New membrane treatment to produce drinking water from real and colored surface waters
Bibliographic record
Abstract
As a consequence of Global Warming, many lakes and rivers which are the main sources to produce drinking water in the North Pole countries (since Canada to Russia) have experienced an increase in the concentration of natural organic matter. This increase affects directly the turbidity and Total Suspended Solids (TSS) resulting as well in a color modification, traditional water treatment methods such as coag/flocc would require a higher quantity of chemicals (coagulants mainly to ensure the drinking water requirements. However, this in turn increases the treatment cost, produces more sludge, and therefore has a higher environmental impact. Considering the need to provide a solution to this market Veolia has been working on a new process to make potable water using an feed flow, surface water, thanks to membrane technology: Ultrafiltration (UF) + special Nano filtration (NF) since 2016. This prototype has been already in Gahard, France using as feed flow groundwater achieving satisfactory results. This work presents the first results of the operation having as feed flow a new source: a real river neat to Sens de Bretagne, France. The prototype has been able to remove 80% of the original Dissolved Organic Carbon found in the feed flow and NF permeate has already the chemical properties in terms of Manganese, Organic Matter; Iron; Alkalinity and Turbidity according to the standard limit for drinking water in France. However due to the increase of NOM and TSS at the feed flows the global efficiency of the prototype has suffered a drop up. Increase in the number of backwashes in pre filters and UF membrane resulting from the increase of fouling and operational time reduction are some of the current challenges of this project stage. Several strategies are already implemented to achieve once again the expected 90% of Global recovery already demonstrated in Gahard.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".