Figure 3: Sliding window method for the transformation of time series data.
This transformation results in many additional data columns, which must be considered for the choice of an appropriate ML model. For the de-noising of the pressure drop signal, an exponentially weighted moving average (EWMA) is used that weighs the most recent measurements stronger as they are more important to detect changes in trend and level of the pressure drop. For some ML methods that are based on distance (SVM, Clustering), an additional scaling step is necessary that normalizes the input data. Linear regression and decision tree regressors do not require this scaling step.

Feature extraction via machine learning ML

The spinning band distillation column contains the following sensors and control variables summarized in Table 1.
Table 2: Sensors and control inputs of spinning band distillation column relevant for ML.