Significant cost savings using a newly developed single‐process technology compared to conventional processes for treating high‐strength effluents
Bibliographic record
Abstract
Abstract This study investigates the performance of a newly developed technology, known as simultaneous anaerobic oxidation/partial nitrification–denitrification (SAO/PND), for treating strong wastewater under laboratory conditions. Excellent results are achieved, with maximum chemical oxygen demand (COD) and nitrogen removal of 98% and 96%, respectively, when treating synthetic wastewater with influent 4120 mg/L COD and 210 mg/L NH 4 + –N. Appropriate living environment in this single‐process technology favoured the co‐existence of various bacterial communities, and removal mechanisms by these organisms are identified and confirmed. The results showed that the single‐process SAO/PND technology required 95% and 90% less oxygen compared to conventional aerobic processes and combined systems, respectively. Furthermore, this newly developed technology produced 60% and 44% less sludge, respectively, in comparison with the mentioned conventional systems. This study shows that the single‐process SAO/PND technology is a promising sustainable alternative to conventional systems, as it combines high efficiency and cost‐effectiveness with simple operation and maintenance requirements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".