A collaborative model planning to coordinate mining and smelting furnace

International audienceIn this paper, we are interested in the tactical planning problem of mines and smelting furnace. The problem concerns a set of mines with one smelting furnace. We are faced to a multi-actor’s context for which a global optimization is not possible due to the independence of the services. This problem is solved using a set of local optimization model of mines bloc extraction and a model of smelting furnace. This paper begin with the state of the art related to the principal problems in mining process. It justifies the novelty of our work. Indeed, this paper aims to discuss on the impact of sharing information between downstream processes and upstream processes. Consequently, after the state of the art, the classical planning process using local optimization and the information sharing process are presented. In the following part, profits generated and related to different contexts: value-creation and approach are compared. At the end of the paper, conclusion and future extensions are presented

Planning and Optimisation Methods for Lunar In-Situ Resource Utilisation

Lunar water resources are expected to be used for space exploration and development in the future. These resources can be used for life support and rocket fuel to reduce the risks and costs associated with lunar settlement. There is a notable gap in literature relating to the planning and optimisation of lunar resource extraction. This thesis aims to address the problem by developing tools for planning and optimisation of In-Situ Resource Utilisation (ISRU) on the Moon, with a focus on H2O resources. The multidisciplinary tools currently used in the terrestrial mining industry are examined as possible solutions to fill the gap. However, several issues are identified with the direct transfer of these methods to ISRU. Four foundational areas of mining engineering are then expanded for off-Earth applications. These are geomechanics and modelling, mining system selection, extraction sequence optimisation and project valuation. For geomechanics, the Discrete Element Method (DEM) is used to determine the stability of regolith excavations on the Moon. This method is also extended to the development of ground engaging tools under lunar gravity. Conceptual proofs are shown for two novel mining systems using DEM, the Impact Excavator and Drill and Pull method. With further development, these new rock breakage systems can improve ISRU planning and optimisation by enabling the access of harder, higher grade icy regolith. Within literature, there are also numerous off-Earth mining systems described. A procedure is developed to objectively select a mining system for a range of possible space resource deposit types. The procedure utilises principles of Axiomatic Design to estimate the reliability of systems in the absence of experimental data. These system reliabilities assist in making selections that can be used as inputs for subsequent planning and optimisation activities. Traditional optimisation algorithms, such the Lerchs-Grossman pit optimisation method and other graph-based methods are next examined for their applicability to off-Earth mining. They are found to be incompatible when directly applied to ISRU and a new paradigm is developed based on Reinforcement Learning. This method has advantages over the traditional mine optimisation algorithms and solves many of the issues identified for ISRU. For example, it does not require uncertain financial inputs such as cost estimations or price forecasting. This particular weakness in financial inputs for off-Earth mine planning is also addressed for project valuations. An opportunity cost measure, the Propellant Payback Ratio, is shown to overcome many of the difficult input requirements of the traditional method for the purpose of ISRU project appraisal. It enables ISRU project appraisals to be conducted completely independent of the uncertain financial inputs mentioned. Overall, the thesis contributes to the expansion of the mining engineering discipline into the ISRU domain. Four interconnected areas of mining engineering are developed including: geomechanics, mining system selection, sequence optimisation and project appraisal. These are all part of a multidisciplinary approach to ISRU planning and optimisation. Although ISRU has so far not begun, the methods and tools developed here can be used to improve the future prospect of resource utilisation on the Moon

Optimised decision-making under grade uncertainty in surface mining

Mining schedule optimisation often ignores geological and economic risks in favour of simplistic deterministic methods. In this thesis a scenario optimisation approach is developed which uses MILP optimisation results from multiple conditional simulations of geological data to derive a unique solution. The research also generated an interpretive framework which incorporates the use of the Coefficient of Variation allowing the assessment of various optimisation results in order to find the solution with the most attractive risk-return ratio

Life cycle inventory uncertainty in resource-based industries : a focus on coal-based power generation

The aim of this thesis is to develop an approach to support prospective environmental decision-making in resource-based industries. The specific focus is on coal-based power generation. The objectives of the approach are that it be able to adequately reflect the environmental burdens arising from primary industries, and to make explicit the trade-offs often encountered in environmental decisions. In addition, it needs to take into account that the context in which the assessment takes place affects data availability and quality significantly, and consequently the certainty with which systems can be evaluated. Resource-based processes typically involve large-scale disruption of the local and regional environments, with imprecise processes and diffuse emissions. The modelling of the environmental performance of such processes therefore raises significant challenges, where many disparate sources of data, available at different levels of aggregation, and over various time intervals, have to be brought together into a coherent assessment. An "uncertain" definition of the system is therefore much more meaningful, in which variables are defined over ranges of values to cover inconsistencies and imbalances in the system. The inherently high variability of mining and minerals processes further supports their modelling as ranges of potential performance rather than "typical" operations, where the relevant process of interest must be identified and the variability within the particular process incorporated into the assessment Life cycle assessment (LeA) has received increasing attention for its role in environmental decision making processes, where it supports the process of defining the contribution of human activities to (at least the environmental dimension of) sustainable development. It is therefore the structured approach to environmental decision-making investigated in this thesis to organise the large data sets of varying quality and completeness available around resource-based industries into useful information, able to provide the environmental objective in a decision-making process. LeA is an inherently uncertain procedure in that it combines data sources of varying accuracy and representativeness, and employs subjective judgement in applying this data to future operating systems. Subjective judgements are also present in the definition of the systems, and in the modelling choices determining the accuracy and complexity of the inventory and impact models used. Nonetheless, LeA results are most often presented as single values, which in a comparative analysis, gives the often incorrect impression that one system is always better or worse than another system. A framework has been developed in this thesis to include all relevant sources of uncertainty encountered in LCA models explicitly, where empirical parameter uncertainty, model parameter uncertainty, and uncertainty in model form are investigated in a looped fashion. The innermost loop assesses empirical uncertainty in an iterative probabilistic analysis, using Latin Hypercube sampling of the uncertain input distributions to propagate the data uncertainty to the output, and rank-order correlation analyses to determine the relative uncertainty importance of the parameters input into the model. Model parameter uncertainty is assessed next, by a parametric analysis, or by a combination of sensitivity analyses and a parametric analysis, if a large number of model parameters require consideration. The top-most layer is an assessment of model form, in which alternative model forms are investigated in a sensitivity analysis

A Multi-stage methodology for long-term open-pit mine production planning under ore grade uncertainty

ABSTRACT: 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

Optimal design and control of mine site energy supply systems.

The mining sector has seen an increase in costs associated with the use of energy in recent decades. Due to lower ore grade, deeper mineralization, or more remote location new mines generally require more energy to produce the same amount of mineral. Mining operations require reliable and cost-effective energy supply, without which extraction becomes economically risky, as well as unsafe for miners. Commercial software and research-oriented computer models are now available to assist in the decision making process regarding the optimal selection of Energy Supply Systems (ESS) and associated costs. However, software and models present limitations: some are designed to minimize the cost of supplying only heat and electricity, while others are custom applications for the residential and commercial sectors. Most computer tools assume invariable operating conditions, e.g. energy supply and demand profiles that do not change throughout the lifetime of the mine, or conditions whose variations can be perfectly predicted. As a result, the optimization of ESS can yield designs that lack robustness to deal with real life, changing environments. Under the same approach, the Optimal Mine Site Energy Supply (OMSES) concept was originally developed as a deterministic mathematical programming tool to find the optimal combination of energy technologies and sources that could meet final energy demands. The solution also included the optimal operation strategy based on typical energy demands of a specific mine site. This thesis expands OMSES to address the robustness of the solution, by considering the uncertainty and variability of real operating conditions. A method is proposed herein, based on the optimal solution obtained by OMSES and utilizing Model Predictive Control (MPC). The MPC-based simulation under changing environmental conditions ensures that energy demands are met at all times, taking into account energy demands and supply forecast, as well as their inherent variability. Results show that near optimal, more robust design solutions are obtained when the system is simulated under uncertain, more realistic operational conditions, leaving MPC in charge of exploring under-capacity events and of redesigning the system to ensure feasibility with minimum cost increase. This new method has been termed MPC-OMSES dynamic redesign. This thesis also reports on research work to adapt OMSES formulation to account for varying demands throughout the life of the mine, as a consequence of the natural process of mine development and extraction, which means deeper operations over time. This process entails a progressive increase in energy demands, and therefore the energy supply system must be planned accordingly. The proposed Long Term OMSES (LTOMSES) shows the advantages of considering an investment plan for the ESS, especially in the case of capital-intensive renewable energy technologies. Other concepts that have been integrated in OMSES and are covered in this thesis include: (i) material flows with considerable impact in the energy consumption have been included in the mathematical formulation, in combination with the corresponding technologies, such as pumps, fans and mobile equipment; (ii) energy and material storage have been also included, along with complex utility tariff structures, and grid and pipeline extensions. More innovative and integrated solutions can be considered by expanding the feasibility region of the optimization problem, as shown in a case study covering the integration of battery-powered electric underground mobile equipment. Overall, this thesis provides insight and tools to assist engineers in the important task of designing comprehensive and cost-effective energy supply systems for underground mines. Future work suggested includes: the development of a methodology to design fully adaptive ESS (not considering a pre-existing optimal or sub-optimal design); the simultaneous optimization of the production plan (ore extracted per day) and the design and operation of the ESS; and a dynamic approach to review the investment plan in the face of long-term environmental operating conditions.Doctor of Philosophy (PhD) in Natural Resources Engineerin

Optimisation de la planification stratégique d’une mine à ciel ouvert en tenant compte de l’incertitude géologique

RÉSUMÉ: Pour l’industrie minière, la planification est une étape critique impliquant plusieurs niveaux de décisions. Ces décisions se prennent à chaque maillon de la chaine d’approvisionnement d’un complexe minier à savoir l’extraction, le transport, le stockage, le concassage, le traitement, etc. La complexité des problèmes de planification est modulable selon le degré de détails qu’on veut considérer et le nombre de composantes de la chaine qu’on veut intégrer. Cette thèse s’intéresse aux problèmes de planification stratégique des mines à ciel ouvert dans un contexte d’incertitude géologique. L’objectif principal porte sur le développement d’un outil mathématique efficace et robuste pour soutenir les compagnies minières dans leurs processus de prise de décision. Pour ce faire, différentes variantes du problème ont été à l’étude, considérant, entre autres, plusieurs destinations et plusieurs éléments géologiques d’intérêt et incluant aussi des options d’investissement. Dans le premier article, un modèle de base est présenté. À partir d’une représentation du gisement discrédité en blocs, on cherche à déterminer quand, le cas échéant, extraire chaque bloc et où l’envoyer : vers le stérile ou les usines de traitement. Cet ordonnancement doit être choisi de sorte que les profits générés par l’exploitation du gisement soient maximisés tout en minimisant les déviations des objectifs de production et en respectant les liens de préséance existants entre les blocs ainsi que les contraintes de ressources. Pour cet article, l’emphase est surtout mise sur la méthode de résolution qui servira de gabarit pour les autres variantes. Il s’agit d’une méthode de décomposition (basée sur l’approche de Bienstock et Zuckerberg) combinée avec une heuristique d’arrondissement et une recherche Tabou (RT). Les résultats obtenus, tant au niveau de la qualité de la solution que le temps de résolution, ont motivé l’extension du modèle en deux variantes dans les articles 2 et 3 tout en conservant plus ou moins la même stratégie de résolution. Le deuxième article intègre les piles de minerai au modèle précédent. Le défi était de correctement modéliser le flux de matière au niveau des piles en considérant les limitations des méthodes d’optimisation existantes. Pour ce faire, un nouveau modèle linéaire a été développé. Ce dernier rompt avec les modèles classiques qui assument une homogénéisation parfaite des matériaux une fois arrivés dans la pile et propose une toute nouvelle approche permettant une estimation exacte du contenu des piles. Les limitations de cette formulation sont discutées et des recommandations pour y remédier sont aussi suggérées. Comme troisième objectif, une certaine forme de flexibilité est rajoutée au modèle en intégrant des options d’investissement sur de nouveaux équipements. On montre que cette flexibilité permet d’augmenter la production et générer ainsi plus de profits. Pour résoudre cette variante, des adaptations ont dû être apportées à la méthode de résolution initiale. Une parallélisation au niveau de la RT a notamment été implémentée afin d’améliorer les temps de calcul de cette étape.----------ABSTRACT: In the mining industry, planning is a critical step involving multiple decision levels. These decisions are made at each stage of the mineral value chain in a mining complex, namely extraction, transportation, storage, crushing, processing, etc. The complexity of scheduling problems can be varied according to the degree of details we want to consider and the number of components of the chain we want to integrate. This thesis addresses the open pit mine strategic planning problem under geological uncertainty. The main objective is to develop an effective and robust mathematical tool to support mining companies in their decision-making processes. In order to achieve this, different variants of the problem have been studied, considering, among others, several destinations and multiple geological elements of interest and including investment options. In the first paper, a basic model is presented. Given a three-dimensional representation of the deposit discretized into blocks, the model seeks to determine when, if ever, to extract each block and where to send it: towards waste dump or processing facilities. This scheduling must be chosen in a way that the profits generated by the deposit exploitation are maximized while minimizing the deviations from the production targets and respecting the slope constraints as well as the resource constraints. For this paper, the emphasis is mainly on the solution approach that will be used as a template for the next variants. It is based on a decomposition method (originally presented by Bienstock and Zuckerberg) combined with a rounding heuristic and a Tabu search. The results obtained, both in terms of solution quality and running time, motivated the extension of the model to two variants in papers 2 and 3 while retaining the same resolution strategy’s structure. The second paper integrates stockpiling as part of the optimization process. The challenge was how to correctly model the material flow inside the stockpiles considering the limitations of existing optimization methods. To tackle that, a new linear model has been developed. The latter breaks with the classic models that assume homogeneous mixing of the material once arrived in the stockpile and proposes a new approach for an accurate estimation of the stockpile content. The limitations of this formulation are discussed and recommendations to bypass them are also suggested. As third objective, some form of flexibility is added to the model by including capital expenditure options. This flexibility allowed to increase production tonnage and thus generate more profits. To solve this variant, some adaptations had to be made to the initial solution approach. In particular, a parallelization at the level of the Tabu search heuristic was implemented in order to speed-up this step