Demonstrating scale-up of a novel water treatment process using super-bridging agents
Bibliographic record
Abstract
Fiber-based materials have emerged as a promising option to increase the efficiency of water treatment plants while reducing their environmental impacts, notably by reducing the use of unsustainable chemicals and the size of the settling tank. Cellulose fiber-based super-bridging agents are sustainable, reusable, and versatile materials that considerably improve floc separation in conventional settling tanks or via alternative screening separation methods. In this study, the effectiveness of fiber-based materials for wastewater treatment was evaluated at lab-scale (0.25 L) and at pilot-scale (20 L) for two separation methods, namely settling and screening. For the fiber-based method, the performance of floc separation during settling was slightly affected by an 80x upscaling factor. A small decrease in turbidity removal from 93 and 86 % was observed for the jar and pilot tests, respectively. By contrast, the turbidity removal of the conventional treatment, i.e., no fibers with a settling separation, was largely affected by the upscaling with turbidity removals of 84 and 49 % for jar and pilot tests, respectively. Therefore, results are suggesting that fiber-based super-bridging agents could be implemented in full-scale water treatment plants. Moreover, the tested fibers increase the robustness of treatment by providing better floc removal than conventional treatment under several challenging conditions such as low settling time and screening with coarse screen mesh size. Furthermore, at both lab-scale and pilot-scale, the use of fiber-based materials reduced the demand for coagulant and flocculant, potentially lowering the operational costs of water treatment plants and reducing the accumulation of metal-based coagulants and synthetic polymers in sludge. Acute toxicity tests using the model organism Daphnia magna show that the cellulose fibers introduce insignificant toxicity at the optimized fiber concentration. Although dedicated mechanistic studies are required at various scales to understand in detail the influence of fibers on water treatment (coagulation/flocculation time, floc formation, floc size distribution velocity gradient, etc.), the efficacy and scalability of the fiber-based approach, along with its minimal environmental impact, position it as a viable and sustainable option for existing and future wastewater treatment plants.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".