Anaerobic Digestion of Kraft Pulp Mill Foul Condensate Under Thermophilic and Mesophilic Conditions
Bibliographic record
Abstract
Foul condensate produced during the chemical recovery step of the kraft pulping process is a methanol-rich wastewater stream commonly treated through a steam-stripping process to extract volatile components before their combustion. Applying anaerobic digestion to treat this wastewater stream presents an opportunity to recover the inherent renewable bioenergy without the associated steam utilization and improve the overall energy balance of the mill. In this study, a comparison of the performance and microbiology of anaerobic digesters treating foul condensate under thermophilic and mesophilic conditions was performed. In terms of performance, both digesters performed similarly with an optimal organic loading rate and hydraulic retention time of 4 kg chemical oxygen demand (COD/[m3·day]) and 1.5 days, respectively, which produced similar methane yields of ∼200 L CH4 per kg COD loaded resulting in a methane productivity of ∼0.75 L/(L·day). The anaerobic digesters removed 95–99% of methanol-associated COD but only 16–25% of non-methanol-associated COD in the feedstock. Microbial community analysis showed that under mesophilic conditions, methylotrophic methanogenesis was the principal mechanism for methanol removal while a hydrogenotrophic methanogenesis pathway was dominant under thermophilic and favored under higher sulfur loading conditions.
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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) | 0.001 | 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".