Anaerobic digestion of wastewater from the fruit juice industry: experiments and modeling
Bibliographic record
Abstract
Anaerobic digestion of wastewater from the fruit juice industry was carried out in a batch digester. To study the effect of the pH values as well as the nutrient medium on the fermentation process, different parameters were monitored under mesophilic temperature, such as cumulative biogas volume, chemical oxygen demand (COD), total sugar, and biomass growth. It was found that for all cases, the COD concentration decreased with time. The lowest value reached was obtained when the nutrient medium was added; it was about 110 g/L after 480 h. In such cases, the COD removal reached about 80%; the highest cumulative biogas volume of about 5,515.8 NmL was reached after 480 h testing; and the lowest value reached was about 2,862.3 NmL in the case of peach-substrate containing sodium sulfite. The addition of nutrient medium improved the cumulative biogas production as well as the COD abatement. Measurement of the biogas composition highlighted three gaseous components, namely, methane (56.52%), carbon dioxide (20.14%), and hydrogen sulfide (23.34%). The modified Gompertz equation and the first-order kinetic model were used to describe the cumulative biogas production and the organic matter removal, respectively. A good agreement was found between simulated and experimental data.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".