Bioenergy Recovery from Bulk Greenhouse Waste and Raw Sewage Sludge
Bibliographic record
Abstract
Global political climate has been leaning towards concepts of environmental consciousness, circular economy, and renewable energy. Several provincial governments in Canada have passed legislation that subjects certain municipal and industrial entities to mandates of waste diversion from landfills. Centralized anaerobic digestion (AD) facilities offer a promising solution to tackling waste management and renewable energy challenges. However, cost implications are typically a challenge with AD projects. This thesis explores two approaches of improving the feasibility of the AD process, namely co-digestion and pre-treatment, particularly for greenhouse crop wastes (GCW) and raw sludge (RS). A batch setup is constructed using the AMPTS II unit to allow the AD to take place at mesophilic conditions. The co-digestion of GCW with RS is first investigated and the methane yield is compared for different mixing ratios. Results reveal that with higher concentrations of GCW in the substrate mix, the methane yield per mass COD of substrate added is improved by 13 – 27% as compared to the mono-digestion of RS. Feedstock availability is found to have no bearing on the synergism/antagonism of the co-digestion. It is also observed that process kinetics favor higher levels of RS in the substrate mixture. The effect of microwave pre-treatment (PT) on the methane yield and process kinetics of the co-digestion process is then investigated using different MW power levels (480 W and 960 W) and different target temperatures (70oC, 80oC, and 90oC). Nevertheless, due to a deficiency in the buffering capacity (i.e., alkalinity) within the bottle reactors, only a slight improvement in the methane yield is observed in one of the MW PT samples (at 960 W and 70oC) over the untreated sample. Nevertheless, notable improvements in the methane yield and process kinetics are observed at the higher MW power intensity (960 W) in comparison with those at 480W.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".