6. Conclusion
This paper presents how the estimability analysis allows parameter estimation simplification of a complex phenomenological model representing an industrial scale process. Previous studies in literature have already dealt with the preliminary analysis of the parameters, but they use experimental data obtained from laboratory or pilot plants. Using experimental data obtained from process history presents some challenges notfaced with laboratory or pilot plant experiments: less information is available, the measurements have more uncertainties and the experiments might not be designed. The estimability analysis based on the orthogonalization method allowed determining the set of estimable parameters of a phenomenological model representing an industrial scale copolymerization process. The number of parameters to be estimated was reduced from 68 to 29, ensuring a successful estimation. The Hessian matrix analysis indicates that the space formed by these parameters is not strictly convex, hence a local minimum might be found. The estimability analysis was then repeated, indicating a positive definite Hessian matrix. The parameter estimation was followed by the model validation with a different data set, representative of another polymer grade. The small deviations between the model prediction and the experimental data, both from the estimation and validation data sets, indicate the quality of fit of the model.
