Sewage treatment in integrated system of UASB reactor and duckweed pond and reuse for aquaculture
Bibliographic record
Abstract
The performance of a laboratory-scale upflow anaerobic sludge blanket (UASB) reactor and a duckweed pond containing Lemna gibba was investigated for suitability for treating effluent for use in aquaculture. While treating low-strength sewage having a chemical oxygen demand (COD) of typically less than 200 mg/L, with an increase in hydraulic retention time (HRT) from 10.04 to 33.49 h, COD removal efficiency of the UASB reactor decreased owing to a decrease in organic loading rate (OLR) causing poor mixing in the reactor. However, even at the lower OLR (0.475 kg COD/(m3 x d)), the UASB reactor gave a removal efficiency of 68% for COD and 74% for biochemical oxygen demand (BOD). The maximum COD, BOD, ammonia-nitrogen and phosphate removal efficiencies of the duckweed pond were 40.77%, 38.01%, 61.87% and 88.57%, respectively. Decreasing the OLR by increasing the HRT resulted in an increase in efficiency of the duckweed pond for removal of ammonia-nitrogen and phosphate. The OLR of 0.005 kg COD/(m2 x d) and HRT of 108 h in the duckweed pond satisfied aquaculture quality requirements. A specific growth rate of 0.23% was observed for tilapia fish fed with duckweed harvested from the duckweed pond. The economic analysis proved that it was beneficial to use the integrated system of a UASB reactor and a duckweed pond for treatment of sewage.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".