Trihalomethanes minimization in drinking water by coagulation/flocculation/sedimentation with natural coagulant <i>Moringa oleifera</i> Lam and activated carbon filtration
Why this work is in the frame
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Bibliographic record
Abstract
Water treatment plants are designed to remove turbidity and apparent colour, and produce safe water from a microbiological and chemical point of view. Disinfection is the step responsible for the microbiological security, for which chlorine is the most widely‐used agent, since it can react with organic matter present in raw water to form total trihalomethanes (TTHM), which are harmful to humans. In this context, it is proposed to evaluate the effectiveness of the combined process of coagulation, flocculation, and sedimentation (C/F/S) followed by an activated carbon column. The water for the tests was from the Pirapó River (Brazil) with low colour and turbidity. In C/F/S tests, natural coagulant solutions from Moringa oleifera (MO) degreased with ethanol (MO(et)) and hexane (MO(hex)) were compared to coagulant aluminum polychloride (PAC) with further filtration in an activated carbon column. For all these tests, removal efficiency of apparent colour, turbidity, dissolved organic carbon (DOC), and compounds with UV 254nm absorption (UV 254nm ) were evaluated. The disinfection process was performed and residual chlorine and TTHM formation were evaluated. It was observed that the process of C/F/S using MO(et) followed by filtration through activated carbon was able to reduce the values of physicochemical parameters (96 % removal for turbidity and apparent colour, 93 % for UV 254nm , and 99 % for DOC) with reduced formation of TTHM (25.31 μg/L). Moreover, oil extraction with ethanol presents advantages over hexane due to being a solvent with good operational security, low toxicity, and being a bio‐renewable source, all characteristics not present in hexane.
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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) | 0.000 | 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 it