Traitement des eaux usées par coagulation-floculation avec recirculation des boues chimiques : Performance générale et stabilité du procédé
Bibliographic record
Abstract
Six series of tests were carried out to have a better understanding of the stability and efficiency of a coagulation-flocculation process with chemical sludge recycling. The tests consisted in sequential sludge recycling in 100-L pilot reactors. Other tests were performed to examine the stability following wastewater loading variations. Results showed that stability was reached immediately during the first recycling sequences. Furthermore, to obtain improved results compared with those of a classical coagulation-flocculation process, the flocculant concentration must be increased according to the sludge recycling load. Results also revealed that recycling sludge does not absorb wastewater load variations. Consequently, the implementation of sludge recycling in a wastewater treatment plant would not cause effluent degradation or entail major changes in a normal plant operation routine. The predominant coagulation-flocculation mechanisms that explained the increase in efficiency, in comparison with the classical process, were identified as enmeshment and sweep flocculation. Finally, the recycled sludge produced were conditioned and dewatered in a fashion similar to that of a classical process.Key words: recycling, sludge, preformed flocs, coagulation-flocculation, treatment, wastewater, stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".