Operating Regime Based Process Modeling and Identi - cation

This paper presents a non-linear modeling framework that supports model development \in between" empirical and mechanistic modeling. A model is composed of a number of local models valid in di erent operating regimes. The local models are combined by smooth interpolation into a complete global model. It is illustrated how di erent kinds of empirical and mechanistic knowledge and models can be combined with process data within this framework. Furthermore, we describe a exible computer aided modeling tool that supports modeling within this framework. Simple but illustrative examples from chemical engineering are used to highlight the exibility and power of the framework.