Combined Removal of Carbon and Nitrogen in an Integrated UASB-Jet Loop Reactor Bioreactor System
Bibliographic record
Abstract
An innovative anaerobic–aerobic integrated bioreactor system consisting of an upflow anaerobic sludge blanket (UASB) and a jet loop reactor was developed to investigate the feasibility of combined removal of carbon and nitrogen for a low-strength wastewater at different hydraulic retention times (HRTs) and recycle ratios. Total chemical oxygen demand (COD) removal of the integrated system increased from 87 to 92%, at a combined system HRT of 44h, when the recycle ratio was increased from 100 to 400%, respectively. Denitrification efficiency of the integrated system increased from 49 to 86%, at all HRTs, when the recycle ratio was increased from 100 to 400%. The integrated system, on average, achieved more than 78% of total nitrogen at all HRTs. Nitrogen content of the biogas produced from the UASB reactor increased with increase in recycle ratios while the methane content exhibited a reverse trend, irrespective of the HRTs. Sludge volume index of the UASB reactor increased from 15to42mL∕g total suspended solids at the end of the study. Specific methanogenic activity of the granular sludge decreased from 1.3 to 0.8 g CH4–COD∕g volatile suspended solids per day at the end of the study. Nitrogen and COD mass balance of the integrated system indicated that a substantial amount of influent nitrogen and COD was lost in the effluent as dissolved form.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".