Heavy Metal Removal and Neutralization of Acid Mine Waste Water ‐ Kinetic Study
Bibliographic record
Abstract
Abstract The influence of the apatite on the efficiency of neutralization and on heavy metal removal of acid mine waste water has been studied. The analysis of the treated waste water samples with apatite has shown an advanced purification, the concentration of the heavy metals after the treatment of the waste water with apatite being 25 to 1000 times less than the Maximum Concentration Limits admitted by European Norms (NTPA 001/2005). In order to establish the macro‐kinetic mechanism in the neutralization process, the activation energy, Ea, and the kinetic parameters, rate coefficient of reaction, k r , and k t were determined from the experimental results obtained in “ceramic ball‐mill” reactor. The obtained values of the activation energy Ea >> 42 kJ mol −1 (e.g. Ea = 115.50 ± 7.50 kJ mol −1 for a conversion of sulphuric acid η H2SO4 = 0.05, Ea = 60.90 ± 9.50 kJ mol −1 for η H2SO4 = 0.10 and Ea = 55.75 ± 10.45 kJ mol ‐1 for η H2SO4 = 0.15) suggest that up to a conversion of H2SO4 equal 0.15 the global process is controlled by the transformation process, adsorption followed by reaction, which means surface‐controlled reactions. At a conversion of sulphuric acid η H2SO4 > 0.15, the obtained values of activation energy Ea < 42 kJ mol ‐1 (e.g. Ea = 37.55 ± 4.05 kJ mol ‐1 for η H2SO4 = 0.2, Ea = 37.54 ± 2.54 kJ mol ‐1 for η H2SO4 = 0.3 and Ea = 37.44 ± 2.90 kJ mol ‐1 for η H2SO4 = 0.4) indicate diffusion‐controlled processes. This means a combined process model, which involves the transfer in the liquid phase followed by the chemical reaction at the surface of the solid. Kinetic parameters as rate coefficient of reaction, kr with values ranging from (5.02 ± 1.62) 10‐4 to (8.00 ± 1.55) 10‐4 (s ‐1 ) and transfer coefficient, kt, ranging from (8.40 ± 0.50) 10‐5 to (10.42 ± 0.65) 10‐5 (m s ‐1 ) were determined.
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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 | 0.000 |
| 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) | 0.000 | 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".