Effect of particle–particle shearing on the bioleaching of sulfide minerals
Bibliographic record
Abstract
The biological leaching of sulfide minerals, used for the production of gold, copper, zinc, cobalt, and other metals, is very often carried out in slurry bioreactors, where the shearing between sulfide particles is intensive. In order to be able to improve the efficiency of the bioleaching, it is of significant importance to know the effect of particle shearing on the rate of leaching. The recently proposed concept of ore immobilization allowed us to study the effect of particle shearing on the rate of sulfide (pyrite) leaching by Thiobacillus ferrooxidans. Using this concept, we designed two very similar bioreactors, the main difference between which was the presence and absence of particle-particle shearing. It was shown that when the oxygen mass transfer was not the rate-limiting step, the rate of bioleaching in the frictionless bioreactor was 2.5 times higher than that in a bioreactor with particle friction (shearing). The concentration of free suspended cells in the frictionless bioreactor was by orders of magnitude lower than that in the frictional bioreactor, which showed that particle friction strongly reduces the microbial attachment to sulfide surface, which, in turn, reduces the rate of bioleaching. Surprisingly, it was found that formation of a layer of insoluble iron salts on the surface of sulfide particles is much slower under shearless conditions than in the presence of particle-particle shearing. This was explained by the effect of particle friction on liquid-solid mass transfer rate. The results of this study show that reduction of the particle friction during bioleaching of sulfide minerals can bring important advantages not only by increasing significantly the bioleaching rate, but also by increasing the rate of gas-liquid oxygen mass transfer, reducing the formation of iron precipitates and reducing the energy consumption. One of the efficient methods for reduction of particle friction is ore immobilization in a porous matrix.
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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) | 0.000 | 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 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".