Optimization of capacity continuity during the deep-area transition for an underground metal mine
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摘要: 為了實現金屬地下礦山開采由淺部轉向深部過程中產能平穩接續，以三山島金礦為研究對象，結合礦山多區段聯合開采的復雜生產格局，綜合考慮產能均衡、品位均衡與各項生產系統能力限制等約束，構建以多礦區資源綜合開采價值最大為目標的產能接續規劃優化模型，在Python和Gurobi環境下實現優化模型構建與解算。優化結果表明，通過對礦山深部轉產過程中的產能接續進行規劃優化，得到的最佳產能接續與生產任務分配方案可以在有效保證多礦區協同開采、產能均衡穩定的同時，提升礦山開采的綜合經濟效益。Abstract: With the rapid depletion of shallow mineral resources, an increasing number of mines are stepping into deep mining to ensure resource continuity, which generally forms a coordinated production model that includes both existing shallow production systems and new deep projects. Given the complex production patterns of both multi-mining areas and multi-sections formed during the process of deep mining, how to achieve geological resource continuity, stable production capacity, balanced supply grade, and sustainable economic benefits are the key issue to be addressed in the mining scheme during the transition to deep-area. When an underground mine transitions to deep mining, it is necessary to steadily advance the production task and maintain the steady growth of metal quantity and economic benefit. Therefore, to maintain production continuity and stability, it is necessary to optimize the production plan for underground metal mines. Considering the situation of multi-section mining submontanely, the Sanshandao gold mine is used as a case study to investigate the complex production layout of multi-section mining simultaneously subjected to constraints of production capacity balance, grade requirements, and other production system capacities through in-depth analysis of the production capacity continuity in the process of mining transition to deep mineral resources. An optimization model is constructed aiming at maximizing the comprehensive resource exploitation value of multi-section mining. A mathematical planning model for the continuation and optimal allocation of production capacity during the transition period of deep mining is constructed with the optimization goal of maximizing the comprehensive exploitation return of resources from multiple mining areas, taking the constraints of overall capacity succession, balanced output grade, and capacity limits of each mining area into account. Following the solution of the Sanshandao gold mine’s production capacity, succession during the transition to deep-area is obtained. The model considers the coordination and succession of multiple mining areas and sections in terms of time and space, and it achieves a smooth production system transition from shallow to deep areas through accurate evaluation of existing operation conditions and optimal allocation of production factors, all while ensuring the production tasks and economic efficiency targets are met. The optimization model is programmed in Python, and it is solved using the Gurobi software. The results show that the best capacity continuation and production task allocation schemes are obtained to effectively ensure benefit improvement as well as production capacity balance and stability. The example application validates the scientific and efficacy of the optimization model, which can be used by other mines to optimize production plans when switching to deep mining.
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圖 1 三山島金礦礦石流向示意圖
Figure 1. Schematic of the ore flow direction for Sanshandao gold mine
圖 3 多礦區同時開采的礦石流圖
Figure 3. Ore flow diagram of simultaneous mining in multiple sections
圖 4 Python和Gurobi建模環境下的模型求解流程
Figure 4. Modeling and solving process in Python and Gurobi software
圖 5 規劃期內各年各礦區礦石產量結果統計
Figure 5. Annual ore output of each mining area during the planning period
圖 6 規劃期內各年各礦區凈現金收益結果統計
Figure 6. Annual net present value of each mining area during the planning period
圖 7 規劃期內新立選廠礦石處理量、入選品位結果統計
Figure 7. Processing capacity and ore grade per year of Xinli processing plant during the planning period
圖 8 規劃期內西山選廠礦石處理量、入選品位結果統計
Figure 8. Processing capacity and ore grade per year of Xishan processing plant during the planning period
表 1 開采技術經濟指標表
Table 1. Mining technical and economic parameters
Indicators Unit Xinli section Xinli deep section Xishan section Xishan deep section Xiling section Reserve 107 t 1.814 1.255 0.239 0.325 8.426 Average grade g·t–1 2.35 3.32 2.08 3.22 4.18 Cost per ton ￥·t–1 380 403.8 380 531.65 516.19 Loss rate % 8 9.41 8 9.25 9.99 Dilution rate % 5 10 5 10 8 Maximum lift capacity 106 t·a–1 3.3 1.65 3.3 Maximum processing capacity 106 t·a–1 3.3 1.65 Recovery rate % 95.89 96 Maximum grade g·t–1 4.6 Minimum grade g·t–1 2.0 Discounted rate % 10 
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久色视频表 2 基于均值回歸運動模型的黃金價格預測結果
Table 2. Prediction results of gold price based on the mean regression motion model
Year Price/(￥·g–1) Year Price/(￥·g–1) Year Price/(￥·g–1) 1 366.7 14 273.6 27 261.0 2 340.5 15 273.4 28 264.2 3 326.9 16 271.5 29 264.7 4 314.9 17 269.8 30 263.2 5 303.3 18 268.2 31 269.0 6 293.3 19 263.8 32 260.7 7 287.5 20 266.1 33 262.1 8 279.9 21 269.3 34 257.0 9 279.4 22 267.6 35 256.2 10 274.9 23 262.4 36 257.8 11 279.3 24 260.5 37 260.9 12 283.0 25 257.5 38 258.5 13 280.1 26 261.0 39 260.4
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