Using the Quasi-Steady-State Approximation to Reduce the Computation Time of the Polymeric Flow Model for Olefin Homopolymerization
Bibliographic record
Abstract
In this article we propose a novel way to solve the Polymer Flow Model (PFM) using the quasi steady-state approximation (QSSA). The accuracy of the QSSA model was quantified by comparing polymerization rates, particle growth factors, mass transfer efficiencies, and molecular weight distributions simulated at different Thiele modulus values with the predictions from two different non steady-state (dynamic) solutions of the PFM. The QSSA model predictions become increasingly more accurate after the first few minutes of polymerization for catalysts with low to medium Thiele moduli. Even though the proposed QSSA method deviates more significantly from the PFM dynamic solution at high Thiele modulus values, from a practical point of view this is of little relevance since this condition corresponds to high levels of mass transfer limitations that are avoided in industrial processes. Finally, the computation time of the QSSA method is drastically faster than those of dynamic models, making the proposed approach ideal for integration with large-scale industrial models that must describe whole polymer particle populations instead of single particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".