Cost Effective Technology of Alunite Ore Processing
Bibliographic record
Abstract
Soda-alkaline method of alunite ore processing includes crushing, grinding and enrichment (flotation) of the alunite ore. Enriched alunite ore, containing 50 - 60% of alunite, is roasted at temperatures between 520о – 620о С for 1 – 3 hours. Roasted alunite is further leached with sodium carbonate solution (5–20 %). Proportion of sodium carbonate for binding of SO3 aluminum sulfate in alunite accounts for 100 – 110 % of stoichiometric quantities. Leaching takes place at temperatures around 70 – 100о С for 0.5 – 2.0 hours. Solution of the resulting pulp contains all the potassium sulfate from alunite and sodium sulfate from soda. Solution of sulfates is separated from the insoluble residue and is fed for conversion with potassium chloride. As result of this conversion we obtain quantities of potassium sulfate (fertilizer) and table (common) salt. The remaining insoluble residue contains all the aluminum oxide from alunite and waste rock. Further processing of the insoluble residue based on the Bayer out-of-autoclave process produces alumina and quartz sand. Besides alumina, this method makes it possible to get four times more the amount of potassium sulfate and certain volumes of table salt. Taking into account the processing capacity of Ganja Alumina Plant (150,000 tons of alumina per year), this method allows the production of fertilizer, potassium sulfate (370,000 tons per year), coagulant for purification of water from mechanical impurities (49,000 tons per year), table salt (NaCl) (126,000 tons per year), and quartz sand for non-ferrous casting and production of construction materials (300,000 tons per year). Approximate yearly financial efficiency of the soda-alkaline technology for processing of 150,000 tons of alumina per year will be around 171,46 million USD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.007 | 0.004 |
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".