Chenkeng Tailings Storage Intelligent Safety Monitoring and Early Warning Project

Ltd. Chenkeng tailings pond is designed to have a final stacking elevation of 625.0m, a total dam height of 205m, a total design capacity of 4781.1×104m³, a catchment area of 3.992Km³, and a design grade of second-class tailings pond. The existing monitoring system in the reservoir area has been unable to comprehensively assess the safety risk level of the tailing pond, and it is necessary to establish a more complete and comprehensive monitoring system.

The company's technicians, led by project manager Zhu Fan, carried out the construction of the three-phase online monitoring system, with a total of: 33 surface displacement monitoring stations, realizing the stability monitoring of the whole dam; 6 deep displacement monitoring stations, realizing the internal displacement monitoring of the dam; 19 groundwater level monitoring stations, 1 beach top elevation and dry beach length monitoring station, realizing the monitoring of the dam's infiltration line; 1 reservoir water level monitoring station, realizing the reservoir water level safety monitoring; 1 rainfall monitoring station, realizing the grid-based meteorological monitoring in the mining area.

Chenkeng Tailings Storage Intelligent Safety Monitoring and Early Warning System integrates advanced technologies such as BeiDou, sensors, big data, cloud computing, etc., and realizes the all-weather collection and intelligent analysis of multi-dimensional information such as the surface displacement of the tailings storage dam body, the internal displacement of the dam body, the infiltration line, the elevation of the top of the beach, the length of the dry beach, and the amount of rainfall. The system realizes regional grid short-term weather forecasting through high-precision water vapor inversion for tailing ponds, tailing pond slope stability calculation and intelligent warning through flood regulation calculation, and comprehensive early warning of Chenkeng tailing pond monitoring data through multi-dimensional heterogeneous information fusion.