Optimization and Management of Flotation Deinking Banks by Process Simulation
Bibliographic record
Abstract
In this work, the contribution of flotation deinking banks design on ink removal efficiency, selectivity, and specific energy consumption was simulated using a semiempirical approach. Single-stage with mixed tank/column cells, two-stage, and three-stage configurations were evaluated, and the total number of flotation units in each stage and their interconnection were used as main variables. Explicit correlations between ink removal efficiency, selectivity, energy consumption, and line design were developed for each configuration. When considering a conventional two-stage configuration as reference, a decrease in the specific energy consumption for constant ink removal efficiency and selectivity was obtained with the single-stage bank with a stack of flotation columns at the front of the line, whereas an increase in ink removal selectivity for constant ink removal efficiency and specific energy consumption was obtained with the three-stage bank. The present results show that the performance of conventional flotation deinking banks can be improved by optimizing process design and implementing mixed tank/column technologies in the same deinking line.
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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".