Polymer Reactor Modeling, Design and Monitoring
Bibliographic record
Abstract
Preface to "Polymer Reactor Modeling, Design and Monitoring"Polymers range from synthetic plastics, such as polyacrylates, to natural biopolymers, such as proteins and DNA.The large molecular mass of polymers and our ability to manipulate their compositions and molecular structures have allowed for producing synthetic polymers with attractive properties.As such, synthetic polymers have been increasingly used in a large number of applications such as paints, coatings, fibers, flexible films, automotive parts, adhesives, fuel cells, batteries, medicine, and controlled drug delivery.For example, only in 2013, around 299 million tons of plastics were produced, and this level has increased since then.Worldwide polymer production is expected to grow; as polymers steadily replace materials such as glass, wood and metals, our understanding of polymers improves, and new polymers with remarkable characteristics are synthesized.Because of the huge production volume of commodity polymers, a little improvement in the operation of commodity-polymer processes can lead to significant economic gains.On the other hand, a little improvement in the quality of specialty polymers can lead to substantial increase in economic profits.This Special Issue includes papers that investigate different approaches to improving polymers, polymer processes, and processes that use polymers.These approaches include state and parameter estimation in polymerization reactors, polymerization reactor modeling for process monitoring, polymerization reactor monitoring, design of optimal polymerization experiments, use of polymeric membranes in integrated gasification combined cycle units, model-based design of polymerization reactors, and polymerization reactor modeling.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".