A new approach for sequential experimental design for model discrimination

Model discrimination procedures are useful tools for selection of the best mathematical models to be used to represent a specific chemical process. The present paper presents and discusses a new sequential discrimination procedure, which makes use of model probabilities and concentrates the efforts on models with higher probabilities. Model probabilities are determined based on simple statistical arguments. Four numerical examples illustrate the application of the proposed discrimination procedure. 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. 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.

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