ABSTRACT
The term hybrid modeling is often found to be ambiguous and misleading, as it could refer to hybrid discrete-continuous systems, gray-box modeling, semiparametric modeling, multi-scale modeling, block models, and other methodologies. Different additions to the term have been proposed, such as gray-box, neural (network), or semiparametric, to distinguish this approach clearly from other hybrids, yet these additions have raised discussions and concerns themselves.
In essence, the type of a modeling that is referred to in this book as hybrid modeling combines model structures that have been distilled from a priori process knowledge with those whose structure is determined from data.
This chapter provides an intuitive introduction to process modeling via fundamental, data-driven, and hybrid modeling methods to set the stage for the following chapters. A brief introduction is given into the idea and history of hybrid modeling. The key benefits of hybrid modeling are highlighted and illustrated using a simple example. Scenarios are outlined under which hybrid modeling can add value to process operation and design in relation to other modeling approaches, but cases in which other approaches should be preferred are also addressed. The challenges of developing hybrid models and potential problems with their application for operation are described in this chapter, and the stage is set for the following book chapters.
