Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Production Scheduling and Waste Disposal Planning for Oil Sands Mining Using Goal Programming

In oil sands mining, timely provisions of ore and tailings containment with less environmental footprints are the main drivers of profitability and sustainability. The recent Alberta Energy Resources Conservation Board Directive 074 requires oil sands waste disposal planning to be an integral part of mine planning. This requires the development of a well integrated strategy of directional mining and tailings dyke construction for in-pit and ex-pit tailings storage management. The objectives of this paper are to: 1) determine the order and time of extraction of ore, dyke material and waste that maximizes the net present value; 2) determine the destination of dyke material that minimizes construction cost; and 3) minimize deviations from the production goals of the mining operation. We have developed, implemented, and verified a theoretical optimization framework based on mixed integer linear goal programming (MILGP) to address these objectives. This study presents an integration of mixed integer linear programming and goal programming in solving large scale mine planning optimization problems using clustering and pushback techniques. Application of the MILGP model was presented with an oil sands mining case. The MILGP model generated a smooth and uniform mining schedule that generates value and provides a robust framework for effective waste disposal planning. The results show that mining progresses with an ore to waste ratio of 1:1.5 throughout the mine life, generating an overall net present value of $14,237M. This approach improves the sustainable development of oil sands through better waste management

Controlling short-term deviations from production targets by blending geological confidence classes of reporting standards

Meeting short-term production targets is desired by many companies, since this would enable them to finetune the processing operation,meet budget plans and obey contract requirements. Recently stochastic optimization solutions have been developed requiring geostatistical simulations as input. The significant value added has been demonstrated, however, an operational implementation of such approaches for day-to-day use is complex and seems currently difficult as it requires expert knowledge and extensive computational capacity. To control the short-term deviations, a new fast metaheuristic scheduler is developed that blends Geological Confidence Classes (GCC’s) from resource reporting standards. For the scheduler, a new penalty function is developed to schedule for a target blend of GCC’s and a new method is developed to enforce smooth mining patterns in three dimensions. The metaheuristic solver uses a Genetic Algorithm and an Ant Colony Optimization algorithm to efficiently converge towards the Pareto optimum. To establish an optimal blend of GCC’s, a methodology is developed which creates a range of equally probable scenarios of deviations from production targets for different blends of GCC’s. A least-squares estimate can be fitted to these scenarios at the required level of confidence to determine the optimal blend for a maximum allowed deviation. An historical world class gold deposit is used to show that the monthly and quarterly deviations can be controlled by blending GCC’s. Furthermore, the case study shows the possibility to establish an optimal blend of GCC’s by using the developed methodology. The scheduler proofs to be able to efficiently create and evaluate schedules to blend the GCC’s for this case study. For a maximum quarterly deviation of 15% at a 90% confidence level, the established optimal blend is 59% ore tonnage classified as measured resources. For the monthly deviations, a maximum of 15% is too low and cannot be met at a 90% confidence level

Presidential address: Optimization in underground mine planning-developments and opportunities.

Presidential address presented at the The Southern African Institute of Mining and Metallurgy Annual General Meeting on 11 August 2016.The application of mining-specific and generic optimization techniques in the mining industry is deeply rooted in the discipline of operations research (OR). OR has its origins in the British Royal Air Force and Army around the early 1930s. Its development continued during and after World War II. The application of OR techniques to optimization in the mining industry started to emerge in the early 1960s. Since then, optimization techniques have been applied to solve widely different mine planning problems. Mine planning plays an important role in the mine value chain as operations are measured against planned targets in order to evaluate operational performance. An optimized mine plan is expected to be sufficiently robust to ensure that actual outcomes are close or equal to planned targets, provided that variances due to poor performance are minimal. Despite the proliferation of optimization techniques in mine planning, optimization in underground mine planning is less extensively developed and applied than in open pit mine planning. This is due to the fact that optimization in underground mine planning is far more complex than open pit optimization. Optimization in underground mine planning has been executed in four broad areas, namely: development layouts, stope envelopes, production scheduling, and equipment selection and utilization. This paper highlights commonly applied optimization techniques, explores developments and opportunities, and makes a case for integrated three-dimensional (3D) stochastic optimization, in underground mine planning.MvdH201

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

Portfolio Analysis in Supply Chain Management of a Chemicals Complex in Thailand

There is a considerable amount of research literature available for the optimisation of supply chain management of the chemical process industry. The context of supply chain considered in this thesis is the supply chain inside the chemical complex which is the conversion of raw materials into intermediate chemicals and finished chemical products through different chemical processes. Much of the research in the area of planning and scheduling for the process sector has been focused on optimising an individual chemical process within a larger network of a chemicals complex. The objective of this thesis is to develop a multi-objective, multi-period stochastic capacity planning model as a quantitative tool in determining an optimum investment strategy while considering sustainability for an integrated multi-process chemicals complex under future demand uncertainty using the development of inorganic chemicals complex at Bamnet Narong, Thailand as the main case study. Within this thesis, a number of discrete models were developed in phases towards the completion of the final multi-objective optimisation model. The models were formulated as mixed-integer linear programming (MILP) models. The first phase was the development of a multi-period capacity planning optimisation model with a deterministic demand. The model was able to provide an optimal capacity planning strategy for the chemicals complex at Bamnet Narong, Thailand. The numerical results show that based on the model assumptions, all the proposed chemical process plants to be developed in the chemicals complex are financially viable when the planning horizon is more than 8 years. The second phase was to build a multi-period stochastic capacity planning optimisation model under demand uncertainty. A three-stage stochastic programming approach was incorporated into the deterministic model developed in the first phase to capture the uncertainty in demand of different chemical products throughout the planning horizon. The expected net present value (eNPV) was used as the performance measure. The results show that the model is highly demand driven. The third phase was to provide an alternative demand forecasting method for capacity planning problem under demand uncertainty. In the real-world, the annual increases in demand will not be constant. A statistical analysis method named “Bootstrapping” was used as a demand generator for the optimisation model. The method uses historical data to create values for the future demands. Numerical results show that the bootstrap demand forecasting method provides a more optimistic solution. The fourth phase was to incorporate financial risk analysis as constraints to the previously developed multi-period three-stage stochastic capacity planning optimisation model. The risks associated with the different demand forecasting methods were analysed. The financial risk measures considered in this phase were the expected downside risk (EDR) and the mean absolute deviation (MAD). Furthermore, as the eNPV has been used as the usual financial performance measure, a decisionmaking method, named “Minimax Regret” was applied as part of the objective function to provide an alternative performance measure to the developed models. Minimax Regret is one kind of decision-making theory, which involves minimisation of the difference between the perfect information case and the robust case. The results show that the capacity planning strategies for both cases are identical Finally, the last phase was the development of a multi-objective, multi-period three stage stochastic capacity planning model aiming towards sustainability. Multiobjective optimisation allows the investment criteria to be traded off against an environmental impact measure. The model values the environmental factor as one of the objectives for the optimisation instead of this only being a regulatory constraint. The expected carbon dioxide emissions was used as the environmental impact indicator. Both direct and indirect emissions of each chemical process in the chemicals complex were considered. From the results, the decision-makers will be able to decide the most appropriate strategy for the capacity planning of the chemicals complex

A Mixed-Integer Programming Model for an In-Pit Crusher Conveyor Location Problem

RÉSUMÉ Les coûts de transport représentent environ la moitié du coût total de fonctionnement (d’exploitation) dans les grandes mines à ciel ouvert. Une manière de réduire les coûts de transport est de raccourcir les distances de transport en rapprochant le point de déchargement du camion ou même de le placer dans la mine. Il y a une tendance à utiliser des systèmes de convoyeurs à grande vitesse et à grande capacité, lesquels ont été très productifs. Les systèmes de transport camion-pelle qu’utilisent des convoyeurs comparés aux conventionnels offrent une rentabilité opérationnelle supérieure et une grande fiabilité du concassage dans la fosse, ce qui les rend plus attrayants pour les activités minières modernes. Les principaux éléments à considérer dans la planification minière pour implémenter un système de concassage dans la fosse sont la disposition du convoyeur et la position du concasseur.---------- ABSTRACT Haulage costs account for around a half of the total operating costs in large open-pit mines. One way to reduce the haulage costs is to shorten the haulage distances by bringing the truck dump point closer or even into the mine. There is a tendency in the direction of the high speed, large capacity conveyor systems, and these arrangements have been very productive. Conveying and truck-shovel systems compared to conventional truck-shovel systems alone, provide operating cost efficiency and high reliability of in-pit crushing, making those types of systems more appealing to be implemented in modern mining activities. The main elements to be considered in mine planning to implement an in-pit crusher system are conveyor layout and crusher position

Data science for engineering design: State of the art and future directions

Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities

Gasification for Practical Applications

Although there were many books and papers that deal with gasification, there has been only a few practical book explaining the technology in actual application and the market situation in reality. Gasification is a key technology in converting coal, biomass, and wastes to useful high-value products. Until renewable energy can provide affordable energy hopefully by the year 2030, gasification can bridge the transition period by providing the clean liquid fuels, gas, and chemicals from the low grade feedstock. Gasification still needs many upgrades and technology breakthroughs. It remains in the niche market, not fully competitive in the major market of electricity generation, chemicals, and liquid fuels that are supplied from relatively cheap fossil fuels. The book provides the practical information for researchers and graduate students who want to review the current situation, to upgrade, and to bring in a new idea to the conventional gasification technologies