Integrated Mineral Carbonation of Ultramafic Mine Deposits—A Review
Bibliographic record
Abstract
Recently, integrated mineral carbonation for CO2 sequestration has received significant attention due to the high potential for commercialization towards mitigating climate change. This review compiles the work conducted by various researchers over the last few years on integrated mineral carbonation processes in the mining industry, which use ultramafic mine wastes as feedstock for mineral carbonation. Here, we introduce the basic concepts of mineral carbonation including a brief description of the process routes and pre-treatment techniques. We discuss the scope of integrated mineral carbonation process application, and critically review the integrated mineral carbonation process in the mining industry including modified passive carbonation techniques in tailing storage facilities, and ex-situ carbonation routes using fresh tailings. The focus of the discussions is the role of reaction condition on the carbonation efficiency of mine waste with various mineralogical compositions, and the benefits and drawbacks of each integrated mineral carbonation process. All discussions lead to suggestions for the technological improvement of integrated mineral carbonation. Finally, we review the techno-economic assessments on existing integrated mineral carbonation technologies. Research to date indicates that value-added by-products will play an important role in the commercialization of an integrated mineral carbonation process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".