Quantification of uncertainty of geometallurgical variables for mine planning optimisation

Interest in geometallurgy has increased significantly over the past 15 years or so because of the benefits it brings to mine planning and operation. Its use and integration into design, planning and operation is becoming increasingly critical especially in the context of declining ore grades and increasing mining and processing costs. This thesis, comprising four papers, offers methodologies and methods to quantify geometallurgical uncertainty and enrich the block model with geometallurgical variables, which contribute to improved optimisation of mining operations. This enhanced block model is termed a geometallurgical block model. Bootstrapped non-linear regression models by projection pursuit were built to predict grindability indices and recovery, and quantify model uncertainty. These models are useful for populating the geometallurgical block model with response attributes. New multi-objective optimisation formulations for block caving mining were formulated and solved by a meta-heuristics solver focussing on maximising the project revenue and, at the same time, minimising several risk measures. A novel clustering method, which is able to use both continuous and categorical attributes and incorporate expert knowledge, was also developed for geometallurgical domaining which characterises the deposit according to its metallurgical response. The concept of geometallurgical dilution was formulated and used for optimising production scheduling in an open-pit case study.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201

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

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

Link Patterns in Complex Networks

Network theorists define patterns in complex networks in various ways to make them accessible to human beholders. Prominent definitions are thereby based on the partition of the network's nodes into groups such that underlying patterns in the link structure become apparent. Clustering and blockmodeling are two well-known approaches of this kind. In this thesis, we treat pattern search problems as discrete mathematical optimization problems. From this viewpoint, we develop a new mathematical classification of clustering and blockmodeling approaches, which unifies these two fields and replaces several NP-hardness proofs by a single one. We furthermore use this classification to develop integer mathematical programming formulations for pattern search problems and discuss new linearization techniques for polynomial functions therein. We apply these results to a model for a new pattern search problem. Even though it is the most basic problem in combinatorial terms, we can prove its NP-hardness. In fact, we show that it is a generalization of well-known problems including the Traveling Salesman and the Quadratic Assignment Problem. Our derived exact pattern search procedure is up to 10,000 times faster than comparable methods from the literature. To demonstrate its practicability, we finally apply the procedure to the world trade network from the United Nations' database and show that the network deviates by less than 0.14% from the patterns we found

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems

Fuzzy clustering with spatial correction and its application to geometallurgical domaining

Published online: 25 July 2018This paper describes a proposed method for clustering attributes on the basis of their spatial variability and the uncertainty of cluster member- ship. The method is applied to geometallurgical domaining in mining ap- plications. The main objective of geometallurgical clustering is to ensure consistent feed to a processing plant by minimising transitions between di erent types of feed coming from di erent domains (clusters). For this purpose, clusters should contain not only similar geometallurgical char- acteristics but also be located in as few contiguous and compact spatial locations as possible so as to maximise the homogeneity of ore delivered to the plant. Most existing clustering methods applied to geometallurgy have two problems. Firstly, they are unable to di erentiate subsets of attributes at the cluster level and therefore cluster membership can only be assigned on the basis of exactly identical attributes, which may not be the case in practice. Secondly, as they do not take account of the spatial relationships they can produce clusters which may be spatially dispersed and/or overlapped. In the work described in this paper a new clustering method is introduced that integrates three distinct steps to ensure qual- ity clustering. In the rst step, fuzzy membership information is used to minimise compactness and maximise separation. In the second step, the best subsets of attributes are de ned and applied for domaining purposes. These two steps are iterated to convergence. In the nal step a graph- based labelling method, which takes spatial constraints into account, is used to produce the nal clusters. Three examples are presented to illus- trate the application of the proposed method. These examples demon- strate that the proposed method can reveal useful relationships among geometallurgical attributes within a clear and compact spatial structure. The resulting clusters can be used directly in mine planning to optimise the ore feed to be delivered to the processing plant.E. Sepúlveda, P. A. Dowd, C. X

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

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

Adaptive monitoring and control framework in Application Service Management environment

The economics of data centres and cloud computing services have pushed hardware and software requirements to the limits, leaving only very small performance overhead before systems get into saturation. For Application Service Management–ASM, this carries the growing risk of impacting the execution times of various processes. In order to deliver a stable service at times of great demand for computational power, enterprise data centres and cloud providers must implement fast and robust control mechanisms that are capable of adapting to changing operating conditions while satisfying service–level agreements. In ASM practice, there are normally two methods for dealing with increased load, namely increasing computational power or releasing load. The first approach typically involves allocating additional machines, which must be available, waiting idle, to deal with high demand situations. The second approach is implemented by terminating incoming actions that are less important to new activity demand patterns, throttling, or rescheduling jobs. Although most modern cloud platforms, or operating systems, do not allow adaptive/automatic termination of processes, tasks or actions, it is administrators’ common practice to manually end, or stop, tasks or actions at any level of the system, such as at the level of a node, function, or process, or kill a long session that is executing on a database server. In this context, adaptive control of actions termination remains a significantly underutilised subject of Application Service Management and deserves further consideration. For example, this approach may be eminently suitable for systems with harsh execution time Service Level Agreements, such as real–time systems, or systems running under conditions of hard pressure on power supplies, systems running under variable priority, or constraints set up by the green computing paradigm. Along this line of work, the thesis investigates the potential of dimension relevance and metrics signals decomposition as methods that would enable more efficient action termination. These methods are integrated in adaptive control emulators and actuators powered by neural networks that are used to adjust the operation of the system to better conditions in environments with established goals seen from both system performance and economics perspectives. The behaviour of the proposed control framework is evaluated using complex load and service agreements scenarios of systems compatible with the requirements of on–premises, elastic compute cloud deployments, server–less computing, and micro–services architectures