency in the control of process variables.
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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) | 0.003 | 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 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".