Modelling and Parameter Estimation of a PO3G Polyether Process
Bibliographic record
Abstract
This thesis focuses on developing advanced fundamental models for production of polytrimethylene ether glycol (PO3G) from bio-based 1,3 propanediol. These models describe the time-varying concentrations of monomer, oligomers, end-groups and by-products (i.e., unsaturated ends, water, and propanal) during PO3G production in a batch reactor system with an overhead condenser. A comprehensive dataset from industrial sponsor, E. I. du Pont Canada, is used to support parameter estimation and model validation. Using model predictions and the available data, the current research provides a better understanding about the influences of process operating conditions on PO3G production rate and product properties. Novel probability factors are developed to permit simplification of model equations when accounting for the complex influence of super-acid catalyst on the polycondensation rate. The model is extended through multiple steps to account for: i) the dynamic behaviour of the condenser, ii) the inhibitory influence of water on polycondensation kinetics, iii) formation, consumption, and evaporation of cyclic oligomers, and iv) the effects of temperature. Model parameters are ranked from most-estimable to least-estimable using orthogonalization-based parameter-ranking techniques, and a mean-squared-error criterion is used to determine which parameters are estimable. Parameter estimation is performed using industrial data obtained from eight batch-reactor runs at temperatures ranging from 160 to 180 ̊C and using super-acid catalyst levels from 0.10 to 0.25 wt%. The resulting PO3G models and parameter estimates provide good predictions of industrial data and will be useful for selecting operating conditions for commercial PO3G production. The PO3G models (along with the current parameter estimates) can also be used to select the operation settings for future experimental runs, which will produce more reliable parameter estimates and consequently, more accurate model predictions. The author recommends using a sequential Bayesian model-based design of experiment (MBDOE) approach when designing new PO3G experiments. This method is recommended because Monte-Carlo simulations results reveal that new parameter estimates obtained using the designed experiment will be more accurate, on average, compared to parameter estimates obtained using new experiments selected from among the corners of the permissible design space.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".