Machine and Deep Learning Approaches for Flooding Prevention in
Distillation and Extraction Columns
Abstract
Machine Learning (ML) algorithms can be combined with the modular
automation protocol (MTP) and recognize the flooding behavior of
laboratory fluids separation columns. Hence, artificial intelligence
(AI) tools with deep learning (DL) offer a high potential for the
process industry and allow to capture operating states that are
otherwise difficult to detect or model. However, the advanced methods
are only hesitantly applied in practice. This article provides an
overview on how artificial intelligence-based algorithms can be
implemented in existing laboratory plants. Process sensor data as well
as image data are used to model the flooding behavior of distillation
and extraction columns and the system is adapted to the existing modular
automation standard of the Module Type Package (MTP).