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

Belief Space Scheduling

This thesis develops the belief space scheduling framework for scheduling under uncertainty in Stochastic Collection and Replenishment (SCAR) scenarios. SCAR scenarios involve the transportation of a resource such as fuel to agents operating in the field. Key characteristics of this scenario are persistent operation of the agents, and consideration of uncertainty. Belief space scheduling performs optimisation on probability distributions describing the state of the system. It consists of three major components---estimation of the current system state given uncertain sensor readings, prediction of the future state given a schedule of tasks, and optimisation of the schedule of the replenishing agents. The state estimation problem is complicated by a number of constraints that act on the state. A novel extension of the truncated Kalman Filter is developed for soft constraints that have uncertainty described by a Gaussian distribution. This is shown to outperform existing estimation methods, striking a balance between the high uncertainty of methods that ignore the constraints and the overconfidence of methods that ignore the uncertainty of the constraints. To predict the future state of the system, a novel analytical, continuous-time framework is proposed. This framework uses multiple Gaussian approximations to propagate the probability distributions describing the system state into the future. It is compared with a Monte Carlo framework and is shown to provide similar discrimination performance while computing, in most cases, orders of magnitude faster. Finally, several branch and bound tree search methods are developed for the optimisation problem. These methods focus optimisation efforts on earlier tasks within a model predictive control-like framework. Combined with the estimation and prediction methods, these are shown to outperform existing approaches

Performance Evaluation of Surface Mining Equipment with Particular Reference to Shovel-Dumper Mining

Surface mining is the most well-known mining around the world, and open pit mining accounts for more than 60% of all surface yield. Haulage costs represent as much as 60% of the aggregate working expense for these type of mines, so it is desirable to keep up an effective haulage framework. Equipment availability and estimation of utilization very precisely which is very important since mine manager wants to utilize their equipment as effectively as possible to get an early return of their investment as well as reducing total production cost. In present situation to achieve high production and productivity of HEMMs in opencast mines, it is desired to have high % availability and % utilization of equipment besides ensure overall equipment effectiveness as per CMPDI norms/global bench marks. OEE shows that how an equipment is utilized with its maximum effectiveness. It uses parameters like availability, performance and utilization for the estimation of equipment effectiveness. One method for effectively use of equipment in haul cycle is queuing theory. Queuing theory was developed to model systems that provide service for randomly arising demands and predict the behaviour of such systems. A queuing system is one in which customers arrive for service, wait for service if it is not immediately available, and move on to the next server or exit the system once they have been serviced. Most mining haul routes consist of four main components: loading, loaded hauling, dumping, and unloaded hauling to return to the loader. These components can be modelled together as servers in one cyclic queuing network, or independently as individual service channels. Data from a large open pit mine are analysed and applied to a multichannel queuing model representative of the loading process of the haul cycle. The outputs of the model are compared against the actual dumper data to evaluate the validity of the queuing model developed

A simulation model for truck-shovel operation

A truck-shovel mining system is a flexible mining method commonly used in surface mines. Both simulation and queuing models are commonly used to model the truckshovel mining operation. One fundamental problem associated with these types of models is that most of the models handle the truck haulage system as macroscopic simulation models, which ignore the fact that a truck as an individual vehicle unit dynamically interacts not merely with other trucks in the system but also with other elements of the traffic network. Some important operational factors, such as the bunching effect and the influence of the traffic intersections, are either over simplified or ignored in such a macroscopic model. This thesis presents a developed discrete-event truck-shovel simulation model, referred to as TSJSim (Truck and Shovel JaamSim Simulator), based on a microscopic traffic and truck-allocation approach. The TSJSim simulation model may be used to evaluate the Key Performance Indicators (KPIs) of the truck-shovel mining system in an open pit mine. TSJSim considers a truck as an individual traffic vehicle unit that dynamically interacts with other trucks in the system as well as other elements of the traffic network. TSJSim accounts for the bunching of trucks on the haul routes, practical rules at intersections, multiple decision points along the haul routes as well as the influence of the truck allocation on the estimated queuing time. TSJSim also offers four truck-allocation modules: Fixed Truck Assignment (FTA), Minimising Shovel Production Requirement (MSPR), Minimising Truck Waiting Time (MTWT) and Minimising Truck Semi-cycle Time (MTSCT) including Genetic Algorithm (GA) and Frozen Dispatching Algorithm (FDA)

FusionPlanner: A Multi-task Motion Planner for Mining Trucks using Multi-sensor Fusion Method

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. \textcolor{red}{Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the Multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives.} \textcolor{red}{The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation.Comment: 2Pages, 10 figure

Smart Manufacturing as a framework for Smart Mining

Based on the analogy between manufacturing and mining (i.e. ore 'production'), smart mining has four dimensions: (i) advanced digital-oriented technologies (such as Cloud computing and the Internet of things) with automated Cyber-Physical Systems (CPSs), adaptable production processes (dependent on working conditions) and production volume control (with optimal resource consumption); (ii) smart maintenance of CPSs; (iii) new ways for workers to perform their activities, using advanced digital-oriented technologies; and (iv) smart supply-chain (procurement of materials and spare parts / products delivery). This paper presents a case study on the smart mining approach implemented at a coal mining system in Serbia

Smart Manufacturing as a framework for Smart Mining

Based on the analogy between manufacturing and mining (i.e. ore 'production'), smart mining has four dimensions: (i) advanced digital-oriented technologies (such as Cloud computing and the Internet of things) with automated Cyber-Physical Systems (CPSs), adaptable production processes (dependent on working conditions) and production volume control (with optimal resource consumption); (ii) smart maintenance of CPSs; (iii) new ways for workers to perform their activities, using advanced digital-oriented technologies; and (iv) smart supply-chain (procurement of materials and spare parts / products delivery). This paper presents a case study on the smart mining approach implemented at a coal mining system in Serbia

A stochastic method for representation, modelling and fusion of excavated material in mining

The ability to safely and economically extract raw materials such as iron ore from a greater number of remote, isolated and possibly dangerous locations will become more pressing over the coming decades as easily accessible deposits become depleted. An autonomous mining system has the potential to make the mining process more efficient, predictable and safe under these changing conditions. One of the key parts of the mining process is the estimation and tracking of bulk material through the mining production chain. Current state-of-the-art tracking and estimation systems use a deterministic representation for bulk material. This is problematic for wide-scale automation of mine processes as there is no measurement of the uncertainty in the estimates provided. A probabilistic representation is critical for autonomous systems to correctly interpret and fuse the available data in order to make the most informed decision given the available information without human intervention. This thesis investigates whether bulk material properties can be represented probabilistically through a mining production chain to provide statistically consistent estimates of the material at each stage of the production chain. Experiments and methods within this thesis focus on the load-haul-dump cycle. The development of a representation of bulk material using lumped masses is presented. A method for tracking and estimation of these lumped masses within the mining production chain using an 'Augmented State Kalman Filter' (ASKF) is developed. The method ensures that the fusion of new information at different stages will provide statistically consistent estimates of the lumped mass. There is a particular focus on the feasibility and practicality of implementing a solution on a production mine site given the current sensing technology available and how it can be adapted for use within the developed estimation system (with particular focus on remote sensing and volume estimation)

Contribution to the capacity determination of semi-mobile in-pit crushing and conveying systems

As ore grades decline, waste rock to ore ratios increase and mines become progressively deeper mining operations face challenges in more complex scenarios. Today´s predominant means of material transport in hard-rock surface mines are conventional mining trucks however despite rationalisation efforts material transportation cost increased significantly over the last decades and currently reach up to 60% of overall mining. Thus, considerations and efforts to reduce overall mining costs, promise highest success when focusing on the development of more economic material transport methods. Semi-mobile in-pit crusher and conveyor (SMIPCC) systems represent a viable, safer and less fossil fuel dependent alternative however its viability is still highly argued as inadequate methods for the long term projection of system capacity leads to high uncertainty and consequently higher risk. Therefore, the objective of this thesis is to develop a structured method for the determination of In-pit crusher and conveyor SMIPCC system that incorporates the random behaviour of system elements and their interaction. The method is based on a structured time usage model specific to SMIPCC system supported by a stochastic simulation. The developed method is used in a case study based on a hypothetical mine environment to analyse the system behaviour with regards to time usage model component, system capacity, and cost as a function of truck quantity and stockpile capacity. Furthermore, a comparison between a conventional truck & shovel system and SMIPCC system is provided. Results show that the capacity of a SMIPCC system reaches an optimum in terms of cost per tonne, which is 24% (22 cents per tonne) lower than a truck and shovel system. In addition, the developed method is found to be effective in providing a significantly higher level of information, which can be used in the mining industry to accurately project the economic viability of implementing a SMIPCC system

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p