Viability of rapid startup and operation of UASB reactors for the treatment of cassava wastewater in the semi‐arid region of northeastern Brazil
Bibliographic record
Abstract
The production of cassava flour generates wastewater with a high concentration of organic matter and nutrients, which gives this effluent potential as a source of both bioenergy and pollution. Thus, cassava wastewater needs to be properly treated prior to release into the environment. Different treatment processes are employed for this purpose, but studies involving up‐flow anaerobic sludge blanket (UASB) reactors without modifications are scarce due to the rapid acidification of cassava wastewater. Thus, the aim of the present study was to evaluate the rapid startup of UASB reactors at 30 °C for the cassava wastewater treatment. The reactor was operated under eight different conditions with a hydraulic retention time (HRT) of 8 or 12 h and organic loading rates (OLR) of 12.0 or 15.5 g COD · L−1 · d−1. The systems were evaluated based on chemical oxygen demand (COD) removal, the production of methane, and the stability of the volatile fatty acids/total alkalinity ratio. The UASB system with the best performance was that with the 8 h HRT and OLR of 12.0 g COD · L−1 · d−1, with COD removal rates ranging from 71 to 80 % and methane production of 0.260 L CH4 · g −1 CODremoved. The system offers a real‐scale prospect and is a promising option for the replacement of firewood in cassava flour toasting ovens, thereby contributing to the preservation of the semi‐arid Caatinga biome in northeastern Brazil.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".