Evaluating the effect of enzymatic pretreatment on the anaerobic digestibility of pulp and paper biosludge
Bibliographic record
Abstract
• A new and rigorous approach for assessing the effect of enzymatic pretreatment on AD is proposed. • Enzymes can improve anaerobic digestion increasing biogas yields by up to 26%. • First study to isolate the effect of catalytic activity and organic load from enzymes to evaluate enzymatic pretreatment. • Enzymes did not appear to be inhibited or denatured in the presence of biosludge. Anaerobic digestion of biosludge has not yet been implemented in pulp mills due to low biogas yields. Enzymatic pretreatment of biosludge has shown improvements in biogas yields but results are varied. A key limitation of previous studies is that they fail to consider the COD contribution from the enzyme solutions. The aim of this study was to systematically investigate the potential for enzymatic pretreatment on the anaerobic digestibility of pulp mill biosludge. Out of the six enzymes tested, four enhanced the anaerobic digestibility of biosludge. At the end of the BMP, a maximum improvement of 26% in biogas yield was observed with protease from B. licheniformis . There was no correlation between enzymatic activities on standard substrates and/or on biosludge and the effect of enzymes on biogas yields. Enzymes have potential for improving biosludge anaerobic digestibility but more research on optimal conditions and potential synergies with other pretreatment is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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, 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".