H. Askari-Nasab

Two-stage clustering algorithm for block aggregation in open pit mines

Abstract One of the main obstacles in using exact optimisation methods for open pit production scheduling is the size of real mining problems, which forms an intractable optimisation problem. The objective of this paper is to develop, implement, verify and validate a clustering algorithm for block aggregation for the purpose of production scheduling. The algorithm aggregates blocks into selective mining units based on a similarity index which is defined based on rock types, ore grades and distances between blocks. A two-stage clustering approach based on agglomerative hierarchical algorithm and tabu search is developed and tested. The algorithm is validated by a case study on an iron ore life of mine production schedule. The results illustrate that the size and shape of the aggregated blocks have 10–15% effect on the project’s net present value and also a significant impact on the practicality of long term production schedules genereated.

Mixed integer linear programming formulations for open pit production scheduling

One of the main obstacles in using mixed integer linear programming (MILP) formulations for large-scale open pit production scheduling is the size of the problem. The objective of this work is to develop, implement, and verify deterministic MILP formulations for long-term large-scale open pit production scheduling problems. The objective of the model is to maximize the net present value, while meeting grade blending, mining and processing capacities, and the precedence of block extraction constraints. We present four MILP formulations; the first two models are modifications of available models; we also propose, test and validate two new MILP formulations. To reduce the number of binary integer variables in the formulation, we aggregate blocks into larger units referred to as mining-cuts. We compare the performances of the proposed models based on net present value generated, practical mining production constraints, size of the mathematical programming formulations, the number of integer variables required in formulation, and the computational time required for convergence. An iron ore mine case study is represented to illustrate the practicality of the models as well.

Large-scale open pit production scheduling using Mixed Integer Linear Programming

One of the main obstacles in using Mixed Integer Linear Programming (MILP) formulations for open pit production scheduling is the size of the problem. The main objective of this paper is to present and implement a practical MILP formulation for open pit production scheduling problem. To reduce the number of binary integer variables in the formulation, we aggregate blocks into larger units referred to as mining-cuts. We also present the numerical modelling approach to setup the optimisation problem. Finally, we verify and validate the MILP production scheduler by a comparative case study against Whittle strategic mine planning software.

Integration of reclamation and tailings management in oil sands surface mine planning

The processing of oil sands generates large volumes of slurry, known as tailings, that is impounded in tailings ponds. Oil sands operators are committed to develop reclamation plans to ensure that the mine site is restored to a natural or economically usable landscape. Since most of the material that is needed for capping of the tailings pond is produced in mining operation, it is reasonable to include material requirement for reclamation as part of mine planning. In this paper, an integrated long-term mine planning model is proposed that includes tailings capacity and reclamation material requirements. A mixed integer linear programming (MILP) model is developed to test the performance of the proposed model. The MILP model is coded in Matlab?. It is verified by carrying out a case study on an actual oil sands dataset, and has resulted in an integer solution within a 2% gap to the optimality. The resulted production schedule meets the capacity constraint of the tailings facility and guarantees the production of the required reclamation material. Display Omitted An integrated mine planning optimization framework for oil sands mining is developed.Material requirement for reclamation and associated costs are considered in modeling.Tailings production and capacity of tailings facilities are included in the model.Mixed-integer linear programming (MILP) is used as the optimization tool.Choices for mining direction and selective mining unit are tested numerically.

Modelling Open Pit Dynamics Using Discrete Simulation

The objective in any mining operation is to exploit ore at the lowest possible cost with the prospect of maximizing profits. The planning of an open pit mine is an economic exercise, constrained by certain geological, operating, technological and local field factors. Heuristic methods, economic parametric analysis, operations research and genetic algorithms have been used to formulate periodic open pit planning problems. Open pit design, optimization and subsequent materials scheduling problems are governed by stochastic dynamic process. Thus, current algorithms are limited in their abilities to address problems arising from these random and dynamic field processes. The primary objective of this study is to use a discrete stochastic simulation to capture the random field processes associated with open pit design and materials scheduling. An open pit production simulator (OPPS), implemented in MATLAB, based on a modified elliptical frustum is used to model the geometry of open pit layout expansion. The simulator mimics the periodic expansion of the open pit layouts. The interaction of the open pit expansion model with the geological and economic block model returns the respective amount of ore, waste, stockpile materials, and the net present value of the venture. A case study of an iron ore deposit with 114 000 blocks was carried out to verify and validate the model. The optimized pit limit was designed using the Lerchs – Grossman algorithm. The best-case annual schedule, generated by the shells node in Whittle Four-X, yielded a net present value (NPV) of

Automatic creation of mining polygons using hierarchical clustering techniques

414 million over a 21-year mine life at a discount rate of 10% per annum. The best scenario out of 5000 simulation iterations using OPPS resulted in an NPV of

Oil sands mine planning and waste management using mixed integer goal programming

422 million over the same time span. Further research, based on hybrid stochastic simulation in conjunction with reinforcement learning, can provide a powerful tool for addressing the random field and dynamic processes in long-term open pit planning.

Open pit optimisation using discounted economic block values

There are multiple stages in a mining operation in which a mining engineer must draw polygons to be used as operation guidelines. These polygons are drawn by hand and based on the engineer’s experience and knowledge of the deposit. However, automatic procedures for forming the shapes can increase the quality and decrease the efforts required. Long-term planning requires large polygons that can be used as mining cuts. On the other hand, short-term planning requires mineable shapes to be used as mining units. These shapes need to be homogenous in grades and rock types so that the quality and dilution of material sent to the plant can be estimated with good approximation. In addition, the direction of mining can affect the desired shapes of the polygons. To address these problems, a clustering algorithm with shape control is introduced, which can provide reasonable guidelines for all the aforementioned shapes by calibrating its parameters. The implementations of the algorithm on two small datasets with 874 and 2794 blocks are illustrated. Performance of the algorithm on a real gold deposit with different mining strategies is also presented and evaluated based on homogeneity of grade, rock types, determined destinations, and run times.

Mining fleet management systems: a review of models and algorithms

Strategic mine planning and waste management are an important aspect of surface mining operations. Recent environmental and regulatory requirements make waste management an integral part of mine planning in the oil sands industry. The research problem here is determining the order of extraction of ore, dyke material and waste to be removed from a predefined ultimate pit limit over the mine life that maximises the net present value of the operation. We have developed, implemented, and tested a proposed mixed integer goal programming theoretical framework for oil sands open pit production scheduling with multiple material types. The formulation uses binary integer variables to control mining precedence and continuous variables to control mining of ore and dyke material. There are also goal deviational variables and penalty costs and priorities that must be set up by the planner. The optimisation model was implemented in TOMLAB/CPLEX environment. The developed model proved to be able to generate a uniform schedule for ore and dyke material. This is in line with recent regulatory requirements by Alberta Energy Resources and Conservation Board (Directive 074) which requires oil sands mining companies to develop life of mine plans which ties in to their in-pit tailings disposal strategy. It also provides a practical mining sequence that is consistent with mining oil sands deposit. Similarly, tradeoffs between achieving a goal and maximising NPV can be made.

Asphalt Mix Design Optimization for Efficient Plant Management

Abstract Strategic mine planning and the management of the future cash flows are a vital core of surface mining operations. The time dimension, which is an integral part of the scheduling problem, is not embedded in traditional ultimate pit outline optimisation algorithms. This study explores the validity of the theorem that a pit outline determined by an optimal long term schedule algorithm is constrained by the conventional Lerchs and Grossmanns (LG) optimised pit outline. This hypothesis was investigated through a case study using the intelligent open pit simulator (IOPS) founded on agent based learning theories. The optimal pushback schedule was determined using IOPS before determination of the optimised final pit outline. The economic block values were discounted with respect to the allocated extraction time, followed by final pit limits optimisation using LG algorithm.