Simulation of continuous solid‐phase polymerization of nylon 6,6. III. Simplified model
Bibliographic record
Abstract
Abstract Solid‐phase polymerization (SPP) reactors are used to increase the degree of polymerization (DP) during nylon 6,6 production. In previous articles, a reactor model with partial differential equations (PDEs) in time and two spatial dimensions was developed to describe dynamic changes in polymer property profiles (DP, temperature, and moisture content) over the height of the reactor and within the polymer particles. In the current article, a simplified model is developed by deriving appropriate expressions for heat‐ and mass‐transfer coefficients and performing a lumped heat‐ and mass‐transfer analysis. Using this approach, the radial dimension is removed from the PDEs, so that the effort required to solve the model equations is substantially reduced. Predictions of the complex and simplified models are compared through simulation of two different start‐up processes. Good agreement between simplified and complex models is obtained, indicating that the simplified model can be used in place of the complex model if the polymer properties profiles within individual particles are not of particular concern to the model user. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 3701–3712, 2003
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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) | 0.003 | 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".