Brewery wastewater treatment using aerobic sequencing batch reactors with mixed culture activated sludge
Bibliographic record
Abstract
Laboratory-scale aerobic sequencing batch reactors, in both suspended-growth and attached-growth modes, were used to study the treatment of brewery wastewater. A Ringlaces material was selected and employed for the attached-growth reactors. Experiments were conducted employing a wide range of hydraulic retention times, from 0.56 to 6.06 days. The experimental results demonstrated that brewery wastewater could be successfully treated using both suspended-growth and attached-growth aerobic sequencing batch reactors. Treatment efficiencies in terms of the removals of total organic carbon (TOC), the five days biological demand (BOD5), chemical oxygen demand (COD), and suspended solids (SS) were consistently maintained over 90%, with the suspended-growth reactors performing significantly better than the attached-growth reactors. As the results of these experiments demonstrated that the performance of suspended-growth SBRs was superior to that of attached-growth SBRs, only the suspended-growth SBR system was selected to study the optimal conditions of HRT and loading rate. The results showed that the maximum removal of TOC and SS could be reached at the optimal of HRT and loading rate. The removal of TOC was more sensitive to variations in the HRT than to variations in the loading rate; however, the effect of loading rate was dominant in the removal of SS compared to the effect of the HRT. The pH remained relatively constant during the aeration stage. The dissolved oxygen concentration changed as aeration proceeded. This may be related to TOC degradation and microbial activity. A lower sludge production rate was observed in the aerobic suspended-growth SBRs. [Scientific formulae used in this abstract could not be reproduced.]
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".