The parameter estimation is an unavoidable procedure regarding to process modeling and simulation in order to adjust these models to the experimental data. However, sometimes the number of parameters to be estimated during the modeling is too high, and statistically inconsistent, since it would demand an enormous number of experiments to provide enough degrees of freedom to make those estimations. To overcome this problem, the parameters identifiability is commonly used. It consists in the selection of a subset of parameters for estimation according to their influence over the states, and this influence is measured through the analysis of the parameters sensibility matrix. In this work it is presented an analysis of the parameters sensibility matrix and the parameters estimated through their idenfiability.