Fe‐As sludge stability and effluent quality for a two‐stage As‐contaminated water treatment with Fe(II) and aeration
Bibliographic record
Abstract
A two-stage (I and II) lab-scale treatment system has been studied for arsenic removal from water using Fe(II) and lignosulphonates with aeration. In stage I, using an Fe/As mole ratio of 1.5-2.5 at a pH of around 6.5-7.5, the dissolved arsenic can be reduced with Fe(II) oxidation-precipitation from an initial 72 mg L(-1) to < 2 mg L(-1). The generated sludge is entirely recycled to the second tank of stage II. In the first tank of stage II, the water is further treated with the same amount of Fe(II) as that used in stage I, in the presence of lignosulphonates and aeration. The air-oxidization of Fe(II) to Fe(III) is continued for about 30 minutes at a pH of around 7.0-8.0. The water output from the first tank is transferred to the second tank in which mixing under aeration occurs with the sludge recycled from stage I. Accordingly, the dissolved arsenic in the effluent is reduced to < 0.1 mg L(-1). The results show that this two-stage process can save more than 50% of total chemical costs, and reduce the amount of sludge by more than 50%, in comparison with the conventional Fe(III)/lime-treatment process. According to US EPA regulations, the final Fe-As sludge is classified as non-hazardous materials by the Toxicity Characteristic Leaching Procedure. But, the study shows that the instability of Fe-As sludge could be influenced by some factors, such as higher pH levels, a longer water-leaching time and larger water-leaching volume, leading to the liberation of more dissolvable As species. After being treated with Ligmet stabilizer, the Fe-As sludge showed an improved stability under varying pH conditions and large amounts of water leaching. The treated Fe-As sludge is suitable for landfill disposal.
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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) | 0.001 | 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".