Prediction of Bubble Sizes in Bubble Columns with Machine Learning Methods

Two Machine Learning algorithms - LASSO and Random Forest - are applied to derive regression models for the prediction of gas bubble diameters using supervised learning techniques. Experimental data obtained from wire-mesh sensor (WMS) measurements in a deionized water/air system serve as the data base. Python libraries are used to extract features characterizing WMS measurement signals of single passing bubbles. Prediction accuracy is largely increased with the obtained regression models, compared to well-established methods to predict bubble sizes based on WMS measurements.

Automated summary

Two Machine Learning algorithms, LASSO and Random Forest, are utilized to predict gas bubble diameters with high accuracy, based on features extracted from WMS measurements in a water/air system. The obtained regression models outperform traditional methods in predicting bubble sizes.