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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".