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

HIGHLIGHTS

• What: The key advantage of the research is integration of the developed theoretical model of absorption based on Levich`s theory and computer vision techniques. The authors propose an innovative approach that integrates neural_network-based computer vision algorithms with an analytical hydrodynamic model to enhance mass transfer predictions in gas-liquid systems. Using data from jet stream fermenters, the authors demonstrate the effectiveness of the model in predicting mass transfer rates under various operational conditions. By integrating neural_network-based computer vision techniques with traditional mass transfer models, the authors aimed to enhance the accuracy and efficiency of . . .
  

   

  

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