Impact of fly ash pretreatment on aerobic treatment of thermomechanical pulping spent liquor
Bibliographic record
Abstract
This study investigated the impact of biomass-based fly ash (FA) pretreatment on the biodegradability of a thermomechanical pulping spent liquor (TMPL) in an aerobic system. In this study, FA was mixed with TMPL under the conditions of 6 wt.% based on TMPL, 25°C and 10 h, which removed a part of recalcitrant organic materials and resulted in 68.0, 40.0, 60.1, 81.2 and 48.3% reductions in chemical oxygen demand (COD), biochemical oxygen demand (BOD), total organic carbon (TOC), lignin and sugar, respectively. FA-pretreated TMPL pressate (FA-TMPL) was biologically treated in an aerobic system of sequencing batch reactor (SBR). The performance of the biological treatment with and without FA pretreatment was studied in two parallel SBRs over three months. The combination of FA and biological treatments removed 97.3% of COD, 98.3% of BOD, 96.3% of lignin, 99.5% of sugar, and 98.1% of TOC. Without FA pretreatment, the biological system removed 87.3% of COD, 89% of BOD, 81.6% of lignin, 98.6% of sugars, and 90.5% of TOC. The results also confirmed that the settling ability of sludge, which was indicated as a sludge volume index, was reduced from 109.3 mL/g to 53.5 mL/g. In addition, the advantages of using FA pretreatment in aerobic systems were discussed in detail. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:370-378, 2018.
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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 | 0.000 |
| 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) | 0.001 | 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".