WORLD TRENDS IN THE DEVELOPMENT OF TECHNOLOGIES OF HYDROMETALLURGICAL PROCESSING OF NICKEL ORES AT OPERATING ENTERPRISES
Bibliographic record
Abstract
The paper provides a critical analysis of sources, a generalization of factual and theoretical material on the main hydromet-allurgical technologies for processing nickel ores. It has been established that the use of autoclave leaching of ores is the most common method of processing. This process takes place mainly in capacitive reactors with a stirrer. It was found that, depending on the type of ore, there are two main ways to carry out autoclave leaching: acid leaching under high pressure and the Caron process (ammonia leaching during roasting). The review shows that sulfuric acid leaching is predominantly used in Cuba and Western Australia, as well as in Finland, South Africa and Canada. Nitric acid leaching is being used in pilot plants in Australia at the CSIRO facility. Chlorine leaching is used in Japan, Norway, France and Canada. Ammonia processes have been implemented in Cuba, the Czech Republic and Australia, as well as in Brazil, Canada and the Philippines, India and Gag Island in Indonesia. The article presents the latest achievements in the field of extraction of nickel and cobalt from productive solutions, as well as the advantages and disadvantages of existing schemes at operating enterprises in the world. Having analyzed the main technologies for processing nickel ore, we can say that traditionally Ni and Co are extracted from productive solutions after leaching in one of three ways: 1. Precipitation of mixed nickel and cobalt sulfide; 2. Precipitation of mixed nickel and cobalt hydroxide; 3. Direct solvent extraction. The analysis showed that the most optimal and least time-consuming process, providing a relatively high degree of extraction of target metals, is extraction. Impurity removal is most expediently carried out by precipitation, however, the loss of nickel and cobalt should be taken into account.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".