Anaerobic Digestion of Dairy Manure with Food and Industry Wastes – Enhanced Biogas Production and Digestate Quality
Bibliographic record
Abstract
The Ontario biogas industry is relatively young and the overall objective of this research was to help support the growth of the industry with investigating the use of co-substrates and reactor design to enhance biogas production, recommend guidelines on the operation of full scale systems to optimize performance and characterize digestate quality. Laboratory studies evaluated the use of various substrates in the co-digestion with liquid dairy manure. These studies were used to establish ultimate biogas yields, % volatile solids (VS) reduction and minimum hydraulic retention times (HRTs). Box-Wilson Central Composite design models for corn thin stillage and waste grease (as co-substrates with dairy manure) suggest methane yields optimize with increasing proportion of the feed VS from co-substrates (constant total VS in all assays) and increasing temperatures; however, temperature had a great effect. Bench scale studies were conducted to determine a change in digester design to optimize biogas yields and increase digestate stability. A two-phase digestion system was implemented for co-digestion systems using thin stillage and waste grease with dairy manure, and methane yields showed to increase by over 22% when compared with single-phase systems. Based on current FIT contracts of 18 to 20¢/kWhe, the increased electricity and heat production could make the two-phase system economically attractive for producers. Organic loading rates (OLRs) over 4.4 g VS/L led to digester upset and OLRs of over 4.2 g VS/L·day are not recommended. On-farm anaerobic digester systems were studied for digester performance and digestate quality. Residual biogas potential (RBP) yields were effective at evaluating the stability of digestate and the U.K. PAS 110:2014 limit of 0.45 L biogas/g VS (28 days incubation) was assessed too lenient for the Ontario systems studied. A limit of 0.25 L biogas/g VS after 28 days of incubation or 0.45 L biogas/g VS after 60 days of incubation are recommended. VS reductions ranged from 56 to 76% and easily achieved the O. Reg. 267/03 regulated 50% VS reduction. E.coli and Salmonella were typically 1 to 3 logs CFU/100 mL lower than raw manure and increased HRT did not demonstrate a significant impact on the bacterial log reductions. Intermediate alkalinity (IA)/partial alkalinity (PA) proved to be a valuable tool in determining potential digester upset and has been recommended as a standard performance parameter for on-farm systems.
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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) | 0.000 | 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 | 0.000 |
| 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".