Neural Network Model of Information Fusion for Coal Storage and Kinetic Energy of Ball Mill

Authors

Bai Yan, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
He Fang, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;

Abstract

Abstract: A dynamic mathematical model of coal pulverizing system was analyzed. Simulation experiments on mill operation process were conducted by PFC3D software platform based on discrete element method. The associated data between different coal quality, coal storage and balls' motion were obtained under certain quantitative optimized operating parameters configuration. Neural network model of information fusion for coal storage and kinetic energy of ball mill was established by using an adaptive combination learning algorithm. Coal storage in mill cylinder was predicted from the energy point of view. The results indicate that there is a close relationship between coal storage, pulverizing efficiency and balls' real-time kinetic energy. The neural network information fusion model has good predictive power to coal storage. The coal storage control method based on balls' kinetic energy is therefore feasible for optimized operation of the coal pulverizing system.

Recommended Citation

Yan, Bai and Fang, He (2020) "Neural Network Model of Information Fusion for Coal Storage and Kinetic Energy of Ball Mill," Journal of System Simulation: Vol. 27: Iss. 4, Article 4.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss4/4

First Page

689

Revised Date

2014-08-20

DOI Link

https://doi.org/

Last Page

696

CLC

TP391.9

Recommended Citation

Bai Yan, He Fang. Neural Network Model of Information Fusion for Coal Storage and Kinetic Energy of Ball Mill[J]. Journal of System Simulation, 2015, 27(4): 689-696.