Stochastic-optimization of equipment productivity in multi-seam formations

Short and long range planning and execution for multi-seam coal formations (MSFs) are challenging with complex extraction mechanisms. Stripping equipment selection and scheduling are functions of the physical dynamics of the mine and the operational mechanisms of its components, thus its productivity is dependent on these parameters. Previous research studies did not incorporate quantitative relationships between equipment productivities and extraction dynamics in MSFs. The intrinsic variability of excavation and spoiling dynamics must also form part of existing models. This research formulates quantitative relationships of equipment productivities using Branch-and-Bound algorithms and Lagrange Parameterization approaches. The stochastic processes are resolved via Monte Carlo/Latin Hypercube simulation techniques within @RISK framework. The model was presented with a bituminous coal mining case in the Appalachian field. The simulated results showed a 3.51% improvement in mining cost and 0.19% increment in net present value. A 76.95yd³ drop in productivity per unit change in cycle time was recorded for sub-optimal equipment schedules. The geologic variability and equipment operational parameters restricted any possible change in the cost function. A 50.3% chance of the mining cost increasing above its current value was driven by the volume of material re-handled with 0.52 regression coefficient. The study advances the optimization process in mine planning and scheduling algorithms, to efficiently capture future uncertainties surrounding multivariate random functions. The main novelty includes the application of stochastic-optimization procedures to improve equipment productivity in MSFs --Abstract, page iii

Optimised decision-making under grade uncertainty in surface mining

Mining schedule optimisation often ignores geological and economic risks in favour of simplistic deterministic methods. In this thesis a scenario optimisation approach is developed which uses MILP optimisation results from multiple conditional simulations of geological data to derive a unique solution. The research also generated an interpretive framework which incorporates the use of the Coefficient of Variation allowing the assessment of various optimisation results in order to find the solution with the most attractive risk-return ratio

Méthode heuristique d’optimisation stochastique de la planification minière et positionnement des résidus miniers dans la fosse

RÉSUMÉ : La planification minière à long terme est essentielle afin d’estimer la viabilité d’un projet, d’obtenir les investissements nécessaires et d’optimiser les ressources disponibles. La recherche opérationnelle est en mesure de répondre efficacement à ce problème, plusieurs modèles mathématiques de programmation linéaire mixte ont été développés. La principale source d’incertitude, encore très peu considérée conventionnellement, est géologique. Pour la prendre en compte, des simulations conditionnelles, représentations équiprobables du gisement, peuvent être utilisées comme données d’entrée à un modèle stochastique en nombres entiers. Ainsi, l’objectif est de maximiser la valeur présente nette moyenne tout en proposant un ordonnancement de la production robuste à l’incertitude. En ajoutant un nombre conséquent de blocs, plusieurs périodes et destinations ainsi que de nombreuses contraintes opérationnelles, les modèles deviennent trop complexes à résoudre de manière exacte avec un solveur. Des méthodes heuristiques doivent alors être envisagées pour obtenir la meilleure solution possible en un temps réduit. Le travail exposé dans ce mémoire est composé de deux parties correspondant à deux différents articles. La première partie présente la résolution d’un modèle stochastique d’optimisation d’une mine à ciel ouvert à l’aide d’une nouvelle méthode heuristique. Sont tout d’abord proposées deux méthodes d’accélération : une relaxation partielle de la binarité des variables d’extraction en utilisant la structure particulière du modèle et les fortes relations entre variables et une convergence du modèle relaxé vers une solution quasi-binaire. Ensuite, un algorithme de tri topologique stochastique est proposé afin d’obtenir rapidement une solution complètement binaire à partir des résultats issus des stratégies d’accélération précédentes. Les résultats obtenus, testés sur un cas réel, sont concluants par leur rapidité et gap par rapport à la solution optimale. La deuxième partie modélise le stockage des résidus miniers et stériles au sein de la fosse au fur et à mesure de l’exploitation. Cette idée, souhaitée par le partenaire industriel lors de l’étude de faisabilité, permet de s’affranchir d’un espace de stockage limité autour de la fosse, de réduire l’impact environnemental de l’exploitation et de diminuer les coûts de remaniement lors de la réhabilitation finale du site. Cette fois, une méthode heuristique de fenêtre de temps sur un horizon fuyant a été utilisée pour résoudre le modèle. Les résultats sont prometteurs puisque l’impact de la disposition de matériel dans la fosse lors de l’exploitation ne dégrade la solution initiale que de 1.77%.----------ABSTRACT : Long-term mine planning is an essential step in order to estimate the viability of a project, to attract investments and to optimize available resources. Operations research is well suited to assess this kind of problem, several mixed integer programming models have been developed over the last decades. Even if still not conventionally considered, the geology is the main source of uncertainty in such a model. To consider it properly, a set of equiprobable conditional simulations of the deposit are used as input in a stochastic integer programming model. The objective is then to maximize the expected net present value while having a robust production schedule to the geological uncertainty. When many blocks are considered but also several destinations and operational constraints, the problem becomes too complex to solve by a general purpose solver. If an exact method is not conceivable, heuristic methods must be used to obtain the best solution as possible in a limited time. The study presented in the thesis is composed of two parts corresponding to two articles. The first one presents a new heuristic method to solve a stochastic open pit mine production scheduling problem. First, two acceleration strategies are proposed: a partial relaxation of the binarity of the extraction variables using the special structure of the model and the strong inter-correlations of the variables; a convergence of the fully relaxed model toward a quasi-binary solution. Then, a stochastic topological sorting algorithm is proposed to quickly obtain a binary solution from the result of the previous acceleration strategies. Applied on a real case study, the results are interesting for their rapidity and their gap to the optimality. The second part establishes a model to store tailings and waste materials directly inside the pit during the operations. This idea was raised in the feasibility of the industrial partner to overpass a limited space for eternal stockpiles, to reduce the environmental impact and the re-handling costs of the rehabilitation phase. This time, to solve the problem, a sliding window time heuristic method was used and the results are promising: the Cplex objective function is only 1.77% lower while considering the in-pit storage and the heuristic method than the initial model solved with an exact method

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Planning and Optimisation Methods for Lunar In-Situ Resource Utilisation

Lunar water resources are expected to be used for space exploration and development in the future. These resources can be used for life support and rocket fuel to reduce the risks and costs associated with lunar settlement. There is a notable gap in literature relating to the planning and optimisation of lunar resource extraction. This thesis aims to address the problem by developing tools for planning and optimisation of In-Situ Resource Utilisation (ISRU) on the Moon, with a focus on H2O resources. The multidisciplinary tools currently used in the terrestrial mining industry are examined as possible solutions to fill the gap. However, several issues are identified with the direct transfer of these methods to ISRU. Four foundational areas of mining engineering are then expanded for off-Earth applications. These are geomechanics and modelling, mining system selection, extraction sequence optimisation and project valuation. For geomechanics, the Discrete Element Method (DEM) is used to determine the stability of regolith excavations on the Moon. This method is also extended to the development of ground engaging tools under lunar gravity. Conceptual proofs are shown for two novel mining systems using DEM, the Impact Excavator and Drill and Pull method. With further development, these new rock breakage systems can improve ISRU planning and optimisation by enabling the access of harder, higher grade icy regolith. Within literature, there are also numerous off-Earth mining systems described. A procedure is developed to objectively select a mining system for a range of possible space resource deposit types. The procedure utilises principles of Axiomatic Design to estimate the reliability of systems in the absence of experimental data. These system reliabilities assist in making selections that can be used as inputs for subsequent planning and optimisation activities. Traditional optimisation algorithms, such the Lerchs-Grossman pit optimisation method and other graph-based methods are next examined for their applicability to off-Earth mining. They are found to be incompatible when directly applied to ISRU and a new paradigm is developed based on Reinforcement Learning. This method has advantages over the traditional mine optimisation algorithms and solves many of the issues identified for ISRU. For example, it does not require uncertain financial inputs such as cost estimations or price forecasting. This particular weakness in financial inputs for off-Earth mine planning is also addressed for project valuations. An opportunity cost measure, the Propellant Payback Ratio, is shown to overcome many of the difficult input requirements of the traditional method for the purpose of ISRU project appraisal. It enables ISRU project appraisals to be conducted completely independent of the uncertain financial inputs mentioned. Overall, the thesis contributes to the expansion of the mining engineering discipline into the ISRU domain. Four interconnected areas of mining engineering are developed including: geomechanics, mining system selection, sequence optimisation and project appraisal. These are all part of a multidisciplinary approach to ISRU planning and optimisation. Although ISRU has so far not begun, the methods and tools developed here can be used to improve the future prospect of resource utilisation on the Moon

A MULTIDISCIPLINARY APPROACH TO THE MANAGEMENT OF A NON-NATIVE TROUT SPECIES

Non-native freshwater fish are considered a significant threat to the survival of native freshwater fish populations. Traditional management strategies for dealing with non-native fish such as chemical or mechanical removal have limitations and can be unsuccessful. A novel method for the removal of non-native fish is a technique where the addition of non-native male fish with a YY genotype (MYY) theoretically results in a shift of the population sex ratio towards all or mostly all males, driving population extirpation. However, many aspects that affect the success of this method have not been thoroughly tested. My doctoral research incorporated multiple disciplines to enhance the management strategies for a non-native trout, the brook trout (Salvelinus fontinalis). Currently, in the Boundary Dam reservoir in Washington, an extensive management program of these non-native fish is underway, including suppression and chemical removal as well as introduction of MYY. I first used genetic monitoring to provide managers with information regarding the genetic structure of brook trout populations and provide information about population resilience in the face of management efforts. I found evidence of significant genetic substructure within the system and highlighted three populations that were most likely to be successfully eradicated due to limited gene flow. I also found evidence of isolation by distance within the largest Boundary tributary (Sullivan Creek) suggesting that partial eradication within this system would likely be followed by recolonization. Next, I performed a study of the reproductive performance of MYY brook trout compared to hatchery XY brook trout in a lab-based setting. My results indicate that MYY brook trout perform similarly to hatchery XY males at fertilization and their offspring survive similarly at early development stages suggesting they could be an effective tool in non-native brook trout eradication efforts. Simulation studies testing the effectiveness of MYY have suggested that eradication success and/or minimum population size of the non-native population may be affected by many different factors. However, no studies to date have looked at the possible consequences on the remaining population if MYY management plans result in failure to eradicate. Suppression and MYY introduction cause reductions in the abundance of the population, essentially forcing these populations through a population bottleneck and increasing the chance of inbreeding depression (ID). I performed a simulation study that looked at the effects of ID on the bottleneck and recovery of the remnant brook trout population after suppression and MYY introduction if it does not result in eradication. I found that during MYY introduction, ID resulted in a decrease in the population abundance compared to models that did not include fitness effects. However, because of increased genetic variation due to hatchery MYY admixture, populations recovered to above pre-treatment levels for most simulations post-suppression and MYY treatment. This result suggests that even if populations are driven to very low abundance, managers should not rely on them going extinct due to the effects of ID. Finally, I conducted a survey of wildlife managers to determine how manager characteristics influence the likelihood that managers will implement two novel management methods (MYY implementation and genetic rescue) to conserve native headwater stream fish populations. Findings suggest that risk tolerance was a good indicator for managers willingness to implement novel strategies. Additionally, we found differences for managers from different states and regions in their willingness to implement novel strategies. These results show that understanding the individual characteristics of managers is important for identifying factors that hinder the implementation of novel methods in the conservation of species. Overall, this research demonstrates that genetic tools can be informative when managing non-native species. MYY may be an effective approach to the management of nonnative species, however, caution should be taken as incomplete eradication could result in full recovery of the population. Finally, understanding manager characteristics could be beneficial for determining whether managers are willing to implement novel management strategies