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Author: Michael Spleit Publisher: ISBN: Category : Languages : en Pages :
Book Description
"In long-term production scheduling, which is of vital importance to a project's success and profitability, the goal is to determine a feasible extraction sequence that maximizes the discounted cash flows of a mine while also ensuring the target ore quantities and qualities are met. There is risk of the actual production deviating from what is planned due to geological variability, which is not considered by conventional mine designs and production schedules that are based on a single estimated ore body model. In order to address this issue, multiple simulations of an orebody can be created to represent its geological variability and allow for quantifying expected bounds, instead of single estimates, for grades, tonnages, and financial results. Beyond simply quantifying the geological uncertainty, a mine production schedule can be optimized while directly considering simulations in order to manage the geological risk.In this study, a set of geological simulations of the LabMag iron ore deposit in Labrador, Canada is generated in order to quantify the geological variability in an existing mining schedule and assess the schedule's performance. The 'DBMAFSIM' algorithm is used to provide joint geostatistical simulation of spatially correlated variables of interest. First, a novel application of the method is used to jointly simulate the thicknesses of seven lithological layers, and then four correlated grades within each lithology are jointly simulated. The variability in an existing production schedule, designed based on a single deterministic geological model, is then evaluated using the simulations. This evaluation quantifies the potential deviations from expected production target grades and tonnages as well as the associated financial impact of these deviations. Subsequently, a production schedule optimization based on stochastic integer programming (SIP) is presented that aims to improve mine profitability while simultaneously managing the risk of production tonnage and quality deviations. In addition, the formulation has components for equipment and waste material management: the truck fleet requirements are minimized while ensuring that the number of required trucks is an increasing function to avoid unnecessary peaks; and the evolution of the pit is controlled so that space within the mined out pit is continuously provided to allow for tailings and waste rock to be replaced, thus minimizing the project's environmental footprint." --
Author: Michael Spleit Publisher: ISBN: Category : Languages : en Pages :
Book Description
"In long-term production scheduling, which is of vital importance to a project's success and profitability, the goal is to determine a feasible extraction sequence that maximizes the discounted cash flows of a mine while also ensuring the target ore quantities and qualities are met. There is risk of the actual production deviating from what is planned due to geological variability, which is not considered by conventional mine designs and production schedules that are based on a single estimated ore body model. In order to address this issue, multiple simulations of an orebody can be created to represent its geological variability and allow for quantifying expected bounds, instead of single estimates, for grades, tonnages, and financial results. Beyond simply quantifying the geological uncertainty, a mine production schedule can be optimized while directly considering simulations in order to manage the geological risk.In this study, a set of geological simulations of the LabMag iron ore deposit in Labrador, Canada is generated in order to quantify the geological variability in an existing mining schedule and assess the schedule's performance. The 'DBMAFSIM' algorithm is used to provide joint geostatistical simulation of spatially correlated variables of interest. First, a novel application of the method is used to jointly simulate the thicknesses of seven lithological layers, and then four correlated grades within each lithology are jointly simulated. The variability in an existing production schedule, designed based on a single deterministic geological model, is then evaluated using the simulations. This evaluation quantifies the potential deviations from expected production target grades and tonnages as well as the associated financial impact of these deviations. Subsequently, a production schedule optimization based on stochastic integer programming (SIP) is presented that aims to improve mine profitability while simultaneously managing the risk of production tonnage and quality deviations. In addition, the formulation has components for equipment and waste material management: the truck fleet requirements are minimized while ensuring that the number of required trucks is an increasing function to avoid unnecessary peaks; and the evolution of the pit is controlled so that space within the mined out pit is continuously provided to allow for tailings and waste rock to be replaced, thus minimizing the project's environmental footprint." --
Author: Maria Vallejo Garcia Publisher: ISBN: Category : Languages : en Pages :
Book Description
"The process of mine planning, from prospection of orebody deposits defining its extension, location and value, until obtaining minerals and their extraction sequence in time, requires mathematical optimization to determine the size and grade (grades) of the deposit and finally define a proper mine schedule to obtain the maximum earnings from it, at the lowest possible cost, in order to fulfill the business targets. In general, a mining project passes through a set of stages in order to evaluate its viability. One of these stages is the "feasibility stage" where the gathered information from mining studies is used to determine the economics and practical aspects of the ore deposit. This identifies, early on, whether further investment in estimation and engineering studies are required, and identifies areas for further work and development. The KéMag iron ore deposit owned by New Millennium Limited in northern Quebec, Canada, is in feasibility stage. The KéMag iron ore deposit is a taconite type, where the iron content is present as finely dispersed magnetite between 20 and 35 % iron (Fe) in a sedimentary rock interlayered with quartz, chert, and carbonate. This thesis focuses on a set of methodologies to develop a whole process of stochastic orebody modelling and stochastic strategic mine planning for the KéMag deposit, aiming to generate a modelling and optimization methodology that integrates geological uncertainty and manages risk in the mine schedule. This mathematical framework has been successfully implemented in the last two decades allowing modelling and integration of geological uncertainty to mine design, production scheduling and valuation of mining projects. From the application, several cases have shown an increment on the value of the production schedules up to 25%, and a reduction of deviation from production targets from 9% to 0.2%. For the KéMag deposit a set of fifteen realizations of nine lithological units (layers) were simulated using WAVESIM which is a multiple point simulation method combined to an image compression procedure to allow faster simulations. The orebody simulations obtained through WAVESIM serve as geological boundaries to integrate the variability of the four grades of interest using DBMAFSIM this method allows the simulation of correlated variables directly at block support using min/max autocorrelation factors MAF. The final result is a series of equally probable representations of the deposit that incorporate both grade and tonnage uncertainty. These simulations of the KéMag deposit were validated in terms of histograms, variograms (low order statistics) and high-order statistics through 3rd order cumulants maps for the boundary limits only. Geological uncertainty can then, be managed by directly incorporating stochastic simulations within the mine scheduling framework. To achieve this, one flexible method for long-term production scheduling based on Stochastic Integer Programming (SIP) was applied with an acceleration methodology based on a heuristic algorithm called Topological Sort Algorithm (TSA) to reduce the computational time required to solve the problem of production scheduling. The result of the stochastic mine planning framework is a single schedule robust enough to account for geological uncertainty of the KéMag deposit giving valuable information for the conceptual stage of the project, in terms of silica content, iron production and expected cash flows per year." --
Author: Matthew Quigley Publisher: ISBN: Category : Languages : en Pages :
Book Description
"Mine production scheduling consists of defining the extraction sequence and process allocation of mineralized material over some length of time. These decisions can be made at different time steps, which will entail varying objectives subject to different technical and operational constraints. Long-term mine production scheduling usually takes place at an annual scale for the entire life of mine and aims to maximize the net present value of the project while satisfying the mining and processing capacities. Short-term mine production scheduling consists of developing an extraction sequence on a shorter time scale, either months, weeks, or days. The goal is typically to maximize compliance with the production targets imposed by the long-term plan while considering more detailed operational constraints. Historically, these optimization frameworks have relied on the assumption of perfect knowledge of highly uncertain inputs. Developments in the field of stochastic mine planning have shown that incorporating uncertainty into the optimization of mine production schedules can add significant economic value while also minimizing the risk of deviating from production targets. This thesis will explore the benefits that stochastic mine planning can offer when applied to both long and short-term production scheduling problems.For the first exercise, the long-term mine production schedule of a rare earth element (REE) project is generated under geological uncertainty using a stochastic optimization framework. The uncertainty in REE grades is modelled using an efficient joint-simulation technique to preserve the strong cross-element relationships. The proposed approach avoids the use of the conventional total rare earth oxide grade. The stochastic long-term schedule is benchmarked against a deterministic schedule generated using an industry standard optimizer. The stochastic solution generates a 20% increase in expected NPV, ensures better utilization of the processing plant, and delivers a superior ore feed in terms of satisfying mineral and REE blending targets.For the second exercise, a formulation is proposed that simultaneously optimizes the short-term equipment plan and production schedule under both geological and equipment performance uncertainty. The proposed approach rectifies certain limitations of previous work in stochastic short-term planning by: incorporating a location-dependant shovel movement optimization; generating more realistic equipment performance scenarios; developing a new approach to facilitate more practical mine designs; and proposing model improvements to allow for a more efficient optimization of very large problem instances. The model is applied to a large copper mining complex and is compared to a more traditional approach, where the same formulation is implemented using averaged inputs for geology and equipment performance. The stochastic solution is more effective in mitigating the risk of deviating from tonnage targets at each processing destination, and the integration of equipment performance variability allows the stochastic optimizer to generate a block extraction sequence that is far more likely to be achieved." --
Author: Alexandre Marinho de Almeida Publisher: ISBN: Category : Languages : en Pages :
Book Description
"The maximization of mining project discounted cash flows by defining the best sequence of extraction of underground materials requires understanding the availability of uncertain metal quantities throughout the deposit. This thesis proposes two versions of a stochastic integer programming formulation based on surfaces to address the optimization of life-of-mine production scheduling, whereby the supply of metal is uncertain and described by a set of equally probable simulated orebody models. The first version of the proposed formulation maximizes discounted cash flows, controls risk of deviating from production targets and is implemented sequentially, facilitating production scheduling for relatively large mineral deposits. Applications show practical intricacies and computational efficiency. The second variant extends the first to a two-stage stochastic integer programming formulation that manages the risk of deviating from production targets. The sequential implementation is considered first for pit space discretization and it is followed by the life-of-mine production scheduling at a relatively large gold deposit. The case studies show the computational efficiency and suitability of the method for realistic size mineral deposits, with production targets controlled, risk postponed to later stages of production and improvements in expected NPV, when compared to deterministic industry practices." --
Author: Martha Villalba Matamoros Publisher: ISBN: Category : Languages : en Pages :
Book Description
"Mine production scheduling may be long-term or short-term based on the time period considered and the final objective. The optimization goal of short-term production scheduling is to minimize the mining cost expected from a mine while satisfying operational constraints, such as mining slope, grade blending, metal production, mining capacity and processing capacity; however some parameters may be uncertain, such as metal quality and fleet parameters. Traditional short-term production planning is carried out by two sequential optimizations, production schedule is defined at the first step and the available fleet is evaluated for this schedule as a second step, however; the fleet availability, hauling time and mining considerations do not influence the schedule decision. In addition, the fleet optimization algorithms do not consider uncertainty in their parameters and do not take into account the local mineralization of the deposit because a single possibly misleading total aggregated block tonnage is linked to each sector to be mined. The local mineralization or local scale variability between blocks assists in the blending process and metal quality control; however, the traditional short-term production scheduling is based on exploration drilling or a sparse data ore body model, while in practice grade control data or close spacing blasthole drilling classify the material as ore and waste because their short-scale information is not available at the time of the monthly short-term planning. The local variability is relevant in the short-term production scheduling to define the destination of the material.The short-term mine production scheduling in this thesis is developed as a single formulation where mining considerations, production constraints, uncertainty in the orebody metal quantity, as well as fleet parameters, are evaluated together to define a well informed sequence of mining that results in high performance at the mine operation. The formulation is implemented at a multi-element iron mine and the resulting monthly schedules show lower cost, minable patterns and, efficient fleet allocation, that ensures a higher and less variable utilization of the fleet over the conventional schedule approach.Uninformed and ultimately costly decisions can be taken because of imperfect geological knowledge or information effect. The orebody uncertainty may be updated by simulated future ore control data to account for local scale grade variability, and the information used to discriminate ore and waste in practice. Multi-element orebody uncertainty models are updated based on the correlation of exploration data and past ore control data, this orebody uncertainty is then used to optimize the short-term production scheduling that leads to better performance in terms of matching ore quality targets and delivering recoverable reserves." --
Author: Renaud Senecal Publisher: ISBN: Category : Languages : en Pages :
Book Description
"In open pit mine planning, the mine deposit is discretized into mining blocks, where the size of these mining blocks is defined by the mine's extracting equipment capacity and selectivity. Mining blocks are removed from the ground at different periods and sent to various destinations to be processed, stockpiled or dumped. Long-term production scheduling with multiple destinations is used in the mining industry, to provide guidelines for this extraction process, deciding the mining period and the destination policy that should apply for each mining block. The destination policy aims to optimize where to send the extracted material, in order to maximize the discounted cash flow according to the system capacity. Stochastic long-term production scheduling with multiple destinations includes the uncertainty associated with the grade's material in the optimization process, by maximizing the net present value, while reducing the risk of not meeting the different production targets at each destination. For deposits represented by a large number of mining blocks, the optimization leads to very complex and large mathematical programs, this cannot be solved to optimality using exact methods such as Branch and Bound. In this thesis, stochastic integer programming formulations are used to integrate the uncertainty directly into the optimization of the long-term production scheduling problem, and Parallel Tabu Search metaheuristics are presented as an approach to provide nearby optimal solution, in a reasonable amount of time. Two different approaches are presented here for the destination policy during the optimization process, based on the economic value of each block. The first approach uses a fixed destination policy, which sends each block to its more profitable destination before the optimization, whereas the second one considers optimizing the policy simultaneously within the optimization process of a life-of-mine schedule.The first part of this thesis, Chapter 3, presents three different implementations of parallel Tabu Search metaheuristics to solve a previously existing stochastic integer program, designed to provide optimal solution for the life-of-mine production schedule with multiples destinations, under geological uncertainty and under a fix destination policy. The first two methods allow a more extensive search of the solution space, the first using several independent Tabu Searches, whereas the second allows communication between the different Tabu Searches to broadcast information. The third method aims to provide a more intensive search by exploring different local area simultaneously, starting from a single solution. An application to a deposit of about 70,000 mining blocks is shown to assess the ability of all methods to generate a schedule with minimized deviations in practical amount of time.In the second part, Chapter 4, a stochastic integer program that jointly optimizes the destination and the year of extraction for each mining block is presented. A parallel multi-neighbourhood Tabu Search implementation is used to approximate the optimal solution of this formulation. The approach considers optimizing simultaneously both the destination and the period of extraction of each mining block by defining different types of neighbour solutions to explore. The computational complexity added by considering simultaneously extraction and destination variables is reduced by the use of a load balancing strategy to distribute the work equally among the different processors. An application at a deposit of about 100,000 mining blocks is made to show the ability of the method to generate a schedule where the production targets are met and the NPV is maximized in a practical amount of time." --
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
Abstract : Mineral deposits are the main assets for the mining industry. Mineral deposits are estimated based on the findings of exploration drilling. Complex host geology with variable grades and geological controls increases difficulty in resource estimation. In these situations, volume (tonnage) and grade are often over- or underestimated, resulting in inaccurate mine plan that leads to costly financial decisions. In this study, a multiple-point geostatistical method, namely Single Normal Equation Simulation (SNESIM) was applied to generate equiprobable orebody models for a copper deposit from Africa that helps to analyze the uncertainty of ore tonnage of the deposit. The grade uncertainty was evaluated by generating multiple realization of grade models using sequential Gaussian simulation within each equiprobable orebody models. The results are validated by generating the marginal distribution, and two- and three-point statistics. In addition, a comparative study is performed for the deterministic version, the stochastic version with grade uncertainty, and the stochastic version with volume and grade uncertainty. The results show that the orebody model with the maximum volume is 4.8% more than the average volume and the minimum volume is 5.1% less than the average volume. The grade simulation results demonstrate that the average grade for all simulations is 3.89%, but average grade for different simulations varied from 3.6% to 4.1%. The results also show that the volume and grade uncertainty model overestimated the orebody volume compared to the conventional orebody volume. The long-term production schedule is generated taking into account the volume and grade uncertainties from the orebody models, and satisfying mine production capacity and xi processing capacity constraints. The production schedule results for the volume and grade uncertainty-based model are compared to the production schedule generated from deterministic orebody model, and grade uncertainty-based model. The results demonstrated that the incorporation of both the volume and grade uncertainty significantly reduces the risk of deviation from the target. The results also show that incorporation of volume and grade uncertainty increases the net present value (NPV) of mining project, when compared to the mine plan generated from the deterministic model and stochastic model with only grade uncertainty. The results show that the production schedule generates high revenue over wide range of initial assumptions and the expected NPV is 3% higher than the deterministic version. A sensitivity analysis was also performed to understand the effect of penalty factor for deviating the constraints.
Author: Michael Spleit
Author: Michael Spleit
Author: Maria Natalia Vallejo
Author: Maria Vallejo Garcia
Author: Martha E. Villalba Matamoros
Author: Matthew Quigley
Author: Alexandre Marinho de Almeida
Author: Martha Villalba Matamoros
Author: Renaud Senecal
Author: Erkan Topal
Author: