A comparison of conventional and non-conventional treatment technologies on arsenic removal from water
Bibliographic record
Abstract
In this study, four treatment methods were used to remove arsenic from water, namely: (i) chemical precipitation, (ii) arsenic adsorption onto iron-oxide-coated sand, (iii) high-rate saturated floating-medium flocculator/filter and (iv) membrane hybrid system (adsorption-microfiltration). The results indicated that more than 90% of total arsenic was removed by using FeCl3 (40 mg/L) as coagulant. The removal efficiency was 10% lower when polysilicato-iron (PSI, 2.5 mg/L) was used as a flocculant. The results of both the batch and column adsorption studies showed that iron-oxide-coated sand can effectively be used to achieve very high levels of arsenic removal (less than 5 μm/L as As in drinking water). Arsenic was removed up to 78% from the packed polystyrene beads filter with in-line FeCl3 addition at a high loading rate of 30 m3/m2.h. When powder activated carbon (PAC) was used in the membrane hybrid system, 87% removal of arsenic was achieved. A mixing time of 2.7 min with the mixing intensity of 87.8 s-1 were used. A very high filtration (permeate flux of 760 L/m2.h) was observed with a membrane of pore size of 0.2 μm.
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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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".