Interpretable Dynamic Modelling and Prediction of Free Acid in Zinc Leaching Process
Bibliographic record
Abstract
In the metallurgical processing industry, the leaching process converts a concentrated slurry of zinc sulphide to zinc sulphate solution. The leaching process occurs within a multi-compartment autoclave in the presence of sulphuric acid and oxygen at high temperatures and pressure. The amount of unreacted acid (free acid) within each autoclave compartment is crucial for achieving high zinc recovery but is not directly measured, necessitating an efficient model. This work involves developing a dynamic model utilizing both the first principles and machine learning techniques to predict the free acid, making the model physically interpretable. Due to the dependency of free acid on upstream process variables, several sub-models were built for each preceding unit. The main challenge was the unavailability of several measurements required for the mass balance model, while some available measurements were sampled at a slower rate. Moreover, bias correction was performed, considering delays in receiving laboratory analysis results and the lack of exact timestamps for samples provided by the field operator. The proposed model is validated with integrated zinc and lead smelter process data. The model successfully predicts free acid at a fast rate despite several practical constraints. It performs well under various process conditions, detects abnormalities, and enhances stability in the leaching process. • A dynamic physics-based model was developed to predict free acid in a leaching process. • Model combines first principle equations with data-driven methods for accuracy. • Free acid depends on upstream variables, some unmeasured or slowly available. • Multiple models were developed to predict upstream variables accurately.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".