  Making an updated and as-built model plays an important role in the
life-cycle of a process plant. In particular, Digital Twin models must be
precise to guarantee the efficiency and reliability of the systems. Data-driven
models can simulate the latest behavior of the sub-systems by considering
uncertainties and life-cycle related changes. This paper presents a
step-by-step concept for hybrid Digital Twin models of process plants using an
early implemented prototype as an example. It will detail the steps for
updating the first-principles model and Digital Twin of a brownfield process
system using data-driven models of the process equipment. The challenges for
generation of an as-built hybrid Digital Twin will also be discussed. With the
help of process history data to teach Machine Learning models, the implemented
Digital Twin can be continually improved over time and this work in progress
can be further optimized.
