Bayesian statistics study of a sustainable dissolution of cobalt‐bearing minerals from <scp>Cu‐Co</scp> ores
Bibliographic record
Abstract
Abstract The present paper discusses the dissolution of cobalt‐bearing minerals from a copper‐cobalt ore using probabilistic models where a priori and a posteriori knowledge of leaching are used to predict the dissolution of cobalt‐bearing minerals in a sulphuric acid medium in the presence of a reducing agent. Priorly, the dissolution of cobalt‐bearing minerals depends on their mineralogy, leading to the use of FeSO 4 as a reducing agent for the trivalent (Co 3+ ) form of cobalt (CoOOH). A posteriori, the dissolution of Co 3+ is improved by the presence of ferrous ions, resulting from the dissolution of Fe‐bearing minerals, including Fe from Co(Fe)OOH. The results showed that the predictive‐oriented probabilistic graphic models based on the Bayesian approach, in combination with the design of the experiment data, made it possible to model the leaching of cobalt‐bearing minerals. The results from the design of the experiment using the experimental tree methodology associated with the optimization of the multiple responses in a multiple input for a multiple output set‐up derived the following optimized parameters: 60°C for the temperature (T), 850 rpm for the agitation, 40% for the solid percentage, 1.5 for the pH, and 4 g/L for the concentration of the Fe 2+ ion. The cobalt dissolution yield obtained was 89.95%. The analysis of the dependence between the random variables only (P(Fe 2+ |T), P(pH|T), and P(Fe 2+ |pH)) and the dependence between the random variables and the responses (P(Co‐yield|pH, Eh)) allowed the construction of two Bayesian networks, respectively, with and without posterior knowledge. For the Bayesian network with posteriori knowledge, the {5–2} structure was found to be the most appropriate arrangement. The model predicted a cobalt yield value, and the experimental value indicated a correlation coefficient ( R 2 ) of 0.861.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".