Modeling, Simulation and Optimal Control of Ethylene Polymerization in Non-Isothermal, High-Pressure Tubular Reactors
Bibliographic record
Abstract
Low-density polyethylene (LDPE) is a very important polymer, which is usually produced through a free radical polymerization process in high-pressure tubular reactors. In this study, the mathematical model of this process is developed on the basis of a comprehensive reaction mechanism to accurately determine polymerization rate, and polymer properties under extreme temperature conditions. The model comprises of (i) the mass balances of monomer, initiator, solvent, live radicals and dead polymer chains, and (ii) the energy balances of a tubular LDPE reactor, and its jacket under steady state condition. The model considers the variations in the density and viscosity of reactants along reactor length. Computer simulation of the model is successfully carried out to accurately describe the performance of a non-isothermal, high-pressure, industrial LDPE reactor with multiple initiator injections. Based on the developed non-linear model, the optimal control of the industrial reactor is carried out using genetic algorithms to maximize monomer conversion using jacket temperature as a control function of reactor length. The application of optimal control leads to significant improvements in the final monomer conversion, and capacity utilization of the reactor.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".