Developing a universal equation to estimate the mass of dewatered wastewater sludge during biological digestion at mesophilic and thermophilic temperatures
Bibliographic record
Abstract
ABSTRACT A series of dewaterability tests were conducted on various types of sludges to establish a wholistic relationship between sludge water fractions. Sludge samples were obtained from batch and continuous sludge digesters, which were operated anaerobically and aerobically under mesophilic and thermophilic conditions. Dewaterability of the sludge samples and the distribution of water fractions were studied using centrifugation and thermal drying. Thickened waste activated sludge (T-WAS) contained 10-11 g bound water (BW)/g of total solids (TS), and it was more hydrophilic than primary and digested sludges. During anaerobic digestion, BW content fluctuated between 3.2 and 4.2 g BW/g TS. However, aerobic digestion at 55°C reduced the BW content of the mixed T-WAS + primary sludges from 3.7 to 2.1 g BW/g TS. A linear function was developed to correlate supernatant and BW mass fractions (R2 = 0.995). An equation was derived from the linear function to estimate the mass of dewatered sludge based on the TS concentration of the initial wet sludge. The developed expression is applicable to different kinds of wastewater sludges. Such an expression would be helpful for the designers and operators of sludge thickening and dewatering systems that use centrifugal separation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".