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Record W6992882234

Modelling and Parameter Estimation of a PO3G Polyether Process

2021· dissertation· en· W6992882234 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueQSpace (Queen's University Library) · 2021
Typedissertation
Languageen
FieldComputer Science
TopicChemical and Environmental Engineering Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacsQueen's University
KeywordsEstimation theoryProcess (computing)Production (economics)Process variableModel parameterEvaporationExperimental dataProcess modeling
DOInot available

Abstract

fetched live from OpenAlex

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.
\nNovel 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%.
\nThe 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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.191
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it