Municipal Wastewater Treatment by USAB Process: Start-up at 20°c and Operation at Low Temperatures
Bibliographic record
Abstract
Laboratory-scale UASB reactors were started-up successfully at 20 degrees C and operated at temperatures of 32, 20, 15, 11, and 6 degrees C applying several hydraulic retention times (HRTs) ranging from 48 to 3 h during an operational period of approximately 900 days. Changes in temperature and HRTs impacted the reactor performance. However, overall reactor performance (70 to 90% COD removal) was found to be stable up to an HRT of 6 h and temperature of 11 degrees C. The performance of UASB reactor was not very stable during 6 degrees C operation, even though 30 to 50% of COD removal could be achieved. Biomass aggregation in the form of granules/bio-pellets (mean size ranged from 1.8 mm to 3.0 mm) could be achieved during 20 degrees C operation. The impact of temperature on morphology, surface structure, and shape of bio-pellets was explored. Morphological and elemental composition analyses showed the possible mechanism of biomass aggregation in UASB reactors. This study demonstrated that the UASB process could be applied successfully with some minor adjustment for the treatment of municipal wastewater in temperate and cold regions (average summer temperature 11-25 degrees C).
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.
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 | 0.000 |
| 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 | 0.001 |
| 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".