Identifiability study of a liquid–liquid phase‐transfer catalyzed reaction system
Bibliographic record
Abstract
Abstract Consistent model parameterization is an important issue when developing mathematical models because it dictates whether parameter values can be estimated uniquely. The problems inherent in model parameterization are presented in an identifiability study of a nonlinear liquid–liquid phase‐transfer reactor model. This reactor model was proposed by Chen et al., in a study, to describe the reaction between organic‐phase benzyl chloride and aqueous‐phase sodium bromide, using tetrabutylammonium as the phase‐transfer catalyst. The model consists of coupled differential and algebraic equations. Existing methods using differential‐algebra for testing identifiability of differential‐algebraic equation (DAE) models are computationally intensive for models with a large number of states and parameters. A new method for testing DAE systems for identifiability was recently proposed by Ben‐Zvi et al. Using this method, the model proposed by Chen et al. is shown to be locally unidentifiable, indicating that it is impossible to uniquely identify all of the model parameters. Two alternatives for simplifying the model to make it identifiable are discussed. © 2004 American Institute of Chemical Engineers AIChE J, 50:2493–2501, 2004
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".