Enhancing Anaerobic Digestion of Pulp and Paper Mill Biosludge Using Thermal Treatment in a Bench-Scale System
Bibliographic record
Abstract
Enhancing Anaerobic Digestion of Pulp and Paper Mill Biosludge Using Thermal Treatment in a Bench-Scale System Masters of Applied Science 2015 Xian Meng Huang Department of Chemical Engineering and Applied Chemistry, University of Toronto This study examines the feasibility of using a bench-scale anaerobic digestion (AD) system to treat pulp and paper mill biosludge. Three thermal treatment methods were studied: pre-treatment, intermediate treatment and post treatment. Thermal pre-treatment of biosludge resulted in overloading of the bench-scale reactor, causing the pH to drop significantly; this in turn caused reactor acidification. Intermediate thermal treatment of digestate had a positive impact on the digestibility of the biosludge; the specific methane yield increased by 76% and 29% in two separate bench-scale experiments. Post-treatment of digestate followed by further digestion was studied in BMP tests, and it was determined that the final specific methane yield increased by 43%. Overall, anaerobic digestion of pulp and paper mill biosludge has been proven to be successful at the bench-scale level, and thermal post-treatment methods have been shown to be effective at enhancing the digestibility of the biosludge.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".