In the core of many process systems engineering tasks, like design, control, optimization and fault diagnosis, a mathematical model of the underlying plant plays a key role. Such models are so important that extensive studies are available, recommending different modeling techniques to be adopted for specific processes or goals. It is usual and practical to split modeling techniques under two main groups: mechanistic methods and empirical or statistical methods. Both paradigms have been adopted, but very few frameworks were developed to combine and integrate features from both of them. In this article we describe a framework for data-driven evolution of static mechanistic models with a selective inclusion of simple empirical terms. To illustrate its practical potential, our framework is applied to the identification of a non-ideal reactor and to the optimization of the Otto-Williams benchmark reactor.

No abstract

No abstract