In order to address issues related with process operation, diagnosis, optimization, improvement and control, among other tasks, several kinds of models have been used. They usually fall under the scope of two distinct paradigms: mechanistic first-principles and empirical approaches. Both have been adopted but very few frameworks were developed so far in order to combine and integrate features from each one of them into hybrid models, which share mechanistic and empirical components. In this article we describe a methodology for overcoming this lack of integration efforts, through an algorithm that leads to the construction of process models that contain mechanistic and localized empirical elements, achieved by exploring symbolic manipulation of the first-principles model equations. This new framework was tested and evaluated by application to a simulated CSTR case study.
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