Nelson Morales

1. Mining activities inevitably lead to land degradation, loss of vegetation, and long-term alteration of land-use patterns. This is especially true when operations rely on large external waste-roc...

Determining the design of pushbacks in an open-pit mine is a key part of optimizing the economic value of the mining project and the operational feasibility of the mine. This problem requires balancing pushbacks that have good geometric properties to ensure the smooth operation of the mining equipment and so that the scheduling of extraction maximizes the economic value by providing early access to the rich parts of the deposit. However, because of the challenging nature of the problem, practical approaches for finding the best pushbacks strongly depend on the expert criteria to ensure good operational properties. This paper introduces the Advanced Geometrically Constrained Production Scheduling Problem to account for operational space constraints, modeled as truncated cones of extraction. To find the best solution for this problem, we present a parallel genetic algorithm based on a genotype–phenotype model such that the genotype symbolizes the base block of a truncated cone, and the phenotype represents the cone itself. A central computer node evaluates these solutions, collaborating with various secondary nodes that evolve a population of feasible solutions. The PGA’s efficacy was validated using comprehensive test instances from established research. The PGA solution exhibited a consistent average copper grade across periods, with its incremental phases reflecting real-world mine geometry. Moreover, the benefits of the MeanShift clustering technique were evident, suggesting effective phase-based scheduling. The PGA’s approach ensures optimal resource utilization and offers insights into potential avenues for further model enhancements and fine-tuning.

The steepness of the slopes of an open pit mine has a substantial influence on the financial return of the mine. This paper proposes a novel design methodology where the overall steeper pitwalls are employed without compromising the safety of the mine. In the current design practice, the pitwall profiles are often planar in a cross-section within each rock layer, i.e., the profile inclination across each layer tends to be constant. Here instead, a new geotechnical software, the OptimalSlope is employed to determine the optimal pitwall profiles of depth varying inclination. The OptimalSlope seeks the solution of a mathematical optimization problem where the overall steepness of the pitwall, from crest to toe, is maximized for an assigned lithology, geotechnical properties and Factor of Safety (FoS). Bench geometries (bench height, face inclination, minimum berm width) are imposed in the optimization as constraints which bind the maximum local inclination of the sought optimal profile together with any other constraints such as the geological discontinuities that may influence a slope failure. The obtained optimal profiles are always steeper than their planar counterparts (i.e. the planar profiles exhibiting the same FoS) up to 8 degrees depending on the rock type and severity of constraints on local inclinations. The design of a copper mine is first carried out employing the planar pitwalls, secondly adopting the optimal pitwall profiles, determined by the OptimalSlope. The adoption of optimal slope profiles leads to a 34% higher net present value and reductions of carbon footprint and energy consumption of 0.17 Mt CO2 eq and 82.5 million MJ respectively due to a 15% reduction of the rock waste volume.

Determining a set of nested pits to support the design of an open pit mine that leads to high economic value is crucial for the strategic planning of these operations; thus, practitioners rely on optimization methods for finding high‐value solutions. However, current approaches are not sufficient as they lack at least one of the following features: fast computations of optimal solutions, good geometric properties, and nestedness of the pits. In this work, we propose an optimization model to address the problem of determining multiple nested pits by introducing a cost‐based penalty for not meeting precedence constraints linked to a minimum bottom width. Using penalties instead of constraints is novel and turns out to have several advantages. First, the constraint matrix is totally unimodular; thus, the problem can be solved efficiently. Second, the model can be parameterized to generate nested pits. Therefore, our model is the first published model that is efficient, can be solved to optimality, preserves the nestedness of the solutions, and produces geometries more amenable for mine design, without the need for heuristics. Finally, we devise an iterative method that profits from the nestedness of the solutions to speed up the resolution and test the model in three different data sets, with different geometrical and cost parameters for a total of 135 different instances. The results show that the geometry of the bottom pits is indeed improved and that we can solve the problems up to optimality up to 80% faster than an off‐the‐shelf solver.

The strategic planning of open pit operations defines the best strategy for extraction of the mineral deposit to maximize the net present value. The process of strategic planning must deal with several sources of uncertainty; therefore, many authors have proposed models to incorporate it at each of its stages: Computation of the ultimate pit, optimization of pushbacks, and production scheduling. However, most works address it at each level independently, with few aiming at the whole process. In this work, we propose a methodology based on new mathematical optimization models and the application of conditional simulation of the deposit for addressing the geological uncertainty at all stages. We test the method in a real case study and evaluate whether incorporating uncertainty increases the quality of the solutions. Moreover, we benefit from our integrated framework to evaluate the relative impact of uncertainty at each stage. This could be used by decision-makers as a guide for detecting risks and focusing efforts.

The incorporation of autonomous equipment is fuelling a revolution in mining, but it poses operational challenges due to potential hazardous interactions between humans and the autonomous machinery. For this reason, block and panel caving mines operate under a static confinement policy that stops all autonomous LHDs when workers must enter the production area, thus having a huge negative impact on productivity. To address this challenge, we propose two dynamic confinement policies, and we model them using discrete event simulation. Results show that the dynamic moderate/intensive policies may lead to increases in production of 35%/45% when compared with the static policy.