Refining zone temperature control: a good choice for pulp quality control?
Bibliographic record
Abstract
In control strategies for thermomechanical pulp refiners, the relation between energy consumption and production rate,\noften called specific energy, has been used as a key variable for decades. The importance of controlling the specific energy and thereby indirectly the pulp quality, has been an indisputable axiom for most engineers engaged in the pulp and paper industry. Recently, another competing concept based on refining zone temperature measurements has been presented as an alternative for improved pulp quality control, but so far no comparison between these two concepts has been made.\n\nIn this study, the two concepts are compared based on a system identification procedure using “Auto Regressive Moving Average eXogenous” (ARMAX) models. The identification procedure adopted creates dynamic models that can provide predictions of the commonly used pulp quality variables Canadian Standard Freeness (CSF) and Mean Fiber Length (MFL). These predictions are all based on the\ntraditional process variables, i.e. production rate, dilution water flow, and hydraulic pressure, in combination with information of either the specific energy and or the temperature profile.\n\nThe results show that it is not motivated to use the specific energy for predictions of CSF and MFL, as it gives\nlimited dynamic information of the refining process besides what already is given by the three traditional process variables. Whereas, using the refining zone temperature measurements as inputs to the ARMAX models results in a significant improvement of the ability to predict the pulp quality variables. From a control perspective, this implies that refining zone temperature control is preferable to any concepts based on specific energy when it comes to minimization of pulp quality variations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".