Incorporation of geometallurgical modelling into long-term production planning

Navarra, Alessandro; Grammatikopoulos, Tassos; Waters, Kristian E.

doi:10.1016/j.mineng.2018.02.010

Cited by 27 publications

(17 citation statements)

References 14 publications

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“…A common approach is estimating some geometallurgical attributes at the block level, but assuming there is no complex interaction between blocks [Morales et al, 2019, Maleki et al, 2020. Another common approach is considering a processing facility flexibility to change the operational mode to deal with different geometallurgical properties [Navarra et al, 2018, Gerald Van Den Boogaart et al, 2011. But works that deal directly with the nonlinearity and nonadditivity of geometallurgical variables are scarce and mostly focused on the long-term mine planning horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

Column Generation for Mining Cut Definition with Geometallurgical Interactions

2021

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In this work we propose a novel approach to solve the mining cut definition problem in open pit mines with geometallurgical interactions. Mining cut definition deals with the aggregation of blocks into clusters which are extracted and processed as a single unit. The aggregation must fulfill operational considerations given by the loading equipment selectivity and should maximize the objectives given by the mine operation.The proposed approach utilizes mixed integer programming and a model inspired by column generation that, contrarily to previous works, has its decision variables defined directly on the set of all feasible cuts. The advantage of this is that the model does not require linear approximations of the geometallurgical behavior of the cuts based on the blocks it contains, and therefore, can utilize any non-linear function.We apply our methodology to a real-size case study to show that the model can be solved in reasonable time, but also that non-linear recovery functions influence the destination policy and expected profit, i.e., that following the traditional free selection policy (based on cut-off grade) is not the best strategy when realistic geometallurgical interactions are considered.

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Section: Literature Reviewmentioning

confidence: 99%

Column Generation for Mining Cut Definition with Geometallurgical Interactions

2021

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“…This can be achieved by modelling spatial variability and uncertainty of the deposit throughout the value chain. Modelling seeks high NPV options that are robust in the face of potential scenarios across the LOM [93].…”

Section: Financial Modellingmentioning

confidence: 99%

Geometallurgy—A Route to More Resilient Mine Operations

et al. 2018

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Geometallurgy is an important addition to any evaluation project or mining operation. As an integrated approach, it establishes 3D models which enable the optimisation of net present value and effective orebody management, while minimising technical and operational risk to ultimately provide more resilient operations. Critically, through spatial identification of variability, it allows the development of strategies to mitigate the risks related to variability (e.g., collect additional data, revise the mine plan, adapt or change the process strategy, or engineer flexibility into the system). Geometallurgy promotes sustainable development when all stages of extraction are performed in an optimal manner from a technical, environmental, and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire mine value chain are being established. Geometallurgy has been shown to intensify collaboration among operational stakeholders, creating an environment for sharing orebody knowledge and improving data acquisition and interpretation, leading to the integration of such data and knowledge into mine planning and scheduling. These aspects create better business optimisation and utilisation of staff, and lead to operations that are more resilient to both technical and non-technical variability. Geometallurgy encompasses activities that utilise improved understanding of the properties of ore and waste, which impact positively or negatively on the value of the product, concentrate, or metal. Properties not only include those that impact on processing efficiency, but also those of materials which will impact on other actions such as blasting and waste management. Companies that embrace the geometallurgical approach will benefit from increased net present value and shareholder value.

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“…Nevertheless, for open-pit mining, managing stochasticity has shown significant improvements in expected NPV, increasing of the likeliness of meeting production forecast, and finding pit limits larger than those found by deterministic approaches (Lamghari, Dimitrakopoulos, and Ferland, 2014b). This is the main reason why stochastic optimization continues to be an active area of research (Navarra, Grammatikopoulos, and Waters, 2018a).…”

Section: Introductionmentioning

confidence: 99%

“…Among these formulations, it is possible to find reserve, slope, mining capacity, processing capacity, and stockpiling constraints (Lamghari et al, 2014a;Lamghari and Dimitrakopoulos, 2016). The features include metal uncertainty (Lamghari Dimitrakopoulos, and Ferland, 2014b), geometallurgical modelling (Navarra, Grammatikopoulos, and Waters, 2018a), supply uncertainty (Goodfellow and Dimitrakopoulos, 2016;Senecal and Dimitrakopoulos, 2020), the actions of mineral concentrators (Navarra et al, 2017), prevention of excessive movement of mining equipment between benches (Gholamnejad, Lotfian, and Kasmaeeyazdi, 2020), and commodity price uncertainty (Rimélé, Dimitrakopoulos, and Gamache, 2020). Montiel and Dimitrakopoulos (2015) have extended their focus beyond a single mine and consider the production planning of a 'mining complex'.…”

Section: Introductionmentioning

confidence: 99%

Integration of strategic open-pit mine planning into hierarchical artificial intelligence

Quelopana

Navarra

2021

J. S. Afr. Inst. Min. Metall.

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The mine production scheduling problem (MPSP) has been studied since the 1960s, and remains an active area of computational research. In extending the concepts of the MPSP, the automated mine may now be regarded as a hierarchical intelligent agent in which the bottom layer consists of distributed robotic equipment, while strategic functionality occupies the higher layers. Here we present a disambiguation of artificial intelligence, machine learning, computational optimization, and automation within the mining context. Specifically, the Q-learning algorithm has been adapted to generate the initial solutions for a high-performing strategic mine planning algorithm, originally developed by Lamghari, Dimitrakopoulos and Ferland, based on the variable neighbourhood descent (VND) metaheuristic. The hierarchical intelligent agent is presented as an integrative conceptual platform, defining the interaction between our new Q-learning adaptation and Lamghari's VND, and potentially other hierarchically controlled components of an artificially intelligent mine, having various degrees of automation. Sample computations involving Q-learning and VND are presented.

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Incorporation of geometallurgical modelling into long-term production planning

Cited by 27 publications

References 14 publications

Column Generation for Mining Cut Definition with Geometallurgical Interactions

Column Generation for Mining Cut Definition with Geometallurgical Interactions

Geometallurgy—A Route to More Resilient Mine Operations

Integration of strategic open-pit mine planning into hierarchical artificial intelligence

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