Disinfecting E.coli Bacteria In Drinking Water Using A Novel Fluidized Bed Reactor.
Bibliographic record
Abstract
Disinfecting microorganisms such as E.coli is a great concern in drinking water supply facilities around the world. Photocatalysis became an attractive technology to disinfect microorganisms due to its ability to degrade very low concentration of microorganisms without producing any harmful by-products. The use of optical fibre mesh reactors and slurry type photocatalytic reactors has been investigated by researchers around the world. It was shown that such reactors have low potential to be scaled up. An annular type fluidized bed reactor has been constructed and tested to disinfect E.coli bacteria. A specially prepared catalyst (TiO2 + Glass Bead + Zeolite) was used for this purpose. The proposed catalyst offered better degradation of E.coli compared to existing technology. Fluidization enhances the degradation of E-coli by moving the catalyst particles to the illuminated zone. E.coli degradation is tested in a similar arrangement in small scale with 100cm3 of contaminated water with the 106 E-coli/cm3. Repeated experiments were done with/without the presence of catalyst along with UV irradiation. With the presence of catalyst in the fluidized condition, the E-coli count is reduced to only 100/cm3 whereas the E-coli count after 30 minutes of UV irradiation only became 1800/cm3. The efficiency of E.coli removal is increased to 99.99% from 99.82% by using the proposed technology. Results of small scale prompted a construction of a pilot plant scale reactor and effectiveness of this reactor is being tested. Use of supported catalyst and the effective illumination of catalyst by fluidization help faster disinfection of microorganisms by this reactor. It has been found that the pilot scale is capable of degrading 84% of E.coli within 80minutes compared with 77.3% by using UV only which has capacity of handling 46 litre/min. E.coli degradation in larger scale is being tested for the first time and its result compared with only UV destruction is promising. The potential to use this method on a continuous reactor increases the potential for scale up.
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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) | 0.000 | 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 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".