Mass transfer estimation in gas–liquid systems through integration of hydrodynamic model and computer vision algorithms

Abstract A novel methodology for estimation of mass transfer in bubbly flows is investigated. The key advantage of the research is integration of the developed theoretical model of absorption based on Levich’s theory and computer vision techniques. This methodology demonstrates high accuracy in predicting mass transfer rates, which is verified by comparisons with empirical data from experimental jet stream fermenter. Using high-speed video recordings and advanced image processing algorithms, our approach overcomes the limitations of many direct methods for mass transfer measurement: offering real-time, dynamic estimation of mass transfer intensity, scale-up flexibility and low costs. The integration of computer vision with analytical modeling represents a significant advancement in the field of liquid–gas media estimation, paving the way for the development of more sophisticated and effective measurement tools. The proposed methods have high adaptability and can be useful to handle complex bubble dynamics both in industrial tasks and scientific research.