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.