Online size distribution measurement of dense iron green pellets using an efficient and multiscale nested U-net method

Abstract

Accurately measuring the pellet size distribution during the pelletizing process is critical, because the pellet size distribution is a key quality indicator and affects the efficiency of the blast furnace. In this study, a method based on a convolutional neural network is proposed to measure the pellet size distribution in the stable area of a rotary disc. The proposed network only uses simple convolution layers in sequence to realize multiscale performance; furthermore, it uses a bottleneck layer and a concatenate path to improve the computing efficiency and alleviate the vanishing gradient problem, respectively. Various experimental results demonstrate that the proposed network outperforms other comparison methods in segmenting overlapped pellets, especially pellets of different sizes. The pellet size distribution obtained by the proposed method agreed well with the manual sieving results. Moreover, the computing time of the proposed method can meet the requirements of online size distribution measurement.