Research on dynamic weighing of rare earth oxide based on Kalman-PSO-BP neural network

Guoyu Zhao; Shiqi Liu; Jinyong Xu; Chenhui Li; Xiaobo Liu

doi:10.1117/12.3056091

19 December 2024 Research on dynamic weighing of rare earth oxide based on Kalman-PSO-BP neural network

Guoyu Zhao, Shiqi Liu, Jinyong Xu, Chenhui Li, Xiaobo Liu

Author Affiliations +

Proceedings Volume 13444, Fifth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Mechatronics (MEIMM 2024); 134440Q (2024) https://doi.org/10.1117/12.3056091
Event: The 5th International Conference on Mechanical Engineering, Intelligent Manufacturing, and Mechatronics, 2024, Guilin, China

Abstract

Molten salt electrolysis is currently a commonly used method for the preparation of rare earth metals, and in order to improve the electrolysis efficiency and product purity, automated equipment is gradually replacing manual operation. The molten salt electrolysis process requires strict control of the temperature in the electrolysis tank, so it is necessary to control the quality of each rare earth oxide discharging. However , the vibration and noise interference during the operation of the equipment leads to dynamic weighing errors. To improve the dynamic weighing accuracy, Kalman filtering and Particle Swarm Optimization(PSO) were combined with Back propagation Neural Network (BP) to obtain the dynamic weighing data of six working conditions by the rare earth oxide automatic discharging equipment. The Kalman-PSO-BP model was constructed and trained by Matlab, and simulations showed that the model error was reduced by 55.9%, 52.6%, 47.2%, 52.9%, 53.7%, and 53.2% compared to the original error.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guoyu Zhao, Shiqi Liu, Jinyong Xu, Chenhui Li, and Xiaobo Liu "Research on dynamic weighing of rare earth oxide based on Kalman-PSO-BP neural network", Proc. SPIE 13444, Fifth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Mechatronics (MEIMM 2024), 134440Q (19 December 2024); https://doi.org/10.1117/12.3056091

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