Optimizing Solid State Anaerobic Digestion Operating Parameters in the Canadian Prairies
Bibliographic record
Abstract
Solid state (>15% solids) anaerobic digestion (SS-AD) research and system optimization is limited when applied to solid organic feedstocks, specifically cattle feedlot manure. The goal of this study was to establish SS-AD baseline information on biogas production and optimization. The project was split into three components: the design and development of a bench scale SS-AD digester; the investigation of the optimization of the SS-AD process by particle size reduction and leachate recirculation (Round 1); and, the investigation of SS-AD inoculation methods and the effects of straw addition and mixing (Round 2). Round 1 looked at the effects of crushing vs. not crushing the manure prior to digestion and examined the effects of no recirculation of the leachate, daily recirculation, and recirculation three times a week. Results regarding the particle size reduction were inconclusive due to the inherent small size of the vessels. Gas production and methane composition were comparable for all recirculation treatments; however, the weekly recirculation regime showed reduced variability in the results. Round 2 inoculation methods examined were: no inoculation, inoculation using manure digestate, and inoculation using manure digestate leachate. The straw addition and application was investigated to acquire real world results, as feedlot manure contain ample amounts of straw as it is used for livestock bedding. Straw was added either completely mixed, or layered. Preliminary data shows comparable gas production and methane composition across all treatments. Detailed analytical results and final conclusions for all rounds of study will be presented, as well as recommendations for research applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".