Open Pit Mine Planning Research Articles

Integrated Optimization of Production Scheduling and Haulage Route Planning in Open-Pit Mines

In mining, deposits are divided into blocks, forming the basis for open-pit mine planning, covering production and haulage route planning. Current studies often stage optimization and lack the consideration of road capacity, leading to suboptimal solutions. A novel approach integrates production scheduling and haulage route planning through a bilevel optimization model. The upper-level model integrates ore mining constraints to establish a mixed-integer production scheduling model, minimizing haulage costs. Spatiotemporal correlation constraints for block mining are determined using a two-stage algorithm. The lower-level model incorporates road capacity, forming a haulage route optimization model based on multicommodity network flow. A solution algorithm with a distance penalty strategy facilitates feedback between the upper and lower levels, achieving optimal solutions. Tested on a real open-pit coal mine with over 5 million blocks, this approach reduces haulage costs by 10.06% compared to stage optimization. Additionally, this approach allows for adjusting haulage demand in both temporal and spatial dimensions, effectively preventing road congestion. This study advances rational mining processes and enhances the efficiency of open-pit mining haulage systems.

Sustainable long-term production planning of open pit mines: An integrated framework for concurrent economical and environmental optimization

As the global demand for mineral resources continues to rise, the mining industry's environmental impact has garnered increasing attention. Sustainable mining practices demand the incorporation of environmental considerations into every phase of planning and operation. This article addresses the critical need to consider and minimize the negative environmental impacts of the operation in the long-term planning of open pit mines, with a specific focus on mitigating greenhouse gas (GHG) and dust emissions. To achieve this, the article suggests an integrated framework that incorporates environmental data obtained from life cycle assessment (LCA) and a mixed-integer linear program (MILP) into the mine planning process. This framework, referred to as LCA-MILP, quantifies the environmental impact of mining activities. The article examines various GHG constraints and economic scenarios. To assess and verify the effectiveness of our proposed LCA-MILP framework, we conducted a case study on an open-pit iron ore mine situated in the Middle East. This article underscores the vital role of harmonizing environmental impact with long-term open-pit mine planning, ultimately fostering a more sustainable and socially responsible mining industry. For example, it was feasible to reduce the GHG emissions for 11.05% while achieving 93.66% of the original NPV. In another instance, the framework was capable of achieving the same NPV as the baseline with a 2% price adjustment (a common policy), while attaining a 5.39% reduction in its environmental impact. These measurable outcomes offer mining managers a comprehension of the compromises and prospects associated with incorporating factors into their long-term mine planning procedure promoting a mining industry that is both sustainable and economically viable.

Short-term planning of open pit mines with Semi-Mobile IPCC: a shovel allocation model

ABSTRACT In-pit crushing and conveying (IPCC) is considered a suitable alternative to truck haulage in open pit mines because it offers a lower operating cost than a truck-shovel system. It also reduces truck haulage distance and truck requirements. One of the IPCC variations is the semi-mobile system, which is relocated every two to five years. The short-term plan needs to be updated accordingly, based on the crusher’s optimal location and relocation time. To the best of our knowledge, short-term planning with IPCC is an area of research that has not been explored extensively yet and hardly any model can generate short-term schedules considering an IPCC in place. This research work proposes a mixed integer programming model to generate short-term production plans and near-optimal shovel allocation to mining faces, within a time horizon of 12 months. The objective of the model is to minimise the cost of material handling and maximise revenue, with respect to plant requirement, maximum allowable tonnage variation and IPCC location constraints, and the production and NPV targets set by the strategic plan. An iron ore mine case including a semi-mobile IPCC (SMIPCC) system with one relocation is used as the case study to verify the proposed model. The comparison of results between scenarios with and without IPCC justifies the use of IPCC in the iron ore mine from a short to medium-term perspective. The project can be considered a pioneering work in the arena of short-term mine planning with the IPCC.

A two-stage simultaneous optimization of NPV and throughput in production planning of open pit mines

Open-pit mines are complex businesses with long-term profits ranging from millions to billions of dollars. These mines have a minimum of one discrete (mining) and one continuous (processing) subsystem working in unison to deliver input raw material to several downstream industries. The inherent difference between these two subsystems causes operational challenges in the production process leading to nonoptimal net present value (NPV) and variable quality and quantity of throughput from the discrete to the continuous subsystem. This paper presents a two-stage clustering-MILP algorithm for long-term production planning in open-pit mines. The research integrates multi-range stockpiles in the decision-making process that leads to determining the optimum number of stockpiles required to maximize the discounted value of the mine, as well as balancing the quality and quantity of throughput. We evaluated our developed model in a real open pit mine case study. Results show that with a four-bin stockpile, we can maximize the discounted value of the mine by minimizing head-grade deviation to 5.1% and maximizing the reclaimed material by up to 10.7% of the total ore delivered to the plant.

A Two-Stage Simultaneous Optimization of NPV and Throughput in Production Planning of Open Pit Mines

A Two-Stage Simultaneous Optimization of NPV and Throughput in Production Planning of Open Pit Mines

Integrating Flexibility in Open Pit Mine Planning to Survive Commodity Price Decline

Integrating Flexibility in Open Pit Mine Planning to Survive Commodity Price Decline

Overview on an open pit mine planning of the pickstone peerless in a volatile environment

The prevailing mining climate is highly characterized by unstable consumables pricing systems, a volatile economy and skyrocketing operational costs, and exacerbated by a steady decline and intermittence in the availability of electricity. Zimbabwean mines need to extensively capitalize on the opportunity to improve productivity by emphasizing variables they can control, predominantly operational efficiency to avoid resizing of operations or facilitating downsizing by relatively insignificant factors. In this study, cycle times, rig penetration, utilization, availability and payloads were used to evaluate the mining cycles, operator costs and the information was compared against the life of mine plans with block models. Shifts were restructured to be concordant to the schedule provided by the utility company to save fuel that was being used to power metallurgical plant. Several challenges have been identified as the principal reasons behind discrepancies between the theoretical capabilities of equipment were proven to be achievable by a trial schedule which reduced the 7 days/month to less than 2 days a month. The new schedule reflects a theoretical improvement of close to 25% and significantly lower operational costs. The current mining fleet is capable of meeting the stipulated targets and even achieving more even within tough working environments characterized by harsh load shedding schedules and volatile inflation rates; however, this requires stringent monitoring and evaluation of unit process. By adopting the recommended short term production plans will avoid resizing of operations as it automatically reduces the operational costs by US $3 million annually whilst coercing both operators and management to improve their operational efficiency.

Integration of discrete fracture networks and flow simulator for quantification of hydrogeological uncertainty

In this study, a discrete fracture network model (DFN) and groundwater flow simulation were applied to a fractured aquifer of an open-pit mine. Conditional simulation of the fracture systems was developed to quantify and evaluate the uncertainty of geological structures and to predict possible hydrogeological risks associated with these uncertainties. The method used was based on the statistical characterization and simulation of spatial distribution scenarios of fracture lengths, directions and openings, as well as their influence on water flow behavior. The spatial configuration of the structures was generated using Poisson processes, while the lengths and angles were generated by Gaussian simulation. Flow simulation was performed with Modflow software. The resulting scenarios honored field data and quantified and evaluated the uncertainty associated with fracture distribution. In addition, the study was able to demonstrate the practical aspects of the proposed simulation method, which can then be applied to increase the planning and operational effectiveness of open-pit mines.

A Block Aggregation Method for Short-Term Planning of Open Pit Mining with Multiple Processing Destinations

Clustering approaches are widely used to group similar objects and facilitate problem analysis and decision-making in many fields. During short-term planning of open-pit mines, clustering aims to aggregate similar blocks based on their attributes (e.g., geochemical grades, rock types, geometallurgical parameters) while honoring various constraints: i.e., cluster shapes, size, alignment with mining direction, destination, and rock type homogeneity. This approach helps to reduce the computational cost of optimizing short-term mine plans. Previous studies have presented ways to perform clustering without honoring constraints specific to mining. This paper presents a novel block clustering heuristic capable of considering and honoring a set of mining block aggregation requirements and constraints. Constraints can relate to the clustering adjacent blocks, achieving higher destination homogeneities, controlled cluster size, consistency with mining direction, and achieving clusters with mineable shapes and rock types’ homogeneity. The proposed algorithm’s application on two different datasets demonstrates its efficiency and capability in generating reasonable block clusters while meeting different predefined aggregation requirements and constraints.

Analysis of the variables: Commodity price and discount rate on long-term open pit mine planning

Long-term open pit mine planning is a complex process which deals with numerous uncertainties, whether they are economical (commodity price, operational costs, production schedule, discount rate, inflation, among others); geological (grade distribution, density, hardness, etc); or physical constraints (property limits, environmental issues, legislation, etc). In this context, this paper aims to evaluate the effects of the variation of two important variables: commodity price and discount rate, with regard to the economic criterion, represented by the Net Present Value (NPV) of the mining business. Starting from a baseline value of US$ 80/t, the commodity (phosphate rock was used as a case study) price was varied within a 50% range, above and below the baseline value, obtained from historic values from the last 5 years. The discount rate values adopted in the analyses were 6%, 8%, 10%, 12%, 14%, 16%, 18% and 20%. The results showed increases in the market price yielded higher NPV and life of mine values. On the other hand, it was noted that increases in the discount rate can significantly alter the NPV, materially reducing the value of the mining undertaking. It is also worth noting that, in contrast to more robust approaches such as Real Options Theory (ROT), traditional Discounted Cash Flow (DCF) methods, such as NPV, assume variables, such as commodity price, to be fixed, which could either lead to the undervaluation or overvaluation of a project.

Some questions on planning, control and data analysis of open-pit mines

Some questions on planning, control and data analysis of open-pit mines

Application of MCDA in the determination of optimal block size for open-pit modelling and mine planning

The process of creating a geological block model as the basis for a further detailed design and planning of mining operations is a very responsible task. Errors made during this initial process are transferred to all other phases of the mining project. Certainly, one of the most important decisions for the modelling process is the choice of the appropriate size of the blocks that form the model itself. The determination of the optimal block size is not a simple process, because it depends on a large number of affecting factors and criteria. This process can be significantly facilitated by the application of multi-criteria analysis methods, which enable establishment of interdependence between the criteria in order to select the optimal solution. This paper presents the possibilities of applying the Analytical Hierarchical Process (AHP) method for selecting the optimal block size for the needs of the coal deposit modelling process and mine planning, as well as the way in which this method can significantly facilitate problem solving, by looking at it from several aspects. The analysis included six criteria and four potential solutions, and the results themselves indicated the advantages and disadvantages of the applied method.

A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty

Long term production planning (LTPP) plays a critical role to achieve success of a mining operation. LTPP, as an optimization problem, aims to maximize the net present value (NPV) of a mine subject to a set of constraints. One of the main reasons for not achieving production targets is the uncertainty of the LTPP's inputs. Geological uncertainty as the main sources of uncertainty is considered in this research. In this regard, a set of equiprobable scenarios of orebody and derived two new block model called “risk block model” and “EType” were used as inputs. Then, a stochastic integer programming (SIP) model was developed to integrate the geological uncertainty. Finally, a PSO-based algorithm was developed to solve the SIP model. Four different strategies were developed, according to the population topology and how to use the risk block model. Population topology defines the subset of particles that effect on each particle. Implementation the proposed approach on a large scale mine demonstrate its performance to develop a unique schedule considering geological uncertainties with maximum NPV and minimum risk of not achieving production targets. Investigations show that Gbest based PSO is more susceptible to trap in local optima. Multiple risk based strategies are able to generate better solutions, however, single risk based strategies are good practices when companies are looking for flexible or specific risk based designs.

Development of An Integrated Mathematical Model to Optimize Waste Rock dumping Satisfying Environmental Aspects

Waste rock dumping is very important in the production planning of open-pit mines. This subject is more crucial when there is a potential of acid-forming (PAF) by waste rocks. In such a type of mines, to protect the environment, the PAF materials should be encapsulated by non-harmful rocks. Therefore, block sequencing of the mined materials should be in such a way that both the environmental and economic considerations are considered. If non-acid forming (NAF) rocks are not mined in a proper time, then a stockpile is required for the NAF materials, which later on would be re-handled for encapsulation of PAF rocks. In the available models, the focus is on either block sequencing or waste dumping strategy. In this work, an attempt has been made to develop an integrated mathematical model for simultaneous optimization of block sequencing and waste rock dumping. The developed model not only maximizes the net present value (NPV) but also decreases the destructive environmental effects of inappropriate waste dumping. The proposed model, which is solved by a CPLEX engine, is applied to two different iron deposits. Also the performance of the proposed model is cross-checked by applying the available (traditional) models in a two-step manner. According to the results obtained, it can be considered that utilizing the developed model, because of extensive re-handling cost reduction, the NPV improvement is significant, especially when the overall stripping ratio is higher (deposit case A).

A practical, long-term production scheduling model in open pit mines using integer linear programming

SYNOPSIS Long-term production scheduling is a major step in open pit mine planning and design. It aims to maximize the net present value (NPV) of the cash flows from a mining project while satisfying all the operational constraints, such as grade blending, ore production, mining capacity, and pit slope during each scheduling period. Long-term plans not only determine the cash flow generated over the mine life, but are also the basis for medium- and short-term production scheduling. Mathematical programming methods, such as linear programming, mixed integer linear programming, dynamic programming, and graph theory, have shown to be well suited for optimization of mine production scheduling. However, the long-term plans generated by the mathematical formulations mostly create a scattered block extraction order on several benches that cannot be implemented in practice. The reason is the excessive movement of mining equipment between benches in a single scheduling period. In this paper, an alternative integer linear programming (ILP) formulation is presented for long-term production scheduling that reduced the number of active benches in any scheduling period. Numerical results of the proposed model on a small-scale open pit gold mine show a 34% reduction in the average number of working benches in a given scheduling period. Keywords: long-term production scheduling, mathematical programming, practical plans, equipment movements.

Updating Road Information in Open-Pit Mines Using Truck Trajectories

Accurate road information is crucial for the effective planning and management of open-pit mines. However, the parameters related to the roads in open-pit mines change constantly, and their shapes are often complicated. These factors indicate the difficulties in updating road information in open-pit mines. This study reports a method for updating the road information in open-pit mines using the trajectories of trucks. The method employs an algorithm for compressing trajectory data, an algorithm for extracting new roads, and an algorithm for old-road classification. Data from the Fushun open-pit mine is used to validate this method, and our experiments show that it can identify more than 90% of new roads and the majority of disappearing roads. In addition, we analyze several factors affecting the results obtained using this approach and provide a detailed description of the limitations of this method. Overall, our findings indicate that this is a promising approach to road-information retrieval.

Incorporation of Geometallurgical Attributes and Geological Uncertainty into Long-Term Open-Pit Mine Planning

Long-term open-pit mine planning is a critical stage of a mining project that seeks to establish the best strategy for extracting mineral resources, based on the assumption of several economic, geological and operational parameters. Conventionally, during this process it is common to use deterministic resource models to estimate in situ ore grades and to assume average values for geometallurgical variables. These assumptions cause risks that may negatively impact on the planned production and finally on the project value. This paper addresses the long-term planning of an open-pit mine considering (i) the incorporation of geometallurgical models given by equiprobable scenarios that allow for the assessing of the spatial variability and the uncertainty of the mineral deposit, and (ii) the use of stochastic integer programming model for risk analysis in direct block scheduling, considering the scenarios simultaneously. The methodology comprises two stages: pit optimization to generate initial ultimate pit limit per scenario and then to define a single ultimate pit based on reliability, and stochastic life-of-mine production scheduling to define block extraction sequences within the reliability ultimate pit to maximize the expected discounted value and minimize the total cost of production objective deviations. To evaluate the effect of the geometallurgical information, both stages consider different optimization strategies that depend on the economic model to be used and the type of processing constraints established in the scheduling. The results show that geometallurgical data with their associated uncertainties can change the decisions regarding pit limits and production schedule and, consequently, to impact the financial outcomes.

A new risk-based optimisation method for the iron ore production scheduling using stochastic integer programming

Stochastic integer programming (SIP) has recently been studied to manage the risk caused by geological uncertainty when solving mine planning and production scheduling problems of open pit mines. However, similar to other mathematical programming techniques that deploy integer variables, the main obstacle of applying SIP on real-life datasets stems from the enormous number of integer variables required by its mathematical formulation, which is a function of number of mining blocks being processed and lifespan of the mining project. In this paper, a new framework is proposed for stochastic mine planning process which makes the application of SIP on large-scale datasets tractable. Firstly, mining blocks of simulated orebody models are clustered using TopCone algorithm to significantly reduce the scale of the data. A new SIP model is then developed to work on aggregated blocks so not only the net present value (NPV) is maximised and the risk of not meeting production targets is minimised, but also solution can be obtained in a practical timeframe. The scheduling result of the new SIP model is also compared to an integer programming (IP) model to highlight the ability to manage risk and generating higher NPV on a case study of a large-scale multi-element iron ore deposit in Pilbara region, Western Australia.

A practical approach to open-pit mine planning under price uncertainty using information gap decision theory

In the context of open-pit mine planning, uncertainties including commodity price would significantly affect the technical and financial aspects of mining projects. A mine planning that takes place regardless of the uncertainty in price just develops an optimized plan at the starting time of the mining operation. Given the price change over the life of mine, which is quite certain, optimality of the proposed plan will be eliminated. This paper presents a risk-averse decision-making tool to help mine planners in mining activities under price uncertainty. The objective is to propose mine planning in a way that a target Net Present Value (NPV) is guaranteed. In order to reach this goal, Information Gap Decision Theory (IGDT) is developed to hedge the mining project against the risk imposed by the information gap between the forecasted and actual price. The proposed approach is of low sensitivity to the price change over the life of mine, and can use the estimated prices with uncertainty. A case study at an existing iron mine demonstrates the performance of the proposed approach. The results obtained showed that the proposed method could provide a robust solution to mine planning under price uncertainty. Moreover, it was concluded that the method could present more reliable mine plans under condition of price uncertainty.

Mine-to-mill multi-objective optimal blending with technical and economic constraints using a modified genetic algorithm

Optimisation techniques have been used effectively in the open pit mine planning. However, they are incorporated more slowly in the optimal solution of mine-to-mill blending. Specifically, no contributions are observed from the multi-objective approach. This work shows the application of a multi-objective genetic algorithm with additional non-natural genetic operations to the solution of this problem. The implementation of the algorithm in a particular case, but easily generalisable, is presented. The results show the efficiency of the procedure, with significant improvements in the Net Present Value.