Shearer parameter optimization and low energy consumption mining based on 3D point cloud characterization of coal wall

Abstract

AbstractTo achieve efficient and low energy consumption mining under different cutting depths of a shearer, a multiparameter coupling optimization method for the shearer based on three‐dimensional (3D) characterization of the coal wall was proposed. First, a seven‐axis absolute articulated arm measuring machine was used to obtain 3D point cloud data of the coal wall, and then the 3D of the coal wall surface was reconstructed by using segmentation, filtering, and stitching processing, thereby obtaining the average thickness of different coal wall areas. Second, through the quadratic rotation regression orthogonal combination experiment, the optimal combination of drum speed, traction speed, and cutting depth was obtained, further obtaining the order of primary and secondary influences, and the regression model. Moreover, a particle swarm optimization algorithm was used to obtain the optimal drum speed and finally, the laboratory and field test experiments were conducted to verify the effectiveness of the proposed optimization algorithm in reducing the cutting energy consumption of shearer. The experiment results show that the given optimization algorithm can adaptively optimize the traction speed and drum speed based on the corresponding cutting depth, which significantly reduces the cutting specific energy consumption of the shearer. Thus, it provided an important technical means for the shearer to achieve low energy consumption and efficient mining.