Enhanced Coagulation/Flocculation by Combining Diatomite with Synthetic Polymers for Oily Wastewater Treatment
Bibliographic record
Abstract
The objectives are to evaluate the feasibility of treating oily wastewater using synthetic polymers (polyaluminum chloride (PAC), polyferric sulfate (PFS), and polyacrylamide (PAM)) combined with natural diatomite and to refine the operating parameters using diatomite as an adsorbent and a coagulant aid. The enhanced coagulation/flocculation by combining PAC with diatomite was investigated through mechanism analysis compared to the combination of PFS/PAM with diatomite, respectively. The effects of coagulant dose, initial pH, and settling time on chemical oxygen demand (COD) and turbidity were studied using PAC-diatomite in comparison with using PAC only. The enhanced coagulation/flocculation of diatomite with PAC was better than that with PFS/PAM in terms of COD/turbidity removal and floc settling characteristics, considering costs. The PAC-diatomite system reduced more than 70% of COD and 90% of turbidity over a wide pH range (7–10) within 20 min, with the optimum dose of PAC 50 mg/l and diatomite 1250 mg/l. The added diatomite effectively saved over 85% of PAC dose and simultaneously increased over 50% of COD removal efficiency. This study provided a novel and economical approach for diatomite utilization in the treatment of oily wastewater, satisfying the demands of reuse or reinjection into the ground.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".