Flow regime identification in aerated stirred vessel using passive acoustic emission and machine learning

Smart at- or online process sensors, which employ machine learning (ML) to process data, have been the subject of extensive research in recent years, due to their potential for real-time process control. In this paper, a passive acoustic emission process sensor has been used to detect gas-liquid regimes within a stirred, aerated vessel using novel ML approaches. Pressure fluctuations (acoustic emissions) in an air-water system were recorded using a piezoelectric sensor installed on the external wall of three identical cylindrical tanks of diameter, T = 160 mm, filled to a volume of 5 L (height, H = 1.5 T). The tanks were made of either glass, steel, or aluminium, and each tank was equipped with a Rushton turbine of diameter, D = 0.35 T. The investigated flow regimes, flooding, loading, complete dispersion, and un-gassed, were obtained by changing the air feed flow rates and by varying the impeller speed. The acoustic spectra obtained were processed to select an optimal number of features characterizing each of the regimes, and these were used to train three different ML algorithms. The pre-processing includes a principal component analysis (PCA) step, which reduces the volume of data fed to the ML algorithms, saving computational time up to a factor of 5. The algorithms (decision tree, k-nearest neighbour, and support vector machines) were challenged to use these features to identify the correct flow regime. Accurate predictions of the three gas-liquid regimes of interest have been achieved. The accuracy of the prediction ranges from 90% to 99%, and this difference is related to the material used for the vessel.