Optimizing Chemically Enhanced Primary Treatment Processes for Simultaneous Carbon Redirection and Phosphorus Removal
Bibliographic record
Abstract
There has been increased interest towards maximizing wastewater energy recovery by enhancing the carbon captured through the primary treatment process such as chemically enhanced primary treatment (CEPT). This research was conducted to optimize the CEPT performance in terms of redirection of carbon and nutrients in both bench- and full-scale operations. In order to improve the CEPT process, the performance of ferric chloride and seven types of polymers were evaluated through jar testing. The optimal coagulant (15 mg/L ferric chloride) and flocculant (0.5 mg/L poly aluminum chloride (PACl)) combination achieved total COD, soluble COD, total suspended solids (TSS), and total phosphorus (TP) removal efficiencies of 76, 58, 89, and 84, respectively, in a full-scale primary clarifier operation. In doing so the organic matter and phosphorus were concentrated in CEPT sludge, making them available for recovery. Furthermore, the relationship between influent characteristics and removal rates under varying operating conditions was investigated. It was found that soluble COD removal appeared to be season-dependent, and TSS removals were independent of influent TSS concentrations in all scenarios. The removal of tCOD, sCOD, and TP had a positive relationship with their corresponding concentrations when the polymer Alcomer® 120L was used, whereas no correlation between removal and concentration was observed with PACl.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".