Mineral-Based Magnesium Extraction Technologies: Current and Future Practices
Bibliographic record
Abstract
Magnesium is a valuable industrial metal prized for its strength and reactivity. Traditionally, magnesium was extracted from seawater and brines. However, to meet the rising global demand, it is now primarily sourced from mineral deposits. This shift has sparked renewed interest in extracting magnesium from non-saline sources, including carbonates, silicates, halides, oxides, and hydroxides. This review examines the extraction technologies currently used for these mineral-based resources, including pyrometallurgical, hydrometallurgical, and electrometallurgical methods. Each method is assessed based on the reactions involved in the transformation, operational principles, efficiency, and energy requirements. The review emphasizes the importance of mineral pretreatment—thermal, mechanical, and chemical—in improving magnesium recovery, especially from refractory silicates. By summarizing recent advancements and process innovations, the review aims to inform future research and industrial practices, and support the development of sustainable, cost-effective, and scalable magnesium extraction strategies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".