Figure 2 – Geometallurgical recovery models of (a) Au and (b) Cu for three different lithologies: AvT, GnD, and QzP (Modified from Mazzinghy et al., 2025). 2.2.3 Scenario 3: incorporating specific energy into the block value For this scenario, Equation (1) was adapted for different processing costs as a function of the total specific energy (kWh/t) obtained from Mazzinghy et al. (2025). For blocks with any metal content, it was verified that the mean, minimum, and maximum values of energy consumption were 11.7, 7.3, and 15.4 kWh/t, respectively. Therefore, given that an average processing cost of = 4.0 $/ was adopted by Minelib (2025), this cost was associated with the average specific energy ̅̅̅̅ = 7.3 ℎ/ from Mazzinghy et al. (2025), and then a proportional processing cost value associated with the energy consumption of each block was calculated. Equation (2) presents the function used in scenario 3: , = ⋅ / ̅̅̅̅, ∀ ∊ (2) where → , ∀ ∊ . 2.2.4 Scenario 4: incorporating both variable recovery and specific energy into the block value Finally, for this scenario, Equation (1) was adapted for both different recovery and specific energy ∀ ∊ , as shown in Equation (3), incorporating all the changes proposed in the previous scenarios: = → ⋅ ∑( , ⋅ , ⋅ ( − )) (3) − ⋅ ( )− ⋅ ( , ) =1 ℎ → − ⋅ ( ) 2.3 Mine sequencing model and simulated annealing metaheuristic 2.3.1 A mine sequencing model The mine sequencing problem was modelled as a direct block scheduling (DBS)
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