Modelling of a continuous kneader reactor for the polymerization of partially neutralized acrylic acid
Bibliographic record
Abstract
Abstract A mathematical model and analysis of the continuous polymerization of partially neutralized acrylic acid (AA) in a continuous kneader reactor is presented here as an initial attempt to simulate the synthesis of a superabsorbent polymer. A detailed kinetic model has been used to describe the copolymerization of AA and sodium acrylate (NaA) in aqueous medium. This model is used to describe batch and continuous operations. The polymerization is initiated by a mixture of potassium persulphate (K 2 S 2 O 8 , KPS) and hydrogen peroxide (H 2 O 2 ) as oxidizing agent and ascorbic acid (AsA) as reducing agent. A novel set of kinetic parameters has been estimated by fitting experimental data from different literature sources. The operation of a continuous kneader reactor modelled as a plug‐flow reactor with axial dispersion is theoretically investigated to predict temperature profile, total and individual monomer conversion, consumption of KPS, H 2 O 2 , and AsA, and polymer average molecular weights. The simulation results show the presence of a hot spot close to the reactor entrance that could be potentially severe during startup and could have a detrimental impact on polymer quality. This model is a first step in the direction of achieving optimal operating protocols and exploring improved polymerization reactor designs.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".