Conclusion and Outlook
The application of machine and deep learning in the process industry is
an adequate way to predict or detect the flooding behavior in laboratory
distillation and extraction columns. It was shown that both time-series
data of process values and image recognition can be used for modelling.
Parallel to this, examples were given, which enable the simple
integration of AI-based monitoring systems into existing plants enabled
by existing control architectures such as Module Type Package MTP. An
adaptation of camera setups or the existing data structures, such as
OPC-UA, are sufficient to provide an interface for a data science
implementation. This results in a high potential for tooling up existing
equipment with AI methods as part of the digital twin. It is possible to
combine both analytical methods in order to specify the flooding
behavior even more and to transfer the flooding detection from an
AI-supported to an AI-controlled monitoring system.