Anaerobic digestion of baker's yeast wastewater using a UASB reactor and a hybrid UASB reactor
Bibliographic record
Abstract
The start-up and step-up operation of two 16-liter, continuously operated, upflow anaerobic reactors receiving baker's yeast wastewater is presented in this thesis. The two reactors (A and B) were almost identical in construction. Reactor A was a conventional upflow anaerobic sludge blanket (UASB) reactor, and reactor B was a hybrid reactor. In addition to all the features of a UASB reactor, a fixed-film structure was installed in the mid section of the reactor B. Both reactors were operated at 35 °C and at a constant hydraulic retention time of 7 days. The waste strength, expressed in chemical oxygen demand (COD), was varied from 8 g COD liter⁻¹ (during the start-up) to 58 g COD liter⁻¹. The organic loading rate ranged from 1.1 to 9.4 g COD liter⁻¹ day⁻¹. The start-up lasted for the first 46 days. Towards the end of the start-up, methane production rates of 0.23 and 0.28 liter CH₄ liter⁻¹ day⁻¹ and COD reductions of 62.2% and 67.2% were achieved at organic loading rates of 1.1 and 1.3 g COD liter⁻¹ day⁻¹ for reactors A and B respectively. During the step-up operation, maximum methane production rates were, for reactors A and B respectively, 0.91 and 0.95 liter CH₄ liter⁻¹ day⁻¹ at organic loading rates of 5.8 and 6.4 g COD liter⁻¹ day⁻¹. In addition, reactor profiles for sludge concentration, pH, volatile fatty acids, and COD are also presented.
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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) | 0.000 | 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".