Username   Password       Forgot your password?  Forgot your username? 

A Study on Faults Diagnosis and Early-Warning Method of Tailings Reservoir Monitoring Points based on Intelligent Discovery

Volume 13, Number 5, September 2017 - Paper 13  - pp. 697-710
DOI: 10.23940/ijpe.17.05.p13.697710

Tianyong Wu­a,Chunyuan Zhangb,Yunsheng Zhaoa,*

aFaculty of Engineering, China University of Geosciences, No. 388, Lumo Road, Hongshan District, Wuhan, 430074, PR China
bSchool of Computer Science, China University of Geosciences, No. 388, Lumo Road, Hongshan District, Wuhan, 430074, PR China

(Submitted on April 20, 2017; Revised on June 30, 2017; Accepted on August 15, 2017)


The tailings reservoir is a major hazard source with high potential energy, which may cause an artificial debris flow. The stability of the tailings reservoir is extremely important to the normal operation of the mining enterprises and the safety of people's lives and property. In order to settle the problem that traditional manual monitoring is scattered, not timely, and difficult to manage, this article takes Huangmailing tailings as an example, and establishes the CMST model to optimize the network topology connection of the tailings monitoring points. BP neural network algorithm is used to discuss the intelligent discovery and early warning of the faults on-line monitoring system of tailings. In this way, the fault-points and the causes can be perceived quickly and accurately, and the risk of the tailings’ safety accident can be reduced. It can be proved by the experimental results and two years stable operation of the system that BP neural network algorithm can accurately predict the value of safety monitoring data.


References: 19

    1. S. Y. Dong and Q. M. Li, “Discussion on Structure and Function Realization of Digital Tailings Ponds System,” Journal of Safety Science and Technology, vol. 11, no. 5, pp. 78-83, May 2015 
    2. Y. Z. Gao, Y. Chu, and W. Liang, “Remote Sensing Monitoring and Analysis of Tailings Ponds in the Ore Concentration Area of Heilongjiang Province,” Remote Sensing for Land and Resources, vol. 27, no. 1, pp. 160-163, January 2015 
    3. D. C. Huang and S. C. Xie, “A Way to Predict the Settlement of Tailings Dam Based on BP Neural Network,” Engineering of Surveying and Mapping, vol. 25, no. 8, pp. 53-56, August 2016 
    4. L. Huang, F. Miao, and M. X. Wang, “Designing and Setting up A Monitoring and Early Warning System for Tailings Ponds in A Region,” China Safety Science Journal, vol. 23, no. 12, pp. 146-152, December 2013
    5. Q. S. Huang, Q. Li, Y. J Wang, and J. Zhang, “Dry Beach of Tailings Dam Length Measurement Based on Waterline Recognition,” Chongqing: IEEE Advanced Information Technology, Electronic and Automation Control Conference, pp. 503-507, December 2015
    6. Y. Huang, Y. G. Li, and X. F. Chen, “Monitoring Information Processing for Saturation Line of Tailing Dam Based on DE-Kalman Filtering,” Control Engineering of China, vol. 23, no. 9, pp. 1319-1324, September 2016
    7. W. J. Liao, K. L. Huangfu, Y. He, and Z. Z. He, “Integrated Real-time Monitoring, Analyzing and Analyzing Pre-warning System for Tailings Reservoir,” China Safety Science Journal, vol. 24, no. 8, pp. 158-163, August 2014
    8. X. Li, J. Han, X. L. Lin, and X. D. Liu, “An Efficient Branch and Bound Algorithm for CMST Problem,” Journal of Harbin Engineering University, vol. 28, no. 12, pp. 1371-1376, December 2007
    9. M. Necsoiu and G. R. Walter, “Detection of Uranium Mill Tailings Settlement Using Satellite Based Radar Interferometry,” Engineering Geology, vol. 197, no. 1, pp. 267-277, October 2015
    10. S. G. Sun, P. Guo, Y. H. Zhang, and Z. Su, “Research on Safety of Fine Tailings Dam under the Influence of Saturation Line Distribution Characteristics,” AER Advances in Engineering Research, vol. 44, no. 1, pp. 413-417, November 2016
    11. J. Wang, Y. Q. Wen, Y. D. Gou, Z. Y. Ye, and H. Chen, “Fractional-order Gradient Descent Learning of BP Neural Networks with Caputo Derivative,” Neural Networks, vol. 89, pp. 19-30, May 2017
    12. L. G. Wang, F. W. Yang, and G. Z. Liu, “Application and Study on Vibrating Wire Sensor in Online Monitoring System of Tailings Reservoir,” Nonferrous Metals(Mining Section), no. 3, pp. 83-86, June 2016
    13. X. Y. Wan, L. H. Sun, H. M. Tian, Z. F. Huang, and H. Yang, “Research on 3S Integration Technology in the Tailings Pond Monitoring,” International Conference on Energy, Environment and Sustainable Development. October 2011
    14. T. L. Xu, X. Z. Lang, X. C. Pei, and L. X. Xue, “Research on Safety Monitoring and Early-warning System of Tailing Reservoir Based on Optical Fiber Transmission,” Gold, vol. 32, no. 7, pp. 43-47, July 2011
    15. H. Zhang, Y. S. Zhao, and X. Li, “Research on Monitoring of Tailing Reservoirs Based on the Internet of Things-A Case Study of Huangmailing Phosphorus Chemical Tailing Reservoir,” Safety and Environmental Engineering, vol. 22, no. 6, pp.143-150, December 2015
    16. H. M. Zhou, Z. Q. Yuan, J. Su, and X. C. Yang, “The Present Situation and Prospects for Safety Online- Monitoring System of Tailings Pond,” 3rd International Conference on Mechatronics, Robotics and Automation, April 2015
    17. L. F. Zhang, X. S. Liu, H. W. Wan, and X. Liu, “Luobei Graphite Mines Surrounding Ecological Environment Monitoring Based on High-resolution Satellite Data,” Beijing: Conference on Multispectral, Hyper-spectral, and Ultra-spectral Remote Sensing Technology, Techniques and Applications V, November 2014
    18. J. F. Zhang, Z. M. Qian, G. F. Ren, and S. S. Zhang, “Information Monitoring and Management System for Tailings Based on Lab VIEW,” Wuhan: 3rd International Conference on Green Power, Materials and Manufacturing Technology and Applications, December 2013
    19. W. Zhang, “Analysis on Public Safety Engineering Supervision Based on Internet of Things,” Urban Construction Theory Research, vol. 316, no. 32, pp. 65-70, October 2013



      Click here to download the paper.

      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      This site uses encryption for transmitting your passwords.