Model of Ball Mill Based on the CPS


Share / Export Citation / Email / Print / Text size:

International Journal of Advanced Network, Monitoring and Controls

Xi'an Technological University

Subject: Computer Science , Software Engineering


eISSN: 2470-8038





Volume / Issue / page

Related articles

VOLUME 2 , ISSUE 1 (March 2017) > List of articles

Model of Ball Mill Based on the CPS

Li Cunfang / Zhang Taohong / Zhang Dezheng / Wang Huan / Zeng Qingfeng / Sun Yi

Keywords : cyber-physical systems, ball mill, cyber model, physical model, cyber-physical model

Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 1, Pages 14-24, DOI:

License : (CC BY-NC-ND 4.0)

Published Online: 06-April-2018



With the development of the new technology of intelligent manufacturing and cyber physical system, a new scheme is proposed for designing of predictive production based on ball mill. First, physical model (PM) and the model based on data (cyber model, CM) are discussed. Then, the combination of physical model and cyber model (CPM) is realized. Physical model is established according to the volume balance formula and the material balance formula. Cyber model uses the algorithm of the extreme learning machine which introduces penalty function. CPM uses the least square method to realize the combination of PM and CM and gets the value of the coefficient. Compare the actual data on ball mill to the data of the model then the result shows that the mean square error of CPM is smaller than the mean square error of PM and CM. The experimental results validate the effectiveness of the proposed method, which can be effectively used in ball mill in our industry.

Content not available PDF Share



Lee J, Bagheri B, Kao H A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems[J]. Manufacturing Letters, 2015, 3: 18-23.


ZHOU Xing-she, YANG Ya-lei, YANG Gang. Modeling methods for dynamic behaviors of cyberphysical system[J]. Chinese Journal of Computers,2014, 37(3): 1


Chen J, Yang J, Zhou H, et al. CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach[J]. Engineering, 2015, 1(2): 247-260.


WANG Dong-feng, YU Xi-ning, SONG Zhi-ping. Dynamic mathematical model of ball mill pulverizing system and its inverse control based on distributed neural networks[J]. Proceeding of the CSEE, 2002, 22(1): 97


Yuan Y, Zhang Y, Cao H, et al. Nonlinear prediction model for ventilation of ball mill pulverizing system[C]//Control Conference (CCC), 2016 35th Chinese. TCCT, 2016: 2025


Mishra B K. A review of computer simulation of tumbling mills by the discrete element method: part I—contact mechanics[J]. International journal of mineral processing, 2003, 71(1): 73-93


MA Tian-yu, GUI Wei-hua. Optimal control for continuous bauxite grinding process in ballmill[J]. Control Theory & Applications, 2012, 29(010): 1339-1347


LEE E A. Introduction to embedded systems-a cyber-physical systems approach[M]. UC Berkeley, 2014: 79


YU Zhen-hua, CAI Yuan-li, FU Xiao, et al. Formal modeling and analyzing high-confidence software of cyber-physical systems[J]. Systems Engineering theory and practice, 2014, 34(7): 1857


XU Xin, YU Hui-qun, HUANG Jun-hu. Petri net based security quantitative analysis model for cyber-physical system[J] . Computer Engineering and Applications, 2014, 50(3): 82


BANERJEE A, GUPTA S K S. Spatio-temporal hybrid automata for safe cyber-physical systems: a medical case study[C]//2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS). Philadelphia,USA, c2013: 71


Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks[C]//Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on. IEEE, 2004, 2: 985


Huang G B, Zhou H, Ding X, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(2): 513