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

For better model discrimination and/or parameter estimation, sequential methods for computer designed plans of experiments have been proposed [52], They take advantage of the information obtained from the previous experiments and plan the new experiments in the region of independent variables where the maximum difference of the dependent variable can be expected. [Pg.568]

Froment, G. "Model discrimination and parameter estimation in heterogeneous catalysis", A.I.Che. Journal, 21,1041 (1975) [Pg.520]

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

The three principal criteria of model discrimination and parameter estimation are [Pg.29]

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

A. Irabien, I. Ortiz, and E. S. Perez de Ortiz, Kinetics of metal extraction. Model discrimination and parameter estimation, Chem Eng Process 27 13-18 (1990). [Pg.222]

Hill, W. J., W. G. Hunter, and D. W. Wichern, A joint criterion for the dual problem of model discrimination and parameter estimation, Technometrics, 10, 145-160 (1968). [Pg.257]

The MULTILOG 7 software program (Thissen 1991) was used to fit a graded IRT response model (Samejima 1969) estimating item discrimination and location parameters (Embretson and Reise 2000) for each HSOPSC item. SPSS software (PASW 18.0) was used to calculate correlations between mean scale scores and corresponding IRT scale scores. [Pg.168]

Utilization of LHHW and/or power function models together with linear and nonlinear regression techniques to fit rate data for purposes of model discrimination and parameter estimation [Pg.28]

The rate equations were determined by Dumez and Froment by means of sequentially designed experimental programs for model discrimination and parameter estimation, discussed and illustrated in Chapter 2. The following equations were obtained [Pg.617]

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]

In a kinetic investigation, the rate-determining step and, hence, the functional form of the rate model are not known a priori also unknown are the rate constants and adsorption equilibrium coefficients. Hence, the aim of data procurement and correlation is both model discrimination and parameter estimation which are completed in tandem [17]. The critical problem at this point is to obtain reliable experimental data from which kinetic models that reflect steady-state chemical activity can be extracted and evaluated. In order to measure correctly the rates of chemical events only, (i) external and internal mass and heat transport resistances at the particle scale have to be eliminated, [Pg.28]

In a kinetic investigation it-is not known a priori which is the rate-controlling step and therefore the form of the rate equation or the model. Also unknown, of course, are the values of the rate coefficient k and of the adsorption coefficients Kyf, Kk, As,..., or, in other words, of the parameters of the model. A kinetic investigation, therefore, consists mainly of two parts model discrimination and parameter estimation. This can ultimately only be based on experimental results. [Pg.106]

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]

Reaction mechanism for the formation of DME by dehydration of methanol over H-ZSM-5 catalyst. (H" ) acidic site, MeOH (+) protonated methanol (methoxonium ion), /R, ) surface methoxy species, DMO" ) protonated DME (dimethyloxonium ion). Adapted from Park T-Y, Froment CF. Analysis of fundamental reaction rates in the methanol-to-olefins process on ZSM-5 as a basis for reactor desigfi and operation. Ind Eng Chem Res 2004 43 682-9 Park F-Y, Froment CF. Kinetic modelingofthe methanol to olefins process. 2. Experimental results, model discrimination, and parameter estimation. Ind Eng Chem Res 2001 40 4187-96 Park F-Y, Froment CF. Kinetic modelingofthe methanol to olefins process. 1. Model formulation. Ind Eng Chem Res 2001 40 4172-86. [Pg.202]

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

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