3. Energy use The mining industry still relies heavily on fossil fuels for the majority of the energy consumed during the extraction and processing phases. This is due to the often remote locations without connection to the grid where mining operations are situated, preventing electrification. Although renewable energy generation on-site is tested, the adoption is slow, given the intermittent nature of the renewable energy sources, high prices of battery storage, and the need for suitable conditions (daylight or persistent winds) year-round (Issa et al. 2023). In light of the fact that the sector is estimated to account for between 1.7 to 3.5% of the global energy use (Aramendia et al. 2023; Engeco 2022), any efforts directed towards more energy-efficient extraction are worth investigating. Energy efficiency is often indirectly addressed in optimization studies, despite not being the primary objective. For example, a more selective extraction schedule may lead to a lower amount of unnecessary waste introduced into the processing circuit, in turn reducing energy demand (Levinson, Dimitrakopoulos, and Keutchayan 2023). Similarly, fleet optimization, although typically focused on cost reduction, often results in lower trucking demand, reducing the need for active material movement, fleet size, and fuel consumption (Rahnema, Amirmoeini, and Moradi Afrapoli 2023). Due to the multiple objectives of the presented studies, in this work, they were broadly categorized into energy reduction efforts related to material considerations and fleet management. 3.1 Material considerations Similar to the inclusion of geochemical variables into mine planning and optimization, geometallurgical considerations are gaining traction in the research community. Along with the shift to optimize mining complexes as a whole instead of considering the extraction stage exclusively, the possibility to optimize processing feed characteristics is more commonly investigated. Utilization of existing synergies between the two stages appears to be a promising research direction, as comminution can account for up to 80% of the energy used on an individual mine site (Abouzeid and Fuerstenau 2009). Kumar and Dimitrakopoulos (2019) introduced an adaptation of the simultaneous stochastic optimization framework developed by Goodfellow and Dimitrakopoulos (2016) that incorporates grindability as one of the optimized parameters of the objective function. Here, two hardness indices are used, namely the SAG power index (SPI) and bond work index (BWI), representing the grindability of the material in SAG and ball mills, respectively. This geometallurgical variable is then used to create a blending solution, ensuring consistent throughput of material in the milling facilities. The novel formulation, solved using simulated annealing with adaptive neighbourhood search, is then tested in a large-scale copper-gold mining complex. The geometallurgical uncertainty is assessed by sampling each of the 10 lithologies present in the deposit. In comparison with the base case, the stochastic solution resulted in reduced underutilization of the individual mills due to joint optimization of the destination policy of all underlying facilities. The optimized schedule also results in 19.3% higher NPV due to more efficient use of synergies between various components of the mining complex.
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