Bibliographic record
Abstract
This work investigates the retention of precipitated calcium carbonate (PCC) in thermo-mechanical pulp suspensions using polyethylene oxide (PEO), phenol formaldehyde resin (PFR) system in conjunction with Raifix 120, Raifix 2515 and Raisapac. Raifix is a starch-based polymer produced by Raisio chemicals of Finland. Raisapac is poly hydroxy aluminum chloride and is also manufactured by Raisio chemicals. The PCC used for this work was obtained from Specialty Minerals Inc. and its commercially known as Albafil. To maintain the pH of the PCC loaded pulp suspension to neutral the PCC was treated with phosphoric acid (0.2g/g of PCC). A 10 wt. % PCC suspension was pretreated with phosphoric acid for 24 hours according to the method proposed by Pang (Pang, 2001)and used in the first pass retention experiments. First pass retention experiments were performed using a Britt dynamic drainage jar. A 0.5% pulp suspension was loaded with 20% of pretreated PCC (10 wt.%). At first the retention experiments were done with only PEO and PFR at a PEO dosage of 0.15 mg/g of OD pulp. The retention of fines and pretreated PCC increased by 27% and 62% respectively at a PEO, PFR ratio of 1. Retention experiments were then performed with Raifix (120 or 2515) in conjunction with the PEO, PFR system. The best sequence of addition using Raifix was found to be Raifix-PCC-PFR-PEO. A 32 statistical design of experiments showed that there is a significant interaction between PEO, PFR and Raifix. This interaction was manifested as an increase in retention at a dosage of 0.05 mg/g of PEO, PFR, but decreased at a PEO, PFR dosage of 0.15 mg/g of OD pulp. It is believed that Raifix scavenges PFR and therefore interferes with retention. The sequence used for first pass retention with Raisapac was Raisapac, PCC, PFR and PEO. The retention obtained using Raisapac was greater than that using Raifix but similar to that obtained by PEO, PFR. The effect of DDJ stirrer speed at various rpm was performed with the best retention conditions for PEO, PFR system, Raifix-PEO-PFR and Raisapac-PEO-PFR system.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".