Interpretation of force profiles in mill-scale LC refining
Bibliographic record
Abstract
Abstract A set of piezo electric force sensors is implemented in a 52-inch mill-scale low consistency refiner to explore the effect of varying operating conditions on bar force profiles. The sensor replaces a short length of a stator bar and measures normal and shear forces applied during the passage of each rotor bar. In previous work with this type of force sensor a rotary encoder was used to investigate force profiles for individualbar passing events(BPE) on a 16-inch laboratory-scale refiner. In the work presented here, force profiles for individual BPEs are identified based on key features in the time domain force data. The individual bar force profiles are classified assingle peakevents which feature one peak corresponding to thefiber compression forceand asdual peakevents corresponding to fiber compression force and thecorner force. The bar passing events are then analysed based on theirmean force profilesand theirdual peak ratioin the bar passing event. Findings are compared and validated by previous work on bar force profiles. It is shown that dual peak events which are considered to represent the corner force, are present through out the whole range of refining and increase with increased refining energy. This increases the understanding of the way corner force contributes to the refining process. Furthermore, it is found that different radial positions on the stator plate are subjected to different force profiles. This is thought to be due to the difference in tangential speed and a change in the fiber and floc material properties at different radial positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".