Counter-Current Metal Leaching and Precipitation for Soil Remediation
Bibliographic record
Abstract
Chemical acid leaching is an effective technique for extracting toxic metals from the finest fractions of polluted soils. Nevertheless, the use of large quantities of reagents and process water results in prohibitive operating costs. The purpose of this study was to evaluate the technical and economic advantages of recirculating water in a counter-current leaching process (CCLP). Five 1-h sulfuric acid extraction steps (at pH = 1.5) followed by three 5-min water-washing steps were applied to the fine particle fraction (<0.125 mm) of an industrial soil polluted by Cd (13.2 mg·kg−1), Cu (3 100 mg·kg−1), Mn (685 mg kg−1), Pb (550 mg·kg−1), and Zn (2 840 mg·kg−1). The leaching experiments were carried out at ambient temperature using a 10% soil suspension and in 1-L working volume stirred tank reactors. This paper presents results of conventional and counter-current leaching process (CCLP) tests and shows that the CCLP yields removal results for Cu (85%), Zn (86%), Mn (75%), and Cd (90%) that are similar to those obtained using the conventional leaching process. Moreover, the CCLP uses half of the quantity of acid and one-eighth of the amount of water that the conventional process uses. Metal precipitation with NaOH and Ca(OH)2 was applied to treat the acidic leachates, and good metal removal yields were achieved with both reagents. However, the large consumption of chemicals implies high operating costs. In addition, the precipitation causes considerable sludge production, particularly when using Ca(OH)2. Overall, the CCLP coupled to metal precipitation using NaOH and water recycling appears to be the most attractive option for the removal of toxic metals from this industrial soil.
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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 | 0.001 | 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 | 0.002 |
| 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 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".