Fermentation – Enhanced Sustainable Biological Phosphorus Removal
Bibliographic record
Abstract
The success of enhanced biological phosphorus removal depends on the constant availability of volatile fatty acids (VFAs). To reduce costs of purchasing external carbon, waste streams would be a preferred source for nutrient removal. VFAs were shown to vary in the incoming sewage and fermentate from primary sludge (PS). Another available source of organic to generate VFAs is waste activated sludge (WAS). The effect of solids retention time and biomass concentration, as well as the effect of temperature and requirement for mixing on generation of VFA from the fermentation of WAS were investigated. It was found that VFA yields from sludge fermentation increased with SRT. At the longest SRT of 10 days improved biomass degradation resulted in the highest soluble to total COD ratio and the highest VFA yield. WAS fermentation was found highly temperature-dependent. The overall VFA–COD concentration in the non-mixed reactors was much lower than the mixed reactors. The study of fermentation of PS, WAS and a mixture of WAS and PS demonstrated that PS fermentation predictably generated a significantly higher amount of soluble COD than WAS. Co-fermentation of WAS with PS enhanced soluble COD production and increased the release of phosphate and ammonium. Fermentation of combined PS and WAS sludge generated a concentration of phosphate high enough to allow phosphorus recovery as struvite The effect of using glycerol as an external carbon source in biological phosphorus removal was investigated. Using glycerol directly resulted in the failure of the process which maintained enhanced biological phosphorus removal. When glycerol was co-fermented with waste activated sludge, significant VFA production was observed. By 2 | P a g e supplying the system with the VFA-enriched supernatant of the fermentate, biological phosphorus removal was enhanced. It was concluded that, if glycerol was to be used as external carbon source for biological phosphorous removal, the effective approach was to ferment glycerol with waste activated sludge. According to the cost analysis, the economic benefit of WAS fermentation can be demonstrated in three ways: 1) cost saving in external carbon addition; 2) cost saving in sludge handling; 3) revenue from phosphorus. At current condition, the value of the recovered P product is insignificant relative to the cost of chemicals that required for recovery and capital cost of the facilities. However, P recovery becomes important when the sustainability take into account.
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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) | 0.000 | 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) | 0.000 | 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".