A robust, low-temperature, closed-loop anaerobic system for high-solid mixed farm wastes: advancing agricultural waste management solutions in Canada
Bibliographic record
Abstract
Abstract This study investigates the effectiveness of low-temperature (20 ± 1 °C) anaerobic digestion (AD) for two organic multiple farm substrate combinations: Set 1 comprising chicken manure (CM), dairy manure (DM), and waste corn silage (CS) and Set 2 comprising CM, DM, pig manure (PM), and CS. Inoculum adaptation steps were carried out using CM and CM+DM for Set 1 and Set 2, respectively. Over three consecutive operating cycles spanning 245 days with increasing organic loads, 4.3 and 2.8 g VS L −1 d −1 for Sets 1 and 2 during Cycles 1 to 5.1 and 4.6 g VS L −1 d −1 for Sets 1 and 2 during Cycle 3, a closed-loop two-stage liquid-solid AD system was employed, with performance assessed via stability ratios of short-chain volatile fatty acids and alkalinity. Results demonstrate that mono-digestion of CM with adapted inoculum yielded the highest biogas production of 424 ± 4 L over 77 days, indicating superior performance by Set 1 during Phase I, whereas a similar performance was observed during Phase 2, where Sets 1 and 2 exhibited highest specific methane yields of 0.233 ± 0.028 and 0.262 ± 0.004 L g −1 VS fed , respectively, over 68 days. Analysis of heavy metal concentrations in digestates revealed a significant decrease compared to initial raw substrate concentrations, highlighting their role as nutrients for microbial growth. This study, the first of its kind, highlights the potential of low-temperature AD systems to manage diverse organic residues/byproducts and offers insights into effective performance monitoring without compromising system integrity.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".