Download or read book Dynamic Mid term Optimization of a Mining Complex Under Uncertainty written by Maria Fernanda Del Castillo and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Simultaneous Optimization of Mineral Value Chains Under Resource Uncertainty written by Maria Del Castillo Suarez and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mining complexes are mineral value chains where extracted material from different mines is transformed into sellable products through a set of processing streams. This value chain is governed by uncertainties at different levels, from the geological attributes of the orebody at the mine(s), to the different operational and processing components that lead the sellable products to the market. Stochastic simultaneous optimization formulations for industrial mining complexes have proven to be effective in generating reliable strategic plans that maximize net present value and, at the same time, manage and reduce risk. However, because of the uncertainties governing a mining complex, particularly the ones related to the geological attributes which define the supply of the system, it has become a priority to integrate flexibility mechanisms that allow a mining project to change and adapt as more information becomes available. Within this adaptability, optimizing the investment timing of high-magnitude capital expenditures throughout the life-of-mine is a priority, due to their high impact on the annual cash-flows and on their effects over the physical mining schedule. Additionally, to improve a mining complex's ability to meet production targets and overall performance, advanced mechanisms should be developed to ensure complex blending constraints are met, managing the geometallurgical variables of the deposit.This thesis presents a methodology to embed flexibility into mineral value chains, by allowing the strategic mine plan of a mining complex to dynamically consider possible options and alternatives for reacting and adapting to future changes. For this, first, a study on extraction capacity optimization is presented, followed by the development of a mechanism to deal with complex variables of the deposit to meet blending constraints and production targets. These two components are later integrated into a dynamic optimization model, which optimizes the mining complex's mine plan under geological uncertainty, integrating flexible investment alternatives, as well as operational modes.The dynamic model developed produces a unique initial extraction sequence, while keeping a viable flexible long-term plan for future investment decisions, as may be needed. The flexible long-term plan is obtained through a dynamic optimization which allows making transitioning plans upfront to facilitate change. This method introduces a new adapted multistage stochastic programming model which expands upon the two-stage framework by performing multiple recourse stages that are solved iteratively, allowing parallel designs to be generated in a scenario-tree structure. In this model, dynamic decisions over capital expenditures are made sequentially over time, based on information that becomes available over production time. The above model is subsequently extended to include alternatives over operating modes at different levels of the mineral value chain. More specifically, optimal operating modes are chosen per period, selecting blasting patterns at the mine, and processing relations of throughput and recovery at the plant. The practical implications of the proposed method are demonstrated through an application over a copper-gold mining complex, where the dynamic model presents a 10.5% increase in net present value compared to a traditional two-stage stochastic formulation.The dynamic mining complex formulation proposed is able to include flexibility into the optimization of the strategic plan of a mineral value chain. This enables possible developments within the feasible set of alternatives that can be taken, considering the mining complex's configuration, capacities, and constraints. The proposed model is able to generate feasible, operational schedules, while providing a wider view of the mining complex's performance, easing the transition to possible changes due to the periodic unveiling of uncertainty." --

Download or read book On Globally Optimizing a Mining Complex Under Supply Uncertainty written by Luis Montiel Petro and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mining complexes are generally comprised of multiple deposits that contain several material types and grade elements, which are transformed in available processing destinations and transported to final stocks or ports as saleable products. These components, associated with a mining complex, encompass multiple sequential activities: (i) Mining the material from one or multiple sources; (ii) blending the material including stockpiling; (iii) transforming the material in different processing destinations considering operating modes; (iv) transporting the transformed material to final stocks or ports. Since these activities are strongly interrelated, their optimization must take place simultaneously. Conventional mining optimization methods suffer from at least one of the following drawbacks when optimizing mining complexes: some decisions are assumed when they should be dynamic (operating modes, destination of mining blocks, etc.); component based objectives are imposed, which might not coincide with global objectives; many parameters are assumed to be known when they are uncertain. Past research works have demonstrated that geological uncertainty is the main cause of the inability of meeting production targets in mining projects. This thesis presents methods to optimize mining complexes that simultaneously consider different components and account for geological uncertainty. A multistage methodology that uses simulated annealing algorithm to generate risk-based production schedules in mining complexes with multiple processing destinations is presented and implemented in Escondida Norte (Chile) copper dataset. Its implementation using Escondida Norte dataset generates expected average deviations of less than 5% regarding mill and waste targets, whereas a mine production schedule generated conventionally over a single estimated model generates expected average deviations of 20 and 12% for mill and waste targets respectively. An iterative improvement algorithm that considers operating modes at different processing destinations is developed and applied to a copper complex. The objective function seeks for maximizing discounted profits along the different periods and scenarios (orebody simulations). The implementation of the method at a copper deposit allows reducing the expected average deviations from 9 to 0.2% regarding the capacity of the first process while increasing the expected NPV by 30% when compared with an initial solution generated conventionally. A method that uses simulated annealing at different decision levels (mining, processing and transportation) is described and tested in a multipit copper operation. The implementation of the method in a multipit copper operation permits the reduction of the expected average deviations from the capacities at two mills from 18-22% to 1-3% and the expected average deviation from the targets regarding two blending elements from 7-1.8% to 0.3-0.6% when compared to an initial solution generated conventionally. The expected NPV also improves by 5%. The previous method is extended to mining complexes that combine open pit and underground operations and it is tested in a gold complex in Nevada. The implementation of the method at Twin Creeks gold complex in Nevada shows improvements in meeting the metallurgical blending requirements while increasing the expected NPV by 14%. The formulations described in this thesis encompass a large number of integer variables given the discretization of the mineral deposits. To solve the problems, efficient optimization algorithms are implemented with significant improvements when compared with conventional deterministic approaches. These algorithms outperform conventional methods regarding expected NPV and meeting targets at the different components of the value chain." --

Download or read book GLOBAL OPTIMIZATION OF THE OPEN PIT MINING COMPLEX WITH INTEGRATED CUT OFF GRADE OPTIMIZATION UNDER UNCERTAINTY written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : The mining complex refers to an integrated problem where the material is extracted from the mines; the extracted material is passed through a series of processing facilities connected with various material handling methods to generate a set of finished products, which can be sold to the customers. The optimization of the mining complex refers to the simultaneous optimization of the multiple mine production schedules, the destination of the materials, and the method of processing throughout the life of the project. The purpose of optimizing the mining complex is to deal with the effective management of resources and maximize cash flows to generate higher profits over the life of the project. The goal of this dissertation is to develop a global optimization methodology that integrates geological (supply) uncertainty and can manage the risk in the design, mining complex operations, and maximize the cash flows. In this study, a new production schedule approach is presented that integrates geological uncertainty and generate the extraction sequence for the mining complex problem. The extraction sequence is developed to maximize the net present value and provide a consistent quantity of the material to different destinations. To optimize the quantity of materials sent to different destinations, the destination policies are defined based on the cut-off grade optimization and block economic values. This allows to form the destination policies for the mined material into various processing streams and maximize the value of the operation. The production schedule and the destination policies are optimized within a unified solution approach for the mining complex problem. The work presented advances the field through the development of the new model that uses the combination of maximum flow, genetic algorithm, and Lane's method for the global optimization of the mining complex. The method simultaneously optimizes the production schedule and the cut-off grade while considering uncertainty. The performance advantages and limitations are analyzed and tested on real-world examples. The results show that the models reduce the production risks and increase the net present value of the mining operation.

Download or read book The Optimization of Mine Planning Aspects Under Uncertainty written by Rein Dirkx and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mining operations typically face critical operational decisions based on uncertain information. This uncertainty is inherent to the mining process and cannot be overcome, as materials have to be extracted from underground, which can never be fully explored. This thesis addresses two pertinent aspects of many mining operations while directly incorporating this inherent uncertainty into the optimization. The uncertainty is used to our advantage by minimizing the resulting downside risk while maximizing the upside potential. The first aspect, addressed in Chapter 2, is the optimization of infill drilling schemes, a decision faced by every single mining operation on various scales. The second aspect, addressed in Chapter 3, focuses on the stochastic optimization of a production schedule of a block caving operation, a popular mass mining alternative to open pit mines. The optimization of infill drilling is based on a multi-armed bandit (MAB) framework. The novelty of this approach is the application of an advanced machine learning method like MAB to find an elegant solution to the infill drilling problem. The method is applied to a case study of a gold mining complex. The main issue in this mining complex is the uncertainty in their long-term, multi-element stockpiles on which the infill drilling optimization is performed. Stockpiles are often very variable, but their uncertainty can be quantified using conventional stochastic orebody modelling. Although the infill drilling optimization is applied to a stockpile, the proposed method is more general and can be applied to any deposit. The approach proposed in Chapter 3 is the first stochastic optimization approach for scheduling a block cave mine that explicitly includes grade and hang-up uncertainty into the optimization. Hang-up uncertainty is defined as the uncertainty related to ore that clogs the draw points due to aberrant fracturing. The grade uncertainty is incorporated via stochastic orebody simulations, similar to what has been employed for open pit operations in the past, while hang-up uncertainty, the main contribution of this work, is incorporated by tracking and minimizing the delays caused by hang-ups over multiple scenarios. The results of the case study show a reduction of the annual overtime incurred due to delays of 95 % when the hang-up uncertainty is included in the optimization compared to when hang-ups are ignored." --

Download or read book Mineral Supply Chain Optimization Under Uncertainty Using Approximate Dynamic Programming written by Cosmin Paduraru and published by . This book was released on 2015 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book An Application of Simultaneous Stochastic Optimization in Mining Complexes and Integrating Mine to port Transportation written by Mélanie LaRoche-Boisvert and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "A mineral value chain or mining complex is an integrated system representing all components of a mining operation for the extraction, transportation and transformation of material, from sources (open pit and underground mines) to customers or the spot market. Simultaneous stochastic optimization aims to optimize all components of a mineral value chain, including extraction schedules for the mines, stockpile management, processing and transportation scheduling, jointly to capitalize on the synergies that exist within the system. Additionally, the simultaneous stochastic optimization approach incorporates material supply or geological uncertainty using equally probable geostatistical (stochastic) simulations of the attributes of interest of the deposits. The incorporation of material supply uncertainty allows the approach to manage the related major technical risks.The first contribution of this thesis is the application of simultaneous stochastic optimization at a three-mine open pit gold mining complex, incorporating material supply uncertainty using stochastic simulations of the gold grades of each deposit. The case study maximizes the net present value of the operation by generating life-of-mine schedules for each deposit considered and stockpile management plans, which maximize gold production and minimize the associated costs. The study also assesses the impacts of material hardness on the processing facilities, notably the SAG mill, and the recovered gold. This assessment indicates that the SAG mill is the bottleneck of the operation; due to the lack of availability of soft material in the considered deposits, the throughput of material at the SAG mill is significantly lowered. The second contribution of this thesis is a new stochastic mathematical programming formulation jointly optimizing long-term extraction scheduling and mine-to-port transportation scheduling for mining complexes under supply uncertainty. Mine-to-port transportation systems represent an important component of certain mining complexes, such as iron ore mining complexes, ensuring that extracted products reach their intended clients. This component of the mineral value chain has not been included in previous simultaneous stochastic optimization formulations, ignoring the interactions between the transportation system and the other components of the mining complex. The proposed model simultaneously optimizes extraction scheduling, stockpile management, mine-to-port transportation scheduling and blending under material supply uncertainty. It aims to minimize the costs associated with meeting quantity and quality demand for the products at the port, managing the risks associated with the material supply uncertainty using stochastic simulations of grades. The model is applied to an iron ore mining complex consisting of two open pit mines, each with a waste dump, a stockpile and a loading area, connected to a single port by a railway system. Material is transported by two trains. At the port, demand for two products are considered, each with quality constraints relating to five elements. Stochastic simulations of the five elements considered are used to represent the material supply uncertainty. By optimizing the extraction and the mine-to-port transportation jointly, the case study is able to determine that only the first train is necessary to transport material to meet demand at the port for the first three years of mine life; for the remainder, the second train is also needed. As such, the second train could be allocated to another operation for better use during the first three years of operation or its purchase could be delayed. The model provides decision makers with a realistic use of the mine-to-port transportation system"--

Download or read book Simultaneous Short term Decision making in Mining Complexes Integrating Geometallurgy Assisted by Production Data written by Christian Both and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "A mining complex is an integrated business of mines and downstream facilities that extracts raw materials, converts extracted materials into sellable products, and transports products to markets and customers. Conventionally, individual components of a mining complex are optimized independently of each other, which causes underperformance of the mineral value chain. Simultaneous stochastic optimization of mining complexes has shown to create strategic mine plans that increase the net present value while reducing risk of meeting production targets by incorporating geological and price uncertainty. While these developments jointly optimize strategic decisions of a mining complex, short-term planning makes weekly to monthly decisions to best meet long-term production targets and maximize value. These decisions include short-term extraction sequence, destination of materials, and downstream material flow in mining complexes. Furthermore, the optimal allocation of the mining fleet is an important aspect of short-term planning; however, the joint stochastic optimization of short-term production schedules and fleet management in mining complexes has not yet been developed. Additionally, geometallurgical properties that drive revenues, costs, and the ability to meet production targets, are not integrated in the optimization of short-term production schedules in mining complexes. This thesis expands the simultaneous stochastic optimization of mining complexes for long-term planning into a decision-making framework for short-term mine planning through the incorporation of fleet management and geometallurgical prediction models of plant performances into the short-term optimization of mining complexes, which is assisted by the utilization of collected datasets from production processes in mines and processing plants. First, a new stochastic integer programming model for short-term planning is developed that extends the simultaneous stochastic optimization of mining complexes to allow the scheduling of a heterogeneous truck fleet and shovel allocations while considering the costs and loss of production caused by shovel relocation. Next to geological uncertainty, equipment performance uncertainties related to production rates, availabilities, and truck cycle times are integrated. Next, a geometallurgical model for the prediction of ball mill throughput in mining complexes is developed which utilizes drilling penetration rates and recorded throughput rates of the operating plant. The creation of hardness proportions avoids biases introduced by the change of support and blending of non-additive geometallurgical properties. By integrating the throughput prediction model into the simultaneous stochastic optimization formulation, planned production can be achieved reliably because scheduled materials match with the predicted mill performance. The throughput prediction model is extended thereafter by including recorded measurements of ball mill power draw and particle size distributions. Since the addition of new features increases the possibilities of non-linearities, a neural network is used. The prediction of metallurgical responses of the operating plant and their incorporation into short-term stochastic production scheduling is finally extended by creating prediction models of consumption rates of reagents and consumables in a gold mining complex. With the new developments presented in this thesis, the simultaneous stochastic optimization of mining complexes can now be applied for short-term planning, modelling the operational aspects of the mining fleet and metallurgical behaviour of processing plants in greater detail. The integration of these short-term aspects leads to short-term mine plans that are more likely to align with long-term production targets while benefitting from synergistic effects that maximize the profit of the mineral value chain"--

Download or read book Global Asset Optimization of Open Pit Mining Complexes Under Uncertainty written by Ryan Goodfellow and published by . This book was released on 2014 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Stochastic Optimization of Long and Short term Mine Production Schedules Incorporating Uncertainty in Geology and Equipment Performance written by Matthew Quigley and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "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." --

Download or read book Mining goes Digital written by Christoph Mueller and published by CRC Press. This book was released on 2019-05-22 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Download or read book Dynamic Optimization Second Edition written by Morton I. Kamien and published by Courier Corporation. This book was released on 2013-04-17 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its initial publication, this text has defined courses in dynamic optimization taught to economics and management science students. The two-part treatment covers the calculus of variations and optimal control. 1998 edition.

Download or read book Proceedings of the Tenth International Forum of Decision Sciences written by Xiang Li and published by Springer Nature. This book was released on 2023-06-11 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on selected aspects of the current and upcoming trends in transportation, logistics and decision making. In detail the included transportation management, optimization and management of logistics system, big data technology and method, financial engineering and risk management, investment decision and risk management, data-driven process management decision, scheduling optimization and combination decision, theory and method of forecasting and decision making, data mining and knowledge management, operation and green supply chain management, industrial engineering and operation management, information system and business intelligence, Internet + green manufacturing, strategic emerging industries and Industrial finance, big data and smart city. The variety of the papers delivers added value for both scholars and practitioners. This book is the documentation of International Conference on Intelligent Transportation and Logistics with Big Data & International Forum on Decision Sciences, which took place in Harbin, Heilongjiang province, China, in 2022.

Download or read book Advances in Applied Strategic Mine Planning written by Roussos Dimitrakopoulos and published by Springer. This book was released on 2018-01-17 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of papers on topics in the field of strategic mine planning, including orebody modeling, mine-planning optimization and the optimization of mining complexes. Elaborating on the state of the art in the field, it describes the latest technologies and related research as well as the applications of a range of related technologies in diverse industrial contexts.

Download or read book Indian Economics Service Book Previous Next Indian Economics Service Book Previous Next Indian Economics Service IES Practice Question Bank Book of 400 Questions With Written Answers By Expert Faculties of All 4 Papers written by DIWAKAR EDUCATION HUB and published by DIWAKAR EDUCATION HUB. This book was released on 2024-03-31 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Indian Economics Service [IES] Practice Question Bank Book of 400 Questions With Written Answers By Expert Faculties of All 4 Papers Highlight- 100 Question of Each Paper Cover all 4 Papers General Economics I,II,III & Indian Economics The Answer Written by Expert & Experienced Faculties Cover all 100,200 & 300 Words Questions of all 3 Section of Each Paper Help You to Get Idea How to Write Good Answer of Questions

Download or read book JPT Journal of Petroleum Technology written by and published by . This book was released on 2008-07 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: