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Prediction of Bubble Sizes in Bubble Columns with Machine Learning Methods
Author(s) -
Biessey Philip,
Bayer Hakan,
Theßeling Christin,
Hilbrands Eske,
Grünewald Marcus
Publication year - 2021
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.202100157
Subject(s) - bubble , python (programming language) , lasso (programming language) , random forest , computer science , regression analysis , artificial intelligence , machine learning , parallel computing , world wide web , operating system
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.