Journal ArticleDOI
Designing experiments for precise estimation of all or some of the constants in a mechanistic model
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TLDR
In this paper, statistical design procedures for estimating all the constants in a mechanistically based mathematical model are reviewed and a new procedure is illustrated for precisely estimating only some constants which may be of more interest than the others.Abstract:
An important experimental problem in chemical engineering is to collect data in order to estimate constants such as rate constants and activation energies which are needed, for example, in equipment and plant design. For a given amount of experimental effort, the engineer wants these constants to be estimated as precisely as possible. In this paper, we discuss statistical design procedures that might be used to accomplish this goal. Available procedures are reviewed for estimating precisely all the constants in a mechanistically based mathematical model. In addition, a new procedure is illustrated for precisely estimating only some of the constants which may be of more interest than the others.
Un important probleme experimental, dans le domaine du genie chimique, est de recueillir des donnees pour estimer les constantes (comme celles qui ont trait a la vitesse et aux energies d'activation) qui sont necessaires, par exemple, lors de la conception de l'outillage et de l'usine. Pour un certain degre de travail experimental, l'ingenieur desire determiner ces constantes avec autant de precision que possible; on traite, dans le present travail, des procedes de calcul statistique qu'on peut utiliser pour atteindre ce but. On examine les procedes disponibles pour determiner avec precision toutes les constantes dans un modele mathematique du genre mecanistique On illustre egalement un nouveau procede pour estimer avec precision quelques constantes seulement qui peuvent presenter un interet parti-culier.read more
Citations
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Journal ArticleDOI
Model-based design of experiments for parameter precision: State of the art
TL;DR: An overview and critical analysis of the state of the art in this sector are proposed and the main contributions to model-based experiment design procedures in terms of novel criteria, mathematical formulations and numerical implementations are highlighted.
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Recent Advances in Nonlinear Experimental Design
TL;DR: In this article, the authors summarized recent work in optimal experimental design in nonlinear problems, in which the major difficulty in obtaining good or optimal designs is their dependence on the true value of the parameters.
Journal ArticleDOI
Designing experiments to understand the variability in biochemical reaction networks.
TL;DR: An optimal experimental design framework is proposed which is employed to compare the utility of dual-reporter and perturbation experiments for quantifying the different noise sources in a simple model of gene expression and it is shown that well-chosen gene induction patterns may allow one to identify features of the system which remain hidden in unplanned experiments.
Journal ArticleDOI
A Total Entropy Criterion for the Dual Problem of Model Discrimination and Parameter Estimation
Book ChapterDOI
Design of Experiments for Estimating Enzyme and Pharmacokinetic Parameters
TL;DR: The principles of optimization are applied to simple enzyme and pharmacokinetic models, and designs yielding the most precise parameters are described, which provide guidelines, and not rules, for experimentation.
References
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Robert Hooke,T. A. Jeeves +1 more
TL;DR: The phrase "direct search" is used to describe sequential examination of trial solutions involving comparison of each trial solution with the "best" obtained up to that time together with a strategy for determining (as a function of earlier results) what the next trial solution will be.
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George E. P. Box,H. L. Lucas +1 more
Journal ArticleDOI
Discrimination Among Mechanistic Models
George E. P. Box,William J. Hill +1 more
TL;DR: To discriminate among these a sequential procedure is developed in which calculations made after each experiment determine the most discriminatory process conditions for use in the next experiment.