Modeling of Infrared Drying of Polymer Solutions
Bibliographic record
Abstract
This work presents the dynamic modeling of drying behavior of polymer solutions in an infrared-convective oven. Two study cases were considered for the drying process. The first one deals with the drying of a coated polymer solution on a fixed substrate while the second one includes drying of the same solution on a moving substrate in an infrared (IR) oven. Both models involve simultaneous heat and mass transfer equations that describe changes in the solvent concentration and the polymer temperature during the drying process. The set of partial differential equations (PDEs) arising from the mass and energy balances constitute a highly nonlinear system due to inter-dependence of the thermodynamic and transport properties of polymer solutions. The models were numerically solved and were validated using published experimental data. The models were employed to simulate the drying of a polyvinyl acetate coating (in toluene) on a polyester substrate. Results obtained from the derived model demonstrated the importance of parameters such as web velocity, heater temperature, and inlet air velocity in the IR drying process. In general, high temperature and air velocity cause rapid drying of the polymer coating, while high substrate velocity resulted in drying. This model can be applied on any industrial applications that include continuous IR drying process of polymer-coated layers to predict the drying behavior of the coated product.
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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) | 0.001 | 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".