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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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, 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".