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Browsing Articles by Subject "Backpropagation neural network"
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Item Open Access EVALUATION OF THE USE OF SUBLEVEL OPEN STOPING IN THE MINING OF MODERATELY DIPPING MEDIUM-THICK OREBODIES(International Journal of Mining Science and Technology, 2021-01-04) Xu, Shuai; Liang, Ruiyu; Suorineni, Fidelis T.; Li, YuanhuiThe flow of blasted ore during mining of moderately dipping medium-thick orebodies is a challenge. Selecting a suitable mining system for such ore bodies is difficult. This paper proposes a diamond layout sublevel open stoping system using fan blastholes with backfilling to mine such orebodies. To evaluate the performance of system the relationships between ore recovery and stope footwall dip angle, footwall surface roughness, drawpoint spacing and production blast ring burden were investigated. An ore recovery data set from 81 laboratory physical model experiments was established from combinations of the listed factors. Various modules in a back propagation neural network structure were compared, and an optimal network structure identified. An ore recovery backpropagation neural network (BPNN) forecast model was developed. Using the model and sensitivity analysis of the factors affecting the proposed open stope mining system, the significance of each factor on ore recovery was studied. The study results were applied to a case study at the Shandong Gold Group Jiaojia Gold Mine. The results showed that the application of a BPNN and sensitivity analysis models for ore recovery prediction in the proposed mining system and field experimental results confirm that the suggested mining method is feasible.