On the prediction of suspension viscosity, grain morphology, and agitation power in <scp>SPVC</scp> reactors
Bibliographic record
Abstract
Abstract The role of primary and secondary stabilizers in the suspension polymerization of VCM and their effect on grain morphology (i.e., particle size distribution, grain porosity, and bulk density) is critically discussed. Using the modified Mooney equation, the time‐varying viscosity of the suspension in an SPVC reactor is calculated in terms of the volume fraction of the dispersed monomer/polymer phase, the ratio of the viscosity of the dispersed phase over the viscosity of the continuous aqueous phase, and the maximum packing volume fraction of SPVC grains. A population balance equation is numerically solved to calculate the dynamic evolution of particle size distribution (PSD) in an industrial batch suspension VCM polymerization reactor. A porosity model is postulated to calculate the dynamic evolution of the PVC grain porosity with respect to monomer conversion and the extent of primary particle fusion. Finally, the underlying theory regarding the calculation of the required agitation power in SPVC reactors is detailed. It is shown that the time‐varying viscosity of the suspension can be calculated and the PVC grain morphology (i.e., extent of particle agglomeration) can be accessed via the on‐line estimation of the effective volume fraction of the dispersed phase using on‐line power agitation measurements obtained from an industrial‐scale SPVC reactor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".