Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  aims to estimate the parameters of a complex model representing an industrial scale polymerization process. The estimability analysis of the parameters prior to estimation allows simplifying the optimization problem but it is usually neglected in literature when industrial data is used for estimation. In this case, though, the estimability analysis would be even more important since usually less data is available, they are associated with a higher uncertainty and the experiments might not be designed as in laboratory or pilot plant. The orthogonalization method reduced from 68 to 29 the number of parameters of the model. Polymer properties, which are measured offline with low frequency, as ll as process temperatures and flow rates are used for validating the model. Small deviations, up to 5%, beten model prediction and experimental data indicate the quality of fit of the model and the importance of carrying out first an estimability analysis.
['Idelfonso Nogueira', 'Karen Valverde Pontes']
I am writing a research paper on a idea Parameter estimation with estimability analysis applied to an industrial scale polymerization process
[{"from": "human", "value": "I am writing a research paper on a idea Parameter estimation with estimability analysis applied to an industrial scale polymerization process"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  aims to estimate the parameters of a complex model representing an industrial scale polymerization process. The estimability analysis of the parameters prior to estimation allows simplifying the optimization problem but it is usually neglected in literature when industrial data is used for estimation. In this case, though, the estimability analysis would be even more important since usually less data is available, they are associated with a higher uncertainty and the experiments might not be designed as in laboratory or pilot plant. The orthogonalization method reduced from 68 to 29 the number of parameters of the model. Polymer properties, which are measured offline with low frequency, as ll as process temperatures and flow rates are used for validating the model. Small deviations, up to 5%, beten model prediction and experimental data indicate the quality of fit of the model and the importance of carrying out first an estimability analysis."}]