Numerical investigation of continuous processes for catalytic hydrogenation of nitrile butadiene rubber
Bibliographic record
Abstract
Abstract Dynamic behavior of continuous processes was numerically investigated for the catalytic hydrogenation of nitrile butadiene rubber, based on developed models, which took into account the coupling between kinetics and mass transfer. The evolution of hydrogenation reaction trajectories in both cases were analyzed. It is proposed that the coupling behavior between the catalytic hydrogenation and mass transfer was completely determined by the ability of the catalyst in activating hydrogen, carbon‐carbon double bond loading level and the relative capacity of reaction to mass transfer as well as the residence time in the reactor. Four dimensionless parameters were derived to characterize these aspects. The effects of operation conditions on the hydrogenation processes were investigated. The application of the ideal flow models to non‐ideal flows was in addition discussed. It is suggested that the optimal reactor for such a hydrogenation system would be a plug flow reactor with an instantaneous well‐mixing component in the inlet of it, and a reasonable approach to the proposed optimal reactor should be with the flow behavior of at least three continuous stirred tank reactors in series. Further research directions are suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".