Swine Wastewater Treatment Using Submerged Biofilm SBR Process: Enhancement of Performance by Internal Circulation through Sand Filter
Bibliographic record
Abstract
Pollutants removal from swine wastewater by a submerged biofilm sequencing batch reactor (BSBR) with internal circulation of liquor through a sand filter was studied. The variation of nutrient removal efficiencies with changes in volumetric circulation ratios and rates were determined. The reactor was operated under the following conditions: One cycle per day, hydraulic retention time of 15 days, average NH4–N loading rate of 55 g m−3 d−1 , and without supplemental external carbon source. System performance was enhanced by conducting internal circulation of liquor through the sand filter. When compared with the performance of a single BSBR without sand filter, nitrogen and phosphorus removal efficiencies were found to increase by 18% and over 33%, respectively. With a circulation rate of 170 L h−1 m−3 , and duration of 22 h (circulation ratio of 0.9), TOC, NH4–N , and total soluble inorganic nitrogen (as NH4–N plus NOx–N ) removal efficiencies of 73, 97.8, and 85.6%, respectively, were achieved. The enhancement of nitrogen removal was attributed to the occurrence of denitrification in the sand filter during circulation of liquor. The denitrification rate was proportional to the volumetric circulation ratio per day, resulting in an average 15% NOx–N removal in the sand filter. Also, it was found that continuous circulation during the entire reaction phases could be one way to achieve better performance.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Environmental engineering study of swine wastewater treatment.
The study evaluates wastewater-treatment performance rather than research practice.
Environmental engineering of swine wastewater treatment processes.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".