A simplified kinetic model for modern cooking of aspen chips
Bibliographic record
Abstract
Abstract Kraft pulping kinetic models are an important component of any fundamental continuity based continuous digester model. These models can be used to further develop our understanding, or as a framework to support the development of real-world control, estimation and optimization strategies. Effective models are tailored to a specific species of wood and must be applicable for a wide range of expected cooking conditions. In this work a series of experiments were conducted on hardwood Aspen chips for 5 different cooking conditions. Each series of cooks were interrupted at different time intervals to capture the dynamic response of the cook. The key chips and liquor components were measured and reconciled at each interval. A dynamic model was then developed based on a simplification of a continuous digester model under batch conditions. This ensures continuity between the key assumptions governing both the batch kinetics model and the expanded continuous form. A kinetic model structure was adapted from the literature that quantifies all three accepted phases of lignin and cellulose degradation, i. e. the initial, bulk and residual phases as well as the effect of modern cooking practices such as intra-cook white liquor addition on the transition between the bulk and residual phases. Additionally, modifications were made to the kinetic model structure to reduce the overall number of states and to impose a floor limit on degradation, thereby reducing the overall complexity and computational burden. The model was then fit to the data using weighted least squares and simulation optimization techniques.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".