Once the dataset was validated and the precedence between blocks was defined, different block values were computed according to the 4 proposed scenarios, where the first employs a conventional cost function; the second employs a variable recovery rate per block; the third employs a variable processing cost per block; and the fourth scenario employs variable recovery and processing cost. The computational time required for the numerical experiments of scenarios 1 to 4 was 16.9, 10.7, 10.9, and 9.6 minutes, respectively, resulting in four feasible schedules. Table 1 presents the excavated masses of ore and waste (Mt), total tonnage (Mt), and economic return (M$) achieved in all scenarios. Although there is no reference to the mining sequence that would achieve a global optimum, since metaheuristics do not have this commitment to optimality, it can be seen that all scenarios that employed geometallurgical variables obtained NPV values lower than scenario 1. Given these results, it does not mean that the scenario that ignores geometallurgical variables is more efficient, but probably the opposite. Since scenario 1 is a greater simplification of reality, it is likely that the proposed mining sequence would not result in the predicted economic return when the model is confronted with reality, as the processing plant would not operate continuously with the same energy efficiency and recovery over the 16 years of project operation. Table 1 – Excavated masses of ore and waste (Mt), total tonnage (Mt), and economic return (M$) achieved in all scenarios. Scenarios Ore excavated (Mt) Waste excavated (Mt) Total tonnage (Mt) NPV (M$) Scenario 1 285.832 163.713 449.545 844.835 Scenario 2 230.375 103.938 334.312 796.714 Scenario 3 226.319 110.929 337.248 731.199 Scenario 4 233.985 109,929 343.914 801.950 Figure 4 depicts mining sequences achieved in each scenario in terms of total tonnage (Mt), Au grade (ppm), and Cu grade (%). In all the scenarios studied, the initial years prioritized gold mining, postponing the predominance of copper. Scenario 1 represents the conventional approach, based on a fixed cutoff grade, constant recovery, and fixed processing costs. The resulting mining sequence prioritises economic value in the early periods, leading to higher initial grades but reduced flexibility and lower robustness over time. Scenario 2 incorporates block-by-block recovery variability. This modification alters the sequencing by favouring blocks with higher metallurgical performance, producing a smoother grade profile and improved economic stability compared to Scenario 1, while maintaining a similar production level, although there is no production in two of the final years of the project. Scenario 3 introduces variable processing costs as a function of specific energy. The sequencing shifts to reduce exposure to high-energy blocks, which results in a more controlled stripping strategy but a faster decline in average grade, particularly in later periods. Scenario 4 combines variable recovery and variable specific energy in block cost calculations. This scenario produces the most balanced mining sequence, with improved production continuity and greater resilience to grade and cost variability. Although grade fluctuations are more pronounced, the integrated geometallurgical approach leads to a more robust economic outcome. Overall, the comparison demonstrates that incorporating geometallurgical variables into the mine planning framework significantly influences block sequencing decisions. Scenarios that account for metallurgical recovery and processing energy provide greater robustness and risk reduction than traditional fixed-parameter approaches, supporting more reliable long-term mine plans.
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