Development of a methodology to optimize low consistency refining of mechanical pulp
Bibliographic record
Abstract
In this dissertation we present a novel two-stage procedure to relate low consistency (LC) refiner operating conditions to changes in fibre morphology. To do so, a large database of operating conditions and resulting pulp properties were collected over a range of both pilot and industrial LC refiners operating with mechanical pulps. In total eight different Andritz TwinFlo™ were sampled over a three year period in both North America and Scandinavia. The two-stage methodology is based upon a classical dimensional analysis in which a reduced parameter space is related to each other through the use of statistical modelling. In the first stage we demonstrate a relationship between net power and operating parameters such as gap, rotational speed, diameter, plate pattern and consistency of the fibre suspension. For all refiners tested the model indicates that the net power increases nearly linearly with the inverse of gap size. In this portion of the analysis we found statistically significant relationships between operating conditions and suspension properties such as change in fibre length and Canadian Standard Freeness, an industrial standard related to pulp dewatering. In the second stage of this methodology, we build upon the work of Forgacs [1] and demonstrate that most paper properties, e.g. the mechanical strength, are related primarily to fibre length and freeness; over 80% of all variation in the data can be attributed to these two parameters. With this novel framework, in conjunction with the statistical models, we demonstrate that an optimum operating condition exist to maximize strength, and demonstrate the sensitivity of this relationship using a number of different type pulps. In the second portion of the thesis, we further develop a novel mechanical pulping process in which multiple stages of LC refining replace the second stage HC refining in a conventional TMP process. This work is motivated from the need to reduce electrical energy consumption to produce mechanical pulp. Using the two-stage methodology developed in the first portion of the thesis, we demonstrate under pilot plant conditions an energy savings of over 20% in comparison to a conventional TMP process to generate mechanical pulp of equal quality.
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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".