Determination of bubble sizes in bubble column reactors with machine learning regression methods

Abstract

In this study, two machine learning based regression models are developed to predict diameters of single bubbles in a bubble column reactor based on wire-mesh sensor (WMS) measurement. Both Least Absolute Shrinkage and Selection Operator (LASSO) regression and a regression tree algorithm are used to predict bubble diameter with supervised learning techniques. Measurements are carried out in a DN150 column filled with deionized water and air as the continuous phase while WMS passage of single bubbles is investigated. A novel method for definition of different labels characterizing the passing bubble is introduced. Based on the defined labels, Machine Learning regression models are developed to predict bubble sizes. Methods for dimensionality reduction are applied, allowing for an investigation of each labels influence on model prediction quality. Both regression models perform similar or better than well-established approaches to calculate bubble diameter based on WMS measurement. As a highlight, it is shown that bubble diameters even below the sensor’s spatial resolution can be predicted with an accuracy of ±13% using the regression tree model, which is about 1/3 of the conventionally assumed measurement uncertainty at bubble diameters below the sensor’s spatial resolution.