Sewage treatment in integrated system of UASB reactor and duckweed pond and reuse for aquaculture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it