Choosing the right model: Case studies on the use of statistical model discrimination experiments

Statistical model discrimination methods were developed to efficiently and reliably choose the 'best' model for a system from a set of candidate models. Three promising model discrimination techniques are compared using three chemical engineering examples in this paper. The examples were studied via computer simulations in which experimental data were generated using a known model. The use of a computer simulation allowed factors such as error magnitude to be studied at different levels in repeat runs of the program. The results indicated the exact entropy method is the best method for use with non-nested nonlinear models, while the Buzzi-Ferraris and Forzatti (1983) method is best for use with nonlinear nested models.