Pretreatment and in Situ Fly Ash Systems for Improving the Performance of Sequencing Batch Reactor in Treating Thermomechanical Pulping Effluent
Bibliographic record
Abstract
In this work, two methods were applied for improving the performance of activated sludge in treating the effluent of the thermomechanical pulping process. In one attempt, the effluent of the pulping process was pretreated with 0.2 wt % of fly ash (FA) at room temperature and 100 rpm for 1 h, and the FA-pretreated samples were further processed by a sequencing batch reactor (SBR) system. In another work, FA (0.2 wt %) and activated sludge were mixed with the effluent simultaneously in an in situ system. The results showed that FA assimilation would benefit the removal of nonbiodegradable substances and thus facilitate the decomposition of contaminants by activated sludge in both systems, especially in the in situ system. The removal efficiencies of 96.1%, 99.1%, 95.2%, 90.51%, and 99.5% were achieved for COD, BOD, TOC, lignin, and sugar from the effluent, respectively. In addition, the sludge volume index (SVI) of the FA-pretreated and in situ systems decreased to 100.7 and 75.5 mL/g and the effluent suspended solids (ESS) decreased to 67.9 and 55.5 mg/L, respectively, indicating that the use of FA improved activated sludge settling and flocculation affinity. These results are attributed to the adsorption of lignocelluloses on fly ash and decomposition of lignocelluloses by activated sludge. Moreover, as under-valued biomass-based fly ash was utilized as an efficient adsorbent, the developed technique is green and promising for application in wastewater treatment systems.
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How this classification was reachedexpand
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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".