Stochastic optimization of mining complexes integrating capital investments and operational alternatives (original) (raw)
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Dynamically optimizing the strategic plan of mining complexes under supply uncertainty
Resources Policy, 2018
Mining complexes are comprised of multiple mines and mineral processing streams, each governed by internal (mineral deposit, operation) and external (commodity prices) uncertainties, and must be optimized jointly to manage technical risk and maximize economic value. This study presents a method that optimizes annual production scheduling of an open pit mining complex by developing a solution that provides a unique strategic mine plan that integrates feasible alternatives over investment decisions along the life of the asset. Accordingly, the long-term optimization is presented as a dynamic plan, which allows planning upfront for possible configuration transitions due to new capital investments, facilitating change. This method uses an adapted multistage stochastic programming model which expands upon the two-stage framework by performing multiple recourse stages that are solved iteratively, allowing feasible mine designs in a scenario-tree structure. In this model, dynamic investment decisions are made sequentially over the mine production schedule of related mines, based on new information that becomes available in each time period; these decision variables activate costs and effects over the model, letting the optimizer choose the capital investments to be considered at the mining and/ or processing components of the mining complex. A copper open pit mining complex is used to test the proposed model, with options to invest in the truck and shovel fleet, and a secondary crusher to increase related capacities. Results show a substantial probability that the mine design should branch, presenting an increased expected net present value of over US$170M compared to the two-stage stochastic formulation.
Simultaneous Stochastic Optimization of Mining Complexes and Mineral Value Chains
Recent developments in modelling and optimization approaches for the production of mineral and energy resources have resulted in new simultaneous stochastic optimization frameworks and related digital technologies. A mining complex is a type of value chain whereby raw materials (minerals) extracted from various mineral deposits are transformed into a set of sellable products, using the available processing streams. The supply of materials extracted from a group of mines represents a major source of uncertainty in mining operations and mineral value chains. The simultaneous stochastic optimization of mining complexes, presented herein, aims to address major limitations of past approaches by modelling and optimizing several interrelated aspects of the mineral value chain in a single model. This single optimization model integrates material extraction from a set of sources along with their uncertainty, the related risk management, blending, stockpiling, non-linear transformations that occur in the available processing streams, the utilization of processing streams, and, finally, the transportation of products to customers. Uncertainty in materials extracted from the related mineral deposits of a mining complex is represented by a group of stochastic simulations. This paper presents a two-stage stochastic mixed integer nonlinear programming formulation for modelling and optimizing a mining complex, along with a metaheuristic-based solver that facilitates the practical optimization of exceptionally large mathematical formulations. The distinct advantages of the approach presented herein are demonstrated through two case studies, where the stochastic framework is compared to past approaches that ignore uncertainty. Results demonstrate major improvements in both meeting forecasted production targets and net present value. Concepts and methods presented in this paper for the simultaneous stochastic opti
INTERNATIONAL JOURNAL OF MINING, RECLAMATION AND ENVIRONMENT, 2022
This paper overviews the simultaneous stochastic optimisation of mining complexes or mineral value chains where raw materials mined from mineral deposits in an area are transformed into a set of sellable products. The supply of materials extracted from available mines represents a major source of uncertainty and technical risk that needs to be managed, along with market demand. An overview of the main concepts, case studies and comparisons show how the approach manages risk and capitalises on synergies between the components of the mining complex and major differences from conventional methods. Results lead to strategic plans with larger amounts of metal produced from the same mineral resources, a substantially improved ability for operations to meet production forecasts, and a significantly higher net present value.
2015
Previous research related to the optimization of mining operations has predominantly focused on generating a life-of-mine production schedule that maximizes the discounted cash flows of the material extracted and products produced. Stochastic optimization models address the issue of integrating uncertainty into the decision-making, leading to mine designs and production schedules with higher value and better risk management, thus helping to ensure that the mining operation is capable of meeting production targets over time. More recent models address the challenge of stochastic global optimization, which aim to holistically optimize a mining complex, from the production schedule, through to the products created, marketed and sold. Existing stochastic formulations, however, assume that the bottlenecks in the mining complex, such as mine production and milling capacities, have been defined a-priori, thus ignore the impact that the quantity and timing of capital expenditures required t...
Dynamic mid-term optimization of a mining complex under uncertainty
2017
This study presents a production scheduling optimization method for a mining complex, which provides a flexible long-term plan for future investments and operational decisions. This strategic planning method uses an adapted two-stage SIP model which expands upon the two-stage framework by performing multiple recourse stages that are solved iteratively, allowing parallel designs in a scenario-tree structure. In this model, dynamic decisions are made sequentially over time, based on new information. A case study with options to invest over trucks and a secondary crusher show an increased expected NPV compared to the two-stage stochastic formulation. Les Cahiers du GERAD G–2017–79 1
Global asset optimization of open pit mining complexes under uncertainty
2014
Global asset optimization aims to simultaneously optimize mine production schedules, destination policies and the various processing streams in order to maximize the economic value over the life of a mineral resource supply chain. Conventional mine optimization approaches are incapable of incorporating geological uncertainty and may lead to severe deviations from forecasted production targets. Stochastic optimization models that manage risk in mine design and production scheduling have been developed over the past several years, however these models are often oversimplified, thus limited to provide only a local optimum in terms of the mining complex as a whole. This paper addresses the issue of global optimization of open pit mine production schedules for complex mining supply chains under geological uncertainty. The proposed simulation-optimization framework builds on previous mining supply chain optimization work by permitting extraction decisions to be made simultaneously with ma...
2015
Mining complexes contain multiple sequential activities that are strongly interrelated. Extracting the material from different sources may be seen as the first main activity, and any change in the sequence of extraction of the mining blocks modify the activities downstream, including blending, processing and transporting the processed material to final stocks or ports. Similarly, modifying the conditions of operation at a given processing path or the transportation systems implemented may affect the suitability of using a mining sequence previously optimized. This paper presents a method to generate mining, processing and transportation schedules that account for the previously mentioned activities (or stages) associated with the mining complex simultaneously. The method uses an initial solution generated using conventional optimizers and improves it by mean of perturbations associated to three different levels of decision: block based perturbations, operating alternative based perturbations and transportation system based perturbation. The method accounts for geological uncertainty of several deposits by considering scenarios originated from combinations of their respective stochastic orebody simulations. The implementation of the method in a multipit copper operation shows its ability to reduce deviations from capacity and blending targets while improving the expected NPV (cumulative discounted cash flows), which highlight the importance of stochastic optimizers given their ability to generate more value with less risk.
The production scheduling of open pit mines is an intricate, complex and difficult problem to address due to its large scale and the unavailability of a truly optimal net present value (NPV) solution, as well as the uncertainty in key parameters involved. These key factors are geological and mining, financial and environmental. Geological uncertainty is a major contributor in failing to meet production targets and the financial expectations of a project especially in the early stages of a project. Stochastic integer programming (SIP) models provide a framework for optimising mine production scheduling considering uncertainty. A specific SIP formulation is shown herein that generates the optimal production schedule using equally probable simulated orebody models as input, without averaging the related grades. The optimal production schedule is then the schedule that can produce the maximum achievable discounted total value from the project, given the available orebody uncertainty described through a set of stochastically simulated orebody models. The proposed SIP model allows the management of geological risk in terms of not meeting planned targets during actual operation, unlike the traditional scheduling methods that use a single orebody model and where risk is randomly distributed between production periods while there is no control over the magnitude of the risks on the schedule. Notably, the testing of the SIP formulation in two cases, a gold and a copper deposit, shows that the expected total NPV of the schedule using the SIP approach is significantly higher (10 and 25% respectively) than the traditional schedule developed using a single estimated orebody model.
A platform for optimizing mining complexes with uncertainty
2016
Over the past several years, there has been substantial progress in developing new stochastic mine planning optimization models and computationally efficient solvers that are capable of managing risk throughout the entire mineral value chain. Recent models have focused on modelling the economic value of the products sold to customers, rather than attempting to evaluate the economic value of the individual mining blocks. This modelling shift permits the integration of many aspects that could not be previously considered, such as the ability to incorporate non-linear geometallurgical interactions in the processing streams. These advanced models have consistently shown the ability to generate a higher net present value than traditional deterministic optimization methods, which is a direct result of risk management. Despite these developments and consistent value-added results, existing commercial mine planning optimization tools have been reluctant to incorporate these new concepts in ...