Experimental Study on Indirect CO<sub>2</sub> Mineralization of Industrial Solid Wastes: Electric Arc Furnace (EAF) Slag and Nickel Mine Tailings
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide CO 2 mineralization utilizing alkaline industrial wastes as feedstocks is a promising approach for long-term carbon sequestration. This study investigates the indirect carbonation of electric arc furnace (EAF) steel slag and nickel mine tailing using two leaching agents, inorganic hydrochloric acid (HCl) and waste-derived acid mine drainage (AMD). The efficiency of metal leaching and CO 2 sequestration capacity were evaluated under varying process conditions, including temperature and carbonation reaction acceleration by ultrasound cavitation. Results demonstrated that AMD can be a viable alternative to HCl, potentially reducing chemical costs while aiding in mine waste remediation. However, the effectiveness of both leaching and carbonation processes is strongly influenced by the mineralogical composition of the feedstocks. For serpentine-based feedstock rich in magnesium (nickel tailing), using AMD as a leaching agent resulted in a sequestration capacity of up to 65.12 g-CO 2 /kg-Ni-Tailing versus 18.28 g-CO 2 /kg-Ni-Tailing with HCl at room temperature. Conversely, for EAF steel slag rich in calcium, the trend was opposite, with HCl achieving a sequestration capacity of up to 154.76 g-CO 2 /kg-EAF versus 63.58 g-CO 2 /kg-EAF with AMD. The study further explores the impact of operating temperature and ultrasound process intensification on reaction kinetics, concluding that CO 2 sequestration efficiency was improved either by increasing the temperature or employing ultrasound processing. These findings contribute to the development of sustainable mineralization strategies for industrial waste valorization and greenhouse gas mitigation.
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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 | 0.000 | 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) | 0.001 | 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 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".