Effect of coagulant and flocculant addition scheme on the treatment of dairy farm wastewater
Bibliographic record
Abstract
Our group was approached by a manufacturer of treatment trains to recycle wastewater from dairy farm manure. Company X treatment trains consist of microscreening, sedimentation and filtration units attached to a reverse osmosis (RO) membrane. To enhance screening, a coagulant and a flocculant are added to the stream feeding the microscreen. However, their customers experience foaming on the microscreen as well as frequent fouling of the RO membranes. This study aimed to identify the source of foaming and to optimize the performance of the treatment train. Results show that interactions between the alum coagulant, the polymer flocculant and the contaminants are the cause of foaming. Addition of silicon polymer antifoam A effectively reduced foaming, while maintaining the same removal of total suspended solids (TSS) and total organic carbon (TOC). Alternatively, we proposed a new reagent addition scheme which circumvents back-to-back addition of the polymer flocculant and the alum coagulant. The proposed scheme resulted in significant reduction in foaming and ∼50% increase in TSS and TOC removal, even at the 40% lower dose of the cationic polymer. In addition to the economic benefit, a low concentration of left-over polymer should alleviate the fouling of the RO membrane.
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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) | 0.000 | 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 | 0.001 | 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".