Reflexiones ante los nuevos retos y oportunidades en la mejora genética del porcino
Bibliographic record
Abstract
This paper investigates the feasibility and advantages of using a dual-stage hyper-thermophilic/thermophilic semi-continuous reactor system for the co-digestion of Thickened Waste Activated Sludge (TWAS) and Fat, Oil and Grease (FOG) to produce biogas in high quantity and quality. The performance of the dual-stage hyper-thermophilic (70°C)/thermophilic (55°C) anaerobic co-digestion system is evaluated and compared to the performance of a single-stage thermophilic (55°C) reactor that was used to co-digest the same FOG-TWAS mixtures. Both co-digestion reactors were compared to a control reactor (the control reactor was a single-stage thermophilic reactor that only digested TWAS). The effect of FOG% in the co-digestion mixture (based on total volatile solids) and the reactor hydraulic retention time (HRT) on the biogas/methane production and the reactors' performance were thoroughly investigated. The FOG% that led to the maximum methane yield with a stable reactor performance was determined for both reactors. The maximum FOG% obtained for the single-stage thermophilic reactor at 15 days HRT was found to be 65%. This 65% FOG resulted in 88.3% higher methane yield compared to the control reactor. However, the dual-stage hyper-thermophilic/thermophilic co-digestion reactor proved to be more efficient than the single-stage thermophilic co-digestion reactor, as it was able to digest up to 70% FOG with a stable reactor performance. The 70% FOG in the co-digestion mixture resulted in 148.2% higher methane yield compared to the control at 15 days HRT. 70% FOG (based on total volatile solids) is so far the highest FOG% that has been proved to be useful and safe for semi-continuous reactor application in the open literature. Finally, the dual-stage hyper-thermophilic/thermophilic co-digestion reactor also proved to be efficient and stable in co-digesting 40% FOG mixtures at lower HRTs (i.e., 9 and 12 days) and still produce high methane yields and Class A effluents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".