Effect of impurities on continuous solution methyl methacrylate polymerization reactors: open-loop process identification and closed-loop real-time control results
Bibliographic record
Abstract
An automated pilot-scale experimental reactor system with facilities for on-line measurement of process variables has been set up for closed-loop control studies on methyl methacrylate (MMA) polymerization in the presence of impurities. The desirable conversion level is maintained using initiator flow rate as the manipulated variable. Open-loop runs were carried out first to identify process transfer function models. Subsequently, control algorithms were developed and implemented on the reactor system considering variations of the reactive impurities introduced in the feed streams as process uncertainties. Conventional proportional-integral-derivative (PID) control algorithms (e.g., PID, Smith Predictor, Dahlin's control) and stochastic control strategies (e.g, unconstrained minimum variance control (MVC), constrained MVC (CMVC), and one-step optimal control) were developed and evaluated first at the simulation level to identify promising control runs. Then, closed-loop experimental runs followed in a series of six two- to three-day continuous runs to verify the simulation results. This study not only verifies theoretical control designs with closed-loop experimental runs, but also triggers many interesting control issues for practical polymer reactor control.>
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".