A machine learning approach for electrical capacitance tomography measurement of gas–solid fluidized beds

Electrical capacitance tomography has been widely used to obtain key hydrodynamic parameters of gas–solid fluidized beds, which is normally realized by first reconstructing images and then by analyzing these images. This indirect approach is time‐consuming and hence difficult for on‐line monitoring. Meanwhile, considering recurrence of similar flow patterns in fluidized beds, most of these calculations are repetitive and should be avoided. Here, we develop a machine learning approach to address these problems. First, superficial gas velocity linear‐increasing strategy is used to perform high‐throughput experiments to collect a large amount of training samples. These samples are used to train the map from normalized capacitance measurements to key parameters that obtained by an iterative image reconstruction algorithm off‐line. The trained model can then be used for on‐line monitoring. Preliminary tests revealed that the trained models show good prediction and generality for the estimation of the overall solid concentration and the equivalent bubble diameter.