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Integer linear programming for mining systems
Published in Amit Kumar Gorai, Snehamoy Chatterjee, Optimization Techniques and their Applications to Mine Systems, 2023
Amit Kumar Gorai, Snehamoy Chatterjee
This skewed distribution of the data leads to exploring the reason for the skewness of the histogram. It may be due to the number of lithologies present in the study area. Therefore, statistical analysis of the lithological data was carried out to show the statistical variability within the lithology. The descriptive statistics of the seven lithotypes are given in Table 5.10. The results show that the lithology-based classification reduces the data variance. The maximum variance was found in BHQ lithology with 235.19, and minimum variance has found in steel grey hematite that is 1.5. But the distribution pattern has not been changed; all are showing the negatively skewed distribution. Box plot was prepared to visualize the variation lithology-wise for the Fe attribute. Figure 5.16 presents the lithology-wise box plots of variable Fe. The box plot results revealed that the four ore-forming lithologies, i.e., SGS, BH, LH, and L, have less variance compare to the other three lithologies. This lithology-wise statistical analysis revealed the importance of lithology for grade estimation. Therefore, resource estimation was performed independently for each lithology after preparing lithology-wise solid modelling. All analysis was carried out using Surpac software (www.3ds.com/products-services/geovia/products/surpac/).
Optimization model for long-term cement quarry production scheduling
Published in Vladimir Litvinenko, Scientific and Practical Studies of Raw Material Issues, 2019
The MILP model was verified and validated by a comparative study against one of the common commercial software in quarry mining – GEOVIA Minesched. The analysis showed that application of MILP model resulted in substantially higher cost saving than GEOVIA Minesched that is not based on mathematical programming methods. Besides the ability of optimality of the objective, the MIP model also considered simultaneously the raw mix blending requirements, whereas GEOVIA Minesched aimed to meet capacity targets but violated the quality targets in most cases. This highlights the importance of application of optimization techniques in solving the LCQPSP.
Short-term planning for open pit mines: a review
Published in International Journal of Mining, Reclamation and Environment, 2019
Michelle Blom, Adrian R. Pearce, Peter J. Stuckey
Numerous other packages providing short-term planning capabilities exist. MineSched by GEOVIA2 is designed for use in both long- and short-term planning in both open-pit and underground mines. MineSched supports the automated sequencing of blocks on a bench-by-bench basis, the modelling of stockpiling and multiple processing pathways, and blending across multiple mine sites. Micromine3 employs MIP solving in its mine planning and design tools, with support for mine design, long-, medium- and short-term planning. This MIP is designed to find an optimal mining sequence at each horizon. MineMax Tempo4 supports the integration of scheduling across the long-, medium- and short-term horizons by measuring the compliance of plans at shorter horizons with longer term plans. Maintenance scheduling, truck dispatch and real-time production monitoring can also be integrated within the MineMax solution. Studio OP by Deswik5 supports the medium and short-term planning of open-pit mines, including: pit and dump design; and the blending of material from different sources given production, haulage and operating constraints. While many tools incorporate block-sequencing optimisation at different horizons, others focus on providing support for visualisation and comparison of mine designs and plans. Vulcan Open Pit Mine Planning by Maptek,6 for example, includes automated features for generating bench plans (‘blocking out’ material and assigning destinations based on defined rules) and the visualisation and comparison of alternative short-term plans.