Comparison of process options for treatment of water treatment residual streams
Bibliographic record
Abstract
Spent filter backwash water (SFBW) and clarifier sludge comprise the majority of the waste streams from conventional surface water treatment plants and collectively are referred to as composite residuals stream. Composite residuals streams can comprise up to 3–10% of the plant throughput and generally consist of concentrated metals (e.g., aluminum), colloidal material, natural organic matter (NOM), and pathogens (e.g., Giardia and Cryptosporidium). This research project evaluated the performance of four different treatment processes in terms of their capability of restoring this waste stream to a quality that was equal to or better than that of the source water quality. The unit operations evaluated were (i) gravity thickening, (ii) sedimentation with reflocculation, (iii) dissolved air flotation (DAF) with reflocculation, and (iv) ultrafiltration (UF). The water quality from the optimal trials met or exceeded the average raw water quality of the source water for all measured parameters with the exception of manganese. The optimal pH for sedimentation was determined to be 6.0 with no alum addition. The best coagulant dosage and recycle ratio for dissolved air flotation (DAF) were found to be 30 mg/L and 20%, respectively. The optimum settling time for thickening was determined to be 0.8–1.0 d, after which soluble metal concentrations began to increase because of re-solubilization. Ultrafiltration required no coagulant and yielded superior results to the other unit operations evaluated. Key words: filter backwash water, residual treatment, gravity thickening, sedimentation, dissolved air flotation, ultrafiltration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".