ON THE IDENTIFICATION OF APPROXIMATE MODELS

The problem of the identification of models defined by complex systems is frequently encountered in chemical engineering. Due to convergence difficulties and/or an excessive computational burden, simplified mod els are often employed. Similarly approximate models are used in a pre liminary phase for the estimation of the most significant parameters. In these cases the residuals (i.e. the difference of the experimental data to the values predicted by the approximate model) do not belong t o a well-defined distribution function. Thus the usual regression meth ods, such as those based on maximum likelihood, can sometimes lead to seriously biased estimates. A new regression technique for avoiding ei ther under- or overestimation due to compensation or cumulation of exp erimental errors and model deviations is presented in this paper. Copy right (C) 1996 Elsevier Science Ltd