Development of a New Open Pit Mine Phase Design Algorithm Using Mixed Integer Linear Programming PDF Download

Author: Chotipong Somrit
Publisher:
ISBN:
Category : Integer programming
Languages : en
Pages : 304

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Author: Amanda G. Smith
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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We study new optimization-driven approaches to two engineering problems, employing techniques from integer, bilevel, stochastic, and multi-objective programming. We first present an approach to the open-pit mining truck dispatching problem that utilizes mixed-integer programming (MIP). The truck dispatching problem seeks to determine how trucks should be routed through the mine as they become available. We describe an optimization-driven approach to solving the dispatching problem in the form of a MIP model. The model is difficult to solve directly, so we present a heuristic that quickly produces high-quality feasible solutions to the model. We give computational results demonstrating the effectiveness of the proposed heuristics and several key model components. To show that our model finds solutions that meet the open-pit mining objectives while accounting for key problem components in novel ways, we embed the MIP-based dispatching policy in a discrete-event simulation of an open-pit mine. We further create two additional heuristic dispatching policies that rely on a new nonlinear rate-setting model that treats queueing at each site as an M/G/1 queue. We present a full computational study of the three policies in which we perform output analysis on key metrics of the open-pit mine simulation. We show that the MIP-based dispatching policy consistently outperforms the heuristic dispatching policies on open-pit mines with a variety of characteristics. The second problem we study is selecting metabolic network changes in cellular organisms. In this problem, enzymes are used to alter the rates at which reactions occur in cellular organisms, causing the cell to increase the output of a desired biochemical product. In existing bilevel MIP models, the lower-level cellular objective is modeled as either maximizing cellular growth or minimizing the biochemical output. We combine these perspectives with two new bilevel MIP models: a single-objective maximum productivity model and a bi-objective maximum yield and maximum growth model. We finally present two-stage stochastic extensions of both models in which we maximize the expected values of productivity, yield, and growth when the planned changes to the metabolic network are uncertain. Because the stochastic bi-objective model contains a complicating budget constraint that lacks parallel structure, we describe a heuristic that alternates between a scenario decomposition-based algorithm and allocating the budget to individual scenarios. Ultimately, we show that this methodology can be implemented to find solutions that meet the metabolic engineering objectives but which are less sensitive to uncertainty than solutions to existing models. This dissertation includes the following three supplemental data files: - A2-SimData.xlsx: data used to generate the simulation of an open-pit mine described in Chapter 3, - A3-CoreModel.xlsx: data used to generate instances of bilevel metabolic engineering models of the core network reconstruction of E. coli as described in Chapter 4, and - A3-iJR904.xlsx: data used to generate instances of bilevel metabolic engineering models of the iJR904 network reconstruction of E. coli as described in Chapter 4.

Author: Mohammad Tabesh
Publisher:
ISBN:
Category : Aggregation (Chemistry)
Languages : en
Pages : 250

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The objective of this thesis is to develop, implement and verify a theoretical framework based upon aggregation and mathematical programming for solving the long-term open pit production planning problem. The goal is to closely estimate the maximum net present value of the operation by providing an optimum and practical mining, processing and stockpiling schedule for the open pit mining operation while respecting the technical and operational constraints. As stated by many researchers in the area and illustrated in the forth chapter of this thesis, using blocks as units of planning results in over-estimation of the operation's profitability and flexibility. Thus, we introduced a clustering algorithm along with a mathematical formulation that can result in good production plans that result in high NPV, are practical and do not under- or over-estimate the value of the operation. In this thesis, we introduced, implemented and verified a specifically-designed clustering technique based on agglomerative hierarchical clustering, in order to aggregate blocks into mining-units that are homogenous in rock type and grade, and have mineable shape and size. We designed the algorithm, developed the codes and implemented and tested the algorithm on small test datasets and large real-size deposits to evaluate the performance of the algorithm. We showed that we can balance the clustering control parameters to obtain clusters of blocks aligned with the clustering purpose such as long-term planning units, ore polygons and blast patterns. Moreover, we formulated, implemented and verified a mixed integer linear programming model, for long-term open pit production planning problem, which can use two different sets of units for making mining and processing decisions. Our model is able to simultaneously determine the optimum stockpiling strategy and the optimum mining and processing schedule in reasonable processing time. We implemented our model on a small test dataset as well as real-size deposits to understand the effects of using different units of planning for making mining and processing decisions. We showed that we can obtain practical and optimum production schedules for real-size open pit mines in a reasonable time by benefiting from the clustering technique we introduced. Moreover, we showed how incorporating stockpile optimization in long-term production scheduling can increase the net present value of the operation. We benchmarked our model against commercial scheduling software to illustrate the flexibility and accuracy of our planning approach. The main contributions of this thesis to the mining body of knowledge are (i) introducing a clustering algorithm that creates aggregates of blocks homogenous in rock type and grade, with controlled shape and size, and respects the mining direction as well as other constraints and boundaries, (ii) introducing an MILP formulation for the long-term open pit production planning problem, with dynamic cut-off grade, that maximizes the net present value of the mine, by using two different units for making mining and processing decisions, while respecting operational and technical constraints, and (iii) incorporating stockpiling in the long-term scheduling to simultaneously optimize the mining, processing and stockpiling strategies.

Author: Christoph Mueller
Publisher: CRC Press
ISBN: 1000439062
Category : Technology & Engineering
Languages : en
Pages : 782

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The conferences on ‘Applications for Computers and Operations Research in the Minerals Industry’ (APCOM) initially focused on the optimization of geostatistics and resource estimation. Several standard methods used in these fields were presented in the early days of APCOM. While geostatistics remains an important part, information technology has emerged, and nowadays APCOM not only focuses on geostatistics and resource estimation, but has broadened its horizon to Information and Communication Technology (ICT) in the mineral industry. Mining Goes Digital is a collection of 90 high quality, peer reviewed papers covering recent ICT-related developments in: - Geostatistics and Resource Estimation - Mine Planning - Scheduling and Dispatch - Mine Safety and Mine Operation - Internet of Things, Robotics - Emerging Technologies - Synergies from other industries - General aspects of Digital Transformation in Mining Mining Goes Digital will be of interest to professionals and academics involved or interested in the above-mentioned areas.

Author: Thys Brentwood Johnson
Publisher:
ISBN:
Category :
Languages : en
Pages : 248

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The multi-period open pit mine production scheduling problem is formulated as a large scale linear programming problem using the block concept. A solution procedure is developed through decomposition and partitioning of the subproblem into elementary profit routing problems for which an algorithm is presented. Many of the traditional mine planning concepts are discussed and suggestions for improvement through use of the techniques developed in this thesis are given. In the development of the solution procedure, those constraints which govern the mining system are considered as the master problem. The constraints which dictate the sequence of extraction are used as the subproblem. The properties of the single period subproblem and its dual are discussed, and the dual problem is shown to be equivalent to a bipartite maximum flow problem for which an algorithm is given. The Multi-period subproblem algorithm is developed by partitioning by stages and using the properties of the single period subproblem. This treatment allows optimization of the complete mining-concentrating-refining system over the entire planning horizon and permits the system to dictate how and when to process a block of material. (Author).

Author: Hesameddin Eivazy
Publisher:
ISBN:
Category : Mine management
Languages : en
Pages : 262

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Author: Salih Ramazan
Publisher:
ISBN:
Category : Mine management
Languages : en
Pages : 328

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Long-term production scheduling design is a very important part of mining because it determines the economic outcome of a project. It is a very complex and difficult problem basically due to its large scale. Much effort has been devoted to solving the optimum pit scheduling problem, but there has been no success in developing a scheduling method to give optimum results in maximizing net present value of a mining project. It is already known that the optimum result for the scheduling problem can be obtained using a mathematical programming method such as Mixed Integer Programming. However, it is not possible to formulate the scheduling problem as a mathematical programming model since the number of variables required for the mathematical model is too great to be solved by today's available computer technology. Therefore, a methodology is required to combine, or aggregate, the mining blocks and to decrease the number of variables in scheduling without losing the optimality. Therefore, fundamental tree concept is introduced in this thesis research to combine the blocks. A fundamental tree is defined as any combination of blocks such that: 1. the blocks can be profitably mined, 2. the blocks obey the slope constraints and, 3. there is no proper subset of the chosen blocks that meets 1 and 2. Linear Programming (LP) formulation is developed as a mathematical model to find a set of fundamental trees that exist for a deposit. Since the blocks are combined to form the fundamental trees, the number of variables required for the scheduling model is decreased significantly. This decrease in the number of variables makes it possible to mathematically formulate the scheduling problem.

Author: Behrang Koushavand
Publisher:
ISBN:
Category : Strip mining
Languages : en
Pages : 216

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Open-pit mining is widely used to extract natural resources. Low cut-off grades and large operations can make open-pit mining profitable. An important challenge is to determine the optimum production schedule. Usually the goal is to maximize the net present value of the project while delivering ore to the plant at full capacity. The best production plan would require complete knowledge of the orebody and all other engineering and economic parameters. An estimated block model is often used to determine the production schedule. Uncertainty is inevitable with widely spaced drill holes. The open-pit production schedule based on estimated models may be suboptimal and affected dramatically by grade uncertainty. The research documented herein develops, implements and verifies four mixed integer optimization frameworks for long-term production scheduling in the presence of grade uncertainty. The main contributions of this research are (1) consideration of cost of grade uncertainty to influence the production plan, (2) accounting for the linear and nonlinear effects of the grade uncertainty on the long-term mine planning, (3) development of a mixed integer linear programming model that maximizes NPV and minimizes the cost of the grade uncertainty by considering a stockpile, and finally (4) implementation of a quadratic optimization model accounts for grade uncertainty in the long-term production plan.

Author: Xinming Tang
Publisher: CRC Press
ISBN: 1466575123
Category : Science
Languages : en
Pages : 1321

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Developments in Geographic Information Technology have raised the expectations of users. A static map is no longer enough; there is now demand for a dynamic representation. Time is of great importance when operating on real world geographical phenomena, especially when these are dynamic. Researchers in the field of Temporal Geographical Information Systems (TGIS) have been developing methods of incorporating time into geographical information systems. Spatio-temporal analysis embodies spatial modelling, spatio-temporal modelling and spatial reasoning and data mining. Advances in Spatio-Temporal Analysis contributes to the field of spatio-temporal analysis, presenting innovative ideas and examples that reflect current progress and achievements.

Author: Mohammad Mahdi Badiozamani Tari Nazari
Publisher:
ISBN:
Category : Mining engineering
Languages : en
Pages : 142

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A strategic mine planning model determines the best order of extraction and destination of material over the mine-life, in a way that maximizes the net present value of the produced minerals. In case of oil sands open-pit mining, further processing of the extracted oil sands generates massive volumes of slurry containing water, sands, clay and fine material known as tailings. Since the tailings volume significantly influences the mine production and site reclamation, it is reasonable to consider tailings management within the frameworks of long-term mine planning. One of the current practices in Alberta oil sands industry is to process the tailings slurry and make composite tailings (CT), through adding coagulant aids to the mature fine tailings (MFT), to accelerate its dewatering and make it ready for reclamation. To save space and also to avoid higher reclamation costs, the processed tailings is deposited in in-pit tailings containments constructed by internal dykes using mine waste material. In this research, an integrated mine planning framework is proposed, implemented and verified using mixed-integer linear programming technique, to optimize the production schedule with respect to mine waste management in terms of dyke construction and in-pit tailings deposition. A tailings model is developed and integrated to the mine planning model that calculates the volume of tailings slurry and composite tailings based on the processed material. Two small case studies and one large-scale case are carried out to verify the performance of the proposed optimization model. Two variable reduction techniques are implemented to increase the efficiency of the run time. The model solves the large-scale problem to optimality over 30 periods within 0.5 to 1.5 hours of CPU time, depending on the model resolution. In the generated schedule, the produced tailings is being deposited in the excavated mining-pit as the mining operations proceed and the in-pit dykes are constructed using mine waste material.