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.

在这项研究中，开发了两个基于机器学习的回归模型，以基于线网传感器（WMS）测量来预测气泡塔反应器中单个气泡的直径。最小绝对收缩和选择算子（LASSO）回归以及回归树算法均用于通过监督学习技术预测气泡直径。在充满去离子水和空气作为连续相的DN150色谱柱中进行测量，同时研究WMS通过单个气泡的过程。介绍了一种新颖的方法，用于定义表征通过气泡的不同标签。基于定义的标签，开发了机器学习回归模型以预测气泡大小。应用了降维方法，可以研究每个标签对模型预测质量的影响。两种回归模型在基于WMS测量来计算气泡直径方面，执行的方法都相似或优于公认的方法。作为一个亮点，表明使用回归树模型可以以±13％的精度预测甚至低于传感器空间分辨率的气泡直径，这大约是在低于传感器空间分辨率的气泡直径下常规假定的测量不确定度的1/3。空间分辨率。