A Protocol for the Estimation of Parameters in Process Models: Case Studies with Polymerization Scenarios
Bibliographic record
Abstract
Abstract Summary: As our understanding of chemical processes increases, the complexity of the models developed to describe them also increases. In most cases the equations are nonlinear in the inputs and parameters, and must be solved numerically. At present in estimating parameters for large process models, there are two shortcomings in the existing knowledge about (multiresponse) parameter estimation. The first is, how effective is the present parameter estimation methodology when applied to large models, and the second is, can any advantage be gained from considering the parameter estimation problem as a whole. This paper will address these questions, by revisiting the various steps of a parameter estimation protocol. There is little discussion in the literature as to how all the steps for parameter estimation are related. In the development of this protocol all of the steps for parameter estimation will be revisited: parameter sensitivity analysis, statistical design of experiments, estimation of parameters and confidence regions. By considering all these steps as a whole the overall parameter estimation procedure can be more efficient and some pitfalls, such as local optima and incorrect confidence regions, may be dealt with in an appropriate manner. To illustrate the application of the protocol, two case studies related to polymerization models are presented. These case studies illustrate some of the problems that may be encountered in the parameter estimation process and how the proposed protocol can aid in overcoming them. Plot of conversion versus time for the copolymerization of styrene/methyl methacrylate. image Plot of conversion versus time for the copolymerization of styrene/methyl methacrylate.
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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 | 0.036 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".