Adsorption optimization of a biomass‐based fly ash for treating thermomechanical pulping (TMP) pressate using definitive screening design (DSD)
Bibliographic record
Abstract
Abstract Wood chips are pretreated with steam or hot water prior to refining in a thermomechanical pulping (TMP) process. Currently, the resultant effluent (i.e., TMP pressate) must be treated in the wastewater treatment facility of the mill. Biomass fly ash is also generated in pulp mills as a residue from burning wood and other biomass in boilers. Fly ash utilization is currently limited and most of it is landfilled worldwide. In this study, biomass fly ash is used as an adsorbent for removing lignocelluloses from a TMP pressate. Biomass fly ash samples were fractionated, and the results showed that their carbon content decreased, but their metal content increased, as the particle size of the fly ash decreased. The main factors impacting the chemical oxygen demand (COD) and lignin concentration of a TMP pressate via treating with biomass fly ash samples (FA1 and FA2) were determined. Model equations and optimum conditions for these reductions were developed using definitive screening design (DSD). Generally, FA1 was considered a more effective adsorbent than FA2. The maximum COD removal from a TMP pressate (91.3 %) was achieved by using FA1 with a particle size of 0.43 mm at a dosage of 70 mg/g FA1/TMP pressate and a treatment time of 2 h. The maximum lignin removal from the TMP pressate (95 %) was obtained by using FA1 with a particle size of 0.11 mm at a dosage of 46.5 mg/g FA1/TMP pressate.
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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 | 0.001 | 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".