Identifiability study of a liquid–liquid phase‐transfer catalyzed reaction system

Ben-Zvi, Amos; McAuley, Kimberley B.; McLellan, James A.

doi:10.1002/aic.10202

Cited by 14 publications

(14 citation statements)

References 18 publications

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“…Many models that appear in the engineering literature have multiple types of response variables (e.g., temperatures, pressures, concentrations, yields). If the model form is complicated, or the data available are not sufficiently informative, estimating all of the unknown parameters may be very difficult or even impossible (e.g., Ben-Zvi et al, 2004;Kou et al, 2005a,b). In these situations, there are often many competing SMs that could be used, depending on the simplifying assumptions that are made and the subset of parameters that are to be estimated (Thompson et al, 2007(Thompson et al, , 2009Chu and Hahn, 2008;Lund and Foss, 2008).…”

Section: Extension To Selection Of Multivariate Modelsmentioning

confidence: 99%

Selection of simplified models: II. Development of a model selection criterion based on mean squared error

McAuley

Harris

2011

Can J Chem Eng

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Simplified models (SMs) with a reduced set of parameters are used in many practical situations, especially when the available data for parameter estimation are limited. A variety of candidate models are often considered during the model formulation, simplification, and parameter estimation processes. We propose a new criterion to help modellers select the best SM, so that predictions with lowest expected mean squared error can be obtained. The effectiveness of the proposed criterion for selecting simplified nonlinear univariate and multivariate models is demonstrated using Monte-Carlo simulations and is compared with the effectiveness of the Bayesian Information Criterion (BIC).Des modèles simplifiés (MS) avec ensemble réduit de paramètres sont utilisés dans de nombreuses situations pratiques, particulièrement lorsque les données disponibles pour l'estimation des paramètres sont limitées. Divers modèles candidats sont souvent examinés durant la formulation du modèle, la simplification et l'estimation des paramètres. Nous proposons un nouveau critère pour aider les modélisateursà sélectionner le meilleur MS et obtenir des prédictions porteuses de l'erreur quadratique moyenne la plus faible. L'efficacité du critère proposé pour la sélection des modèles simplifiés non linéairesà variable uniques et multiples est démontrée en utilisant les simulations Monte-Carlo et comparéeà l'efficacité du critère bayesian des informations (BIC).

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Section: Extension To Selection Of Multivariate Modelsmentioning

confidence: 99%

Selection of simplified models: II. Development of a model selection criterion based on mean squared error

McAuley

Harris

2011

Can J Chem Eng

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“…Models (or Systems) that have parameters that cannot be estimated, even under ideal conditions, are unidentifiable. Tests for unidentifiability have been proposed in the literature for a wide variety of linear and nonlinear systems 5, 6. Typically, a system can be tested for unidentifiability a priori because unidentifiability is a structural fault in the model formulation.…”

Section: Introductionmentioning

confidence: 99%

Electroactive mixtures of polyaniline and EPDM rubber: Chemical, thermal, and morphological characterization

Domenech

Bortoluzzi

Soldi

et al. 2002

J of Applied Polymer Sci

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ABSTRACT:A new route for blending polyaniline (PAni) and EPDM rubber was devised with maleic anhydride as a compatibilizer precursor. Rubber matrices containing ammonium peroxidisulfate and dodecyl benzene sulfonic acid were cast from organic solvents. Exposure to the monomer vapors allowed the chemical polymerization of aniline. The influence of PAni and the compatibilizer on the thermal properties, chemical structure, electrical conductivity, and morphology of the mixtures was observed with differential scanning calorimetry, thermogravimetric analysis, Fourier transform infrared, in situ conductivity measurements, and optical microscopy. The micrographs showed a good distribution of the PAni complex in the matrix. The use of approximately 2 wt % compatibilizer resulted in chemical interactions between maleic anhydride and the PAni complex formed with dodecyl benzene sulfonic acid, which could enhance the compatibility between the polymers. The obtained blends reached relative electrical conductivity values of up to 9 ϫ 10 Ϫ3 S cm Ϫ1 .

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“…Estimability problems can be addressed by conducting additional informative experiments or by simplifying the model 33 . In chemical, biochemical and pharmacological systems, models often contain a large number of kinetic and transport parameters (e.g., 10–80 parameters) which may result in noninvertible/ill‐conditioned FIM s 35‐39 . To avoid this problem, several approaches have been considered during sequential MBDoE calculations including parameter‐subset selection, 14,40,41 pseudoinverse methods, 25,42 Tikhonov regularization, 43‐46 and Bayesian approaches 13,47,48 …”

Section: Introductionmentioning

confidence: 99%

Using prior parameter knowledge in model‐based design of experiments for pharmaceutical production

Shahmohammadi

McAuley

2020

AIChE Journal

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Sequential model-based design of experiments (MBDoE) uses information from previous experiments to select new experimental conditions. Computation of MBDoE objective functions can be impossible due to a noninvertible Fisher information matrix (FIM). Previously, we evaluated a leave-out (LO) approach that designed experiments by removing problematic model parameters from the design process. Unfortunately, the LO approach can be computationally expensive due to its iterative nature. In this study, we propose a simplified Bayesian approach that makes the FIM invertible by accounting for prior parameter information. We compare the proposed simplified Bayesian approach to the LO approach for sequential A-optimal design. Results from a pharmaceutical case study show that the proposed approach is superior, on average, for design of experiments. We suggest that simplified Bayesian MBDoE should be combined with a subset-selection-based approach for parameter estimation. This combined methodology gave the best results on average for the case study.

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Identifiability study of a liquid–liquid phase‐transfer catalyzed reaction system

Cited by 14 publications

References 18 publications

Selection of simplified models: II. Development of a model selection criterion based on mean squared error

Selection of simplified models: II. Development of a model selection criterion based on mean squared error

Electroactive mixtures of polyaniline and EPDM rubber: Chemical, thermal, and morphological characterization

Using prior parameter knowledge in model‐based design of experiments for pharmaceutical production

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