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** Determination of Optimal Inputs for Precise Parameter Estimation and Model Discrimination **

Froment, G.F., "Model Discrimination and Parameter Estimation in Heterogeneous Catalysis", AIChE. 1, 21 1041 (1975). [Pg.395]

Because of these limitations, different models may appear to describe the unit operation equally well. Analysts must discriminate among various models with the associated parameter estimates that best meet the end-use criteria for the model development. There are three principal criteria forjudging the suitability of one model over another. In addition, there are ancillary criteria like computing time and ease of use that may also contribute to the decision but are not of general concern. [Pg.2578]

If instead of precise parameter estimation, we are designing experiments for model discrimination, the best grid point of the operability region is chosen by maximizing the overall divergence, defined for dynamic systems as [Pg.200]

Fig. 28. General methods of selecting model discrimination or parameter estimation designs. |

Determination of Optimal Inputs for Precise Parameter Estimation and Model Discrimination [Pg.200]

Two examples are provided here to illustrate nonlinear parameter estimation, model discrimination, and analysis of variance. [Pg.119]

Note, however, that, in the case of fundamental models, there is not always a need to discriminate among rival models since, often, only a single model has been built up. Furthermore, the best criterion of the quality of a model is the consistency of fundamental parameter estimates with other values obtained by means of several methods under a large range of experimental conditions. Let us not be misled about the principle enemy the systematic errors both in experiments and in reaction and reactor models. [Pg.316]

The sequential experimental design can be made either for precise parameter estimation or for model discrimination purposes. [Pg.196]

Hill, W. J., W. G. Hunter, and D. W. Wichern. A joint design criterion for the dual problem of model discrimination and parameter estimation. Technometrics, [Pg.136]

Parameter estimation is a procedure for taking the unit measurements and reducing them to a set of parameters for a physical (or, in some cases, relational) mathematical model of the unit. Statistical interpretation tempered with engineering judgment is required to arrive at realistic parameter estimates. Parameter estimation can be an integral part of fault detection and model discrimination. [Pg.2572]

With this book the reader can expect to learn how to formulate and solve parameter estimation problems, compute the statistical properties of the parameters, perform model adequacy tests, and design experiments for parameter estimation or model discrimination. [Pg.447]

Estimation of parameters. Model parameters in the selected model are then estimated. If available, some model parameters (e.g. thermodynamic properties, heat- and mass-transfer coefficient, etc.) are taken from literature. This is usually not possible for kinetic parameters. These should be estimated based on data obtained from laboratory expieriments, if possible carried out isothermal ly and not falsified by heat- and mass-transport phenomena. The methods for parameter estimation, also the kinetic parameters in complex organic systems, and for discrimination between models are discussed in more detail in Section 5.4.4. More information on parameter estimation the reader will find in review papers by Kittrell (1970), or Froment and Hosten (1981) or in the book by Froment and Bischoff (1990). [Pg.234]

This paper describes the procedure and criteria used to evaluate commercially available software packages for kinetic modeling, and their capabilities for parameter estimation, model discrimination and design of experiments. Also the ease of use and other user-friendliness aspects receive attention. [Pg.632]

Procedures on how to make inferences on the parameters and the response variables are introduced in Chapter 11. The design of experiments has a direct impact on the quality of the estimated parameters and is presented in Chapter 12. The emphasis is on sequential experimental design for parameter estimation and for model discrimination. Recursive least squares estimation, used for on-line data analysis, is briefly covered in Chapter 13. [Pg.448]

The use of computers has made it possible to characterise models with large numbers of individual steps. Andersson and Lamb [25] used an analogue computer to estimate parameters in a model with 15 reactions which described naphthalene production by hydrodealkylation. Also, they were able to predict temperature distributions and effluent concentrations for a commercial reactor. Kurtz [26] took 200 simultaneous reactions into account in an experimental study of the gas-phase chlorination of methyl chloride. Model discrimination and parameter estimation for catalytic processes are discussed in a comprehensive review by Froment [27]. [Pg.126]

However, this particular experimental design only covered values of x3 up to 1.68 consequently, the saddle point is only predicted by the model and not exhibited by the data. This is the reason the lack-of-fit tests of Section IV indicated neither model 3 nor model 4 of Table XVI could be rejected as inadequately representing the data. As is apparent, additional data must be taken in the vicinity of the stationary point to confirm this predicted nature of the surface and hence to allow rejection of certain models. This region of experimentation (or beyond) is also required by the parameter estimation and model discrimination designs of Section VII. [Pg.157]

** Determination of Optimal Inputs for Precise Parameter Estimation and Model Discrimination **

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