Accelerated CO<sub>2</sub> Mineralization of Acid Mine Drainage Assisted by an Ultrasound Technique: An Experimental Parametric Study
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Among the wastes produced by the mining industry, acid mine drainage (AMD) is one of the most hazardous wastes for the environment because of its highly acidic nature and high concentration of heavy metals, which may lead to harmful effects in animals, plants, and humans. Various remediation technologies are available and have been applied to AMD treatment to meet mining effluent regulations. Remediation options are divided into those that use either chemical or biological mechanisms to neutralize AMD and remove metals from the solution. Among the chemical neutralizing methods, lime (calcium oxide) treatment is a cost-effective technology that has been widely used. The effluents from a lime treatment plant usually contain high concentrations of calcium (Ca) and magnesium (Mg), which are recognized as viable metal ion sources for mineral carbonation. This work presents an experimental study on the CO 2 mineralization of AMD solutions with a simulated flue gas stream and its acceleration by ultrasound intensification. Two test methods are investigated: one involving the CO 2 mineralization of previously demetallized AMD, and the other focusing on the one-pot demetallization of raw AMD and its CO 2 mineralization. Furthermore, additional experiments are conducted by incorporating ultrasound intensification to accelerate the carbonation reactions. The effect of the temperature on the process is also investigated. The ultrasound-assisted experiments result in a higher CO 2 sequestration capacity and Mg removal efficiency than those without ultrasound, indicating the corresponding process intensification and the enhancement effect of ultrasound on carbonation reactions and greater conversion. Moreover, the Mg removal rate is enhanced by increasing the operating temperature, while the Ca precipitation rate is not immensely sensitive to temperature variation. Additionally, the results indicate that one-pot AMD treatment and CO 2 mineralization produce almost heavy metal-free effluents that comply with the Canadian Metal Mining Effluent Regulations. A maximum of 6.992 × 10 –4 CO 2 sequestration capacity (g-CO 2 /g-solution) at 80 °C is achieved for one-pot processing, which suggests that this method can be a viable approach for the mining industry, with the benefits of mitigating AMD effluent impacts and generating additional revenue through carbon credits or offsets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".