Study On Digital Mining Technique And Mining Index Optimization Of Sublevel Caving In Gongchangling Iron Mine

The mining technique of sublevel caving without pillars is widely used in China’s underground metal mines.This method is carried out under the covering of the loose rock formations.The ore caving and drawing process has invisibility,which not only results in high ore loss and depletion rate,but also brings great difficulties in optimizing the ore mining index.With the improvement of digital mine construction in China,the application of digital technique in the field of mining engineering has become more and more extensive,which provides a new way for optimization study on ore mining index.Supported by sub-topic of the 12th Five-Year National Science and Technology Support Program "Research on the Digital Technique of Recovery System in Underground Mine",this paper takes Gongchangling iron mine as the research object to study the optimization on ore mining index for digital mining technique of sublevel caving without pillars.Insufficient types of mine data,lack of data integration and shared software platforms,and high ore loss and depletion rate are three major issues in digital mine construction of sublevel caving without pillars.To solve these problems,an optimization model of ore mining index was built.Methods to establish the numerical model of the caving body and optimize the ore mining index were proposed.And the "Digital Mining Assistant Decision-Making System for Caving Mine" was developed to realize data integration and shared.Based on the structured analysis method,functional and non-functional requirments analysis of the software platform was performed to build the design framework.Through on-site investigation,in-situ stress measurement,similarity simulation test of fan-shaped holes blasting and field ore-drawing test in Gongchangling iron mine,the required data to build the optimization model of ore mining index was obtained.These data includes the stope structural parameters,the fan-shaped holes blasting parameters,the status of ground pressure activities,the shape characteristics of caving body,the variation trend of ore drawing grade and the ore loss and depletion rate.Based on the relational data model,the type and specific content of the required data were determined through requirements analysis and their E-R data models were obtained by the conceptual design.The transformation from the E-R data models to the relational data models was completed by the logical design and the database of the "Digital Mining Assistant Decision-Making System for Caving Mine" was constructed by the physical design with the database software Access.Using the graphical programming software Lab VIEW,functional modules were designed and developed,including the system login module,the data addition module,the data query module,the data processing module,the data modification module,the data output module and the optimization module of ore mining index.By subVI technique,these modules are integrated to establish the "Digital Mining Assistant Decision-Making System for Caving Mine".Based on the completed "Digital Mining Assistant Decision-Making System for Caving Mine",using the measured stress state as the mechanical boundary condition,the numerical model of the caving body with ore size distribution was built by ANSYS/LS-DYNA,AUTO CAD,3D MINE,and PFC3D and the ore drawing process was simulated.The digitization of the mining process in Gongchangling underground mine was realized.Taking the numerical model of the caving body as the simulated drawing object,the ore mining indexes corresponding to the extension length L and extension angle a were obtained by ore drawing simulation.The regression equations for ore mining index based on L and a as independent variables were established and applied to predict the optimal ore-drawing hole size of Gongchangling iron mine.Finally,the optimization objective of the ore mining index was achieved.