The impact of chemically enhanced primary treatment on the downstream liquid and solid train processes
Bibliographic record
Abstract
Abstract The use of chemical coagulants and flocculants to supplement chemically enhanced primary treatment (CEPT) processes is increasing in popularity as it has been demonstrated to improve carbon redirection and suspended solids and phosphorus removal. Dosing 15 mg ferric chloride/L of wastewater and poly aluminum chloride (PACl; 0.5 mg/L) to the influent of a primary clarifier successfully achieved improved carbon redirection and suspended solids removal at a full‐scale WWTP. In this study, the impacts of PACl on the downstream liquid and solid train processes of the same WWTP were investigated. Compared to FeCl3 addition, a combined PACl and FeCl3 addition to the primary influent reduced the TSS and TP concentrations of the secondary clarifier effluent by 20% and 33%, respectively. Effluent BOD5 and ammonia‐nitrogen concentrations of the downstream activated sludge process were not affected by the addition of a combined FeCl3 and PACl in the primary clarifier. PACl addition affects the bioavailability of carbon and hence reduced the methane production efficiency of the primary sludge by 20%–30%. However, the significant amount of carbon concentrated in the CEPT sludge would enhance the amount of energy recovered through incineration. Practitioner points The chemically enhanced primary treatment process is an attractive method for carbon redirection and energy recovery. The combined FeCl3 and PACl addition in the primary clarifier improves the full scale activated sludge process effluent quality. PACl has a negative effect on methane production.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".