Sentence examples for model discrimination procedures from inspiring English sources

Model discrimination procedures are useful tools for selection of the best models to be used to represent a specific process.

Model discrimination procedures are useful tools for selection of the best mathematical models to be used to represent a specific chemical process.

From model discrimination procedures, the most suitable model was found to be a Langmuir-Hinshelwood model, involving associative adsorption of cyclohexanol, cyclohexanone and hydrogen on the same kind of sites.

The kinetic models discrimination procedure leads to a reaction rate expression based on the SO2 adsorption on a nonideal surface as the rate-controlling step, in terms of an exponential influence of the calcium conversion in the reaction rate.

Furthermore, the speed of the proposed discrimination procedure can be controlled by tuning a design parameter which reflects the analyst's mood (confidence) towards the discrimination problem and allows for increase or decrease of the number of experiments required for model discrimination during the sequential procedure.

Experimental design procedures for model discrimination and for estimation of precise model parameters are usually treated as independent techniques.

Buzzi-Ferraris, P. Forzatti, G. Emig, H. Hofmann, Sequential experimental design procedure for model discrimination in the case of multiple responses.

The whole procedure for model discrimination is firstly described in detail and then, applied to a numerical study case, consisting of a chemical synthesis carried out in a microreactor.

The relationship with other design procedures for parameter estimation and model discrimination is discussed.

The obtained results indicate that the new procedure is able to discriminate kinetic models with fewer experiments when compared to other procedures and also indicates when model discrimination is not possible and, thus, when the sequential design must be halted.

In our approach to data-supported computational modelling of biological networks, we take the view that one should follow an iterative procedure that includes model identification (model fitting), model discrimination (in which a new experiment is designed) and model invalidation (using the new experimental data).