Enhancing Agricultural Biogas Desulfurization: Improving Cost-Efficiency and Robustness Through Micro-Aeration with Psychrophilic Anaerobic Liquid/Solid Media
Bibliographic record
Abstract
This study endeavors to develop an economical and user-friendly biological sulfide oxidation system and explore its mechanism for generating biological elemental sulfur under micro-aerobic conditions using psychrophilic anaerobically digested media (liquid/solid inoculums obtained from agricultural livestock wastes) for sulfide-free biogas production. With an initial hydrogen sulfide concentration of 5000 ppm, a biogas flow rate ranging from 0.9 to 1.8 L/h-Linoculum-mix, and an air injection rate of 0.6–1% (oxygen concentration in biogas), a remarkable biodesulfurization efficiency of 99–100% was attained using solid inoculum as the biodesulfurization medium. This efficiency was achieved without compromising the methane quality in the treated biogas. Compared to liquid inoculum, solid inoculum requires less than half the volume and no mixing equipment, such as bubble column reactors. The biodesulfurization reactor requires only 1 m3, which is approximately 1.5% of the volume of a wet anaerobic digester and 3% of a dry anaerobic digester, while processing cow manure (Total Solids: 20%) at 1.03 m3 of manure per day. Moreover, it can be operated at (19–20 °C), leading to substantial reductions in cost and footprint.
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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) | 0.000 | 0.000 |
| 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".