Energy and paper recycling: Modelling the time and energy requirements for low consistency batch repulping
Bibliographic record
Abstract
Abstract An analytical model for low‐consistency repulping linking pulp material properties, consistency, temperature, and rotor and vat geometry is provided, which allows for accurate prediction of the time and energy required for repulping in both a 0.25 m3 laboratory‐scale repulper and a 15 m3 industrial‐scale repulper. The model assumes that all deflaking work is done by the repulper rotor in the rotor‐swept volume by turbulence generated by the rotor and that no deflaking occurs in the rest of the vat. Rotor shaft power is split linearly between the breakup of waste paper and dissipation by turbulence. Comparing the model predictions and experimental data for different pulp types, vat fill levels, and pulp suspension consistencies yields a correlation of R2 = 0.99 between all experimental results and the model predictions given the condition of fully turbulent (Reynolds‐independent) repulper rotor operation. The efficiency of the laboratory repulper is reduced from that predicted by the model for very low vat fill levels. This is due to a loss of effectiveness of the baffles at these low levels as indicated by solid body motion of the suspension and reduced rotor power number. This indicates that thorough mixing is a requirement to maximize repulping efficiency. Repulping time and energy savings can be accomplished by increasing the suspension consistency and the rotor‐swept volume/vat volume ratio by either increasing rotor size or reducing vat volume, all while ensuring complete mixing and circulation in the vat.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".