Improving the biological treatment of waste water using pressurised sequencing batch reactor
Bibliographic record
Abstract
Pollution is one of the major problems that mankind faces nowadays. Society’s expectations of waste water treatment have evolved over time. While protecting aquatic environment and public health continues to be a paramount concern, the desire to recover clean water, energy and useful resources from waste water is getting increasingly stronger today. The current study aims to reduce the pollution of water and air resulting from the biological treatment process by using a new technique (pressurised sequencing batch reactor (SBR) model) through improvement of waste water treatment performance and control of the surplus air resulting from the aeration process and reusing it more than once in the aeration process. The results showed that the pressurised SBR model compared with conventional SBR model enhanced the biochemical oxygen demand removal, the total suspended solids removal and total dissolved solids removal by 6, 8 and 12%, respectively, at a gauge pressure of 0·5 atm and by about 9, 15 and 19%, respectively, at a gauge pressure of 2 atm. In addition, the enhancement in the nitrogen and phosphorus removal was remarkable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".