Flooding detection in the distillation column by ML
tools
In the following sections, the preprocessing of the acquired sensor
data, feature extraction, training of the ML methods and the
implementation with live data are described.
Preprocessing of time series
data
Multivariate time series data can be tricky to deal with as the temporal
structure should be preserved in some way during the training process.
One way to make supervised learning methods applicable to time series
data is the sliding window method [40], which transforms data in
such a way that past and “future” measurements are preserved for each
data point. For this use case, the future pressure drop (\(p_{t+1}\),\(p_{t+2}\), …) will be predicted based on the past data of
pressure drop and other significant parameters \(X_{i}\) (…,\(p_{t-1}\), \(X_{1,\ \ t-1}\), … , \(p_{t}\), \(X_{1,t}\),
…). These other significant parameters are determined in section
3.2. A schematic representation of the sliding window data
transformation is given in Figure 3. The window size refers to the time
window of past data and the response size describes the forecast window.