Optimizing removal of arsenic, chromium, copper, pentachlorophenol and polychlorodibenzo-dioxins/furans from the 1–4 mm fraction of polluted soil using an attrition process
Bibliographic record
Abstract
The objective of this study was to evaluate, at a pilot scale, the performance of an attrition process for removing As, Cr, Cu, pentachlorophenol (PCP) and polychlorodibenzodioxins and furans (PCDDF) from a 1-4 mm soil fraction. A Box-Behnken experimental design was utilized to evaluate the influence of several parameters (temperature, surfactant concentration and pulp density) and to optimize the main operating parameters of this attrition process. According to the results, the concentration of surfactant (cocamidopropylbetaine-BW) was the main parameter influencing both PCP and PCDDF removal from the 1-4 mm soil fraction by attrition. The behavior of each 2,3,7,8-PCDD/F congener during the attrition process was studied. The results indicated that the concentration of surfactant had a significant and positive effect on the removal of almost all of the dioxin and furan. The removal of 56%, 55%, 50%, 67% and 62% of the contaminants were obtained for As, Cr, Cu, PCP and PCDDF, respectively, using the optimized conditions ([BW]= 2% (w.w-1), T = 25°C and PD = 40% (w.w-1)). These results showed that attrition in the presence of a surfactant can be efficiently used to remediate the coarse fractions of soil contaminated by As, Cr, Cu, PCP and PCDDF.
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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 | 0.001 |
| 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 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".