Estimating local gas holdup profiles in bubble columns is key for their performance evaluation and optimization, as well as for design and scale-up tasks. However, there are important limitations in the accuracy and range of applicability of the available models in literature. A promising alternative for advancing the knowledge of the local holdup distributions in bubble columns is found in the application of Machine Learning techniques; nevertheless, up to these days there are no developed Neural Networks for the prediction of local gas holdups in bubble columns, and particularly radial profiles. In a great extent, the main drawback preventing the application of these techniques in previous years was the availability of a large enough databank of local gas holdup experimental measurements. Advances over the last decades in measurement techniques have allowed to have enough data reported in literature to gather a significative databank for these modelsâ development. In this work, a new databank containing 1252 experimental points was gathered and used for the development of a Deep Neural Network (DNN). The new DNN allowed a highly accurate prediction of the local gas holdup profiles, exhibiting a Mean Squared Error 0.001. Furthermore, the DNN allowed to estimate the single and multi-feature effects of the operation conditions, geometrical characteristics, and physical properties of the fluids, over the local gas holdup profiles
