Figure 1: (left) normal flooding behavior with increasing band speed (right) spinning band column with a view of the uninsulated column with vacuum double jacket and internal
Besides the already mentioned actuators and sensors, the column is equipped with necessary sensors and actuators to be operated nearly fully automated. The automation is executed on a PLC by WAGO Kontakttechnik GmbH & Co. KG, Minden, Germany, and features the manufacturer independent interface module type package (MTP) standardized in the VDI/VDE/NAMUR 2658 guidelines. Besides this, the distillation column offers a service “distill” with a state machine implemented in the decentralized logic of the modules PLC. Enabled by the MTP, the distillation column can be integrated to the prototype ABB process orchestration layer (POL), where the service can be conducted and the human machine interface (HMI) is automatically visible. Deeper insights of the MTP concept and its architecture can be taken from , the architecture combined with the ML implementation is explained in more detail below.
Regarding the flooding phenomena, the distillation column is behaving similarly to conventional distillation columns. The pressure drop is increasing nearly linearly with the gas factor and liquid loading. Beyond certain gas factors and liquid loadings, called loading point, the pressure drop is increasing more steeply than before until the flooding point is reached, which is an undesirable state of the column., In the case of the spinning band distillation column, the pressure drop is also massively influenced by the spinning band speed. This behavior could be already implemented to a control strategy , but still, the column is running into the undesired state of flooding. The complex hydrodynamics of the system with a rotating internal and the two-phase flow is very hard to model and predict precisely. Hence, the flooding points need to be examined experimentally. This procedure is very time consuming and still has some uncertainties, which cannot be described completely. Hence, it is from high interest to predict the pressure drop and classify the current operating point with help of a trained ML algorithm with historical data. Like this, it would be possible to already get information about a certain parameter set and a classification even before undesired states like the flooding point is reached in operation.
For the implementation of ML scripts, following hard- and software architecture is used. Sensor data from the SBDC is transferred to and stored on the OPC/UA server of the PLC. Current and historical values for each PEA can be accessed by the POL via the MTP interface. In order to implement ML methods to design an early flooding warning system, the sensor data is directly taken from the OPC/UA servers via a Python script and fed to the developed monitoring system to determine the current operating state of the distillation column by classification. The monitoring concept is implemented in four steps: i) pressure drop preprocessing and filtering ii) forecast by supervised learning methods, iii) operating state classification through clustering, iv) graphical output for the operator with forecast and classification. Data flow and implementation of the flooding prevention system are visualized in Figure 2.