Assessment of Ore Grade Estimation Methods for Structurally Controlled Vein Deposits - A Review

Resource estimation techniques have upgraded over the past couple of years, thereby improving resource estimates. The classical method of estimation is less used in ore grade estimation than geostatistics (kriging) which proved to provide more accurate estimates by its ability to account for the geology of the deposit and assess error. Geostatistics has therefore been said to be superior over the classical methods of estimation. However, due to the complexity of using geostatistics in resource estimation, its time-consuming nature, the susceptibility to errors due to human interference, the difficulty in applying it to deposits with few data points and the difficulty in using it to estimate complicated deposits paved the way for the application of Artificial Intelligence (AI) techniques to be applied in ore grade estimation. AI techniques have been employed in diverse ore deposit types for the past two decades and have proven to provide comparable or better results than those estimated with kriging. This research aimed to review and compare the most commonly used kriging methods and AI techniques in ore grade estimation of complex structurally controlled vein deposits. The review showed that AI techniques outperformed kriging methods in ore grade estimation of vein deposits.   Keywords: Artificial Intelligence, Neural Networks, Geostatistics, Kriging, Mineral Resource, Grad

Modern approaches to control of a multiple hearth furnace in kaolin production

The aim of this thesis is to improve the overall efficiency of the multiple hearth furnace (MHF) in kaolin calcination by developing control strategies which incorporate machine learning based soft sensors to estimate mineralogy related constraints in the control strategy. The objective of the control strategy is to maximize the capacity of the furnace and minimize energy consumption while maintaining the product quality of the calcined kaolin. First, the description of the process of interest is given, highlighting the control strategy currently implemented at the calciner studied in this work. Next, the state of the art on control of calcination furnaces is presented and discussed. Then, the description of the mechanistic model of the MHF, which plays a key role in the testing environment, is provided and an analysis of the MHF dynamic behavior based on the industrial and simulated data is presented. The design of the mineralogy-driven control strategy for the multiple hearth furnace and its implementation in the simulation environment are also outlined. The analysis of the results is then presented. Furthermore, the extensive sampling campaign for testing the soft sensors and the control strategy logic of the industrial MHF is reported, and the results are analyzed and discussed. Finally, an introduction to Model Predictive Control (MPC) is presented, the design of the Linear MPC framework for the MHF in kaolin calcination is described and discussed, and future research is outlined

UNDERSTANDING STRENGTH OF DRIED IRON ORE PELLETS

After an extensive literature review, it was found that there was no reliable way for predicting the impact of binders or mixtures of binders on iron ore pellets. This is a challenging problem because iron ore pellets are a complex product of an agglomeration process which is typically controlled only to the extent that is necessary to form a quality product for ironmaking. This work identifies the resistance of a dried pellet to abrasion as a prime variable to record and analyze to understand the influence of combined pellet binders. A consistent method of measuring abrasion resistance is identified and via novel analysis shown to be highly supported by theory. In turn, this theory is used to connect abrasion resistance to compressive strength and for mixing results for application to other binder dosages. Furthermore, compatibilities and incompatibilities between a group of dispersant based binders are identified, and a methodology of understanding, categorizing, and making qualitative predictions this compatibility is also proposed. The most major conclusion is that a one-parameter model based on abrasion kinetics allows for the accurate understanding of abrasion data, which can in turn be correlated to other abrasion data with good reliability for determining the properties of mixed binders, or which can be used to estimate other mechanical properties of the pellet such as compressive strength. This provides novel insight into mixed binders using a simple test by isolating the strength contribution of the binders in the abrasion resistance

Target Tracking in Confined Environments with Uncertain Sensor Positions

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201

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

Modeling and fault detection of an industrial copper electrowinning process

Copper electrowinning plants are where high purity copper (Cu) product is obtained through electrochemical reduction of copper from the leaching solution. The presence selenium (Se) and tellurium (Te) in copper sulphide minerals may result in contamination of the leach solution and, eventually of the copper cathode. Unfortunately, hydrometallurgical processes are often difficult to monitor and control due to day-to-day fluctuations in the process as well as limitations in capturing the data at high frequencies. The purpose of this work is to model key variables in the copper electrowinning tank and to apply statistical fault detection to the selenium/tellurium removal and copper electrowinning process operations. First principle modeling was applied to the copper electrowinning tank and partial differential equation models were derived to describe the process dynamics. Industrial data were used to estimate the model parameters and validate the resulting models. Comparison with industrial model shows that the models fit reasonably well with industrial operation. Simulations of the models were run to explore the dynamics under varying operating conditions. The derived models provide a useful tool for future process modification and control development. Using the collected industrial operating data, dynamic principal component analysis (DPCA) based fault detection was applied to Se/Te removal and copper electrowinning processes at Vale’s Electrowinning Plant in Copper Cliff, ON. The fault detection results from the DPCA based approach were consistent with the industrial product quality test. After faults were detected, fault diagnosis was then applied to determine the causes of faults. The fault detection and diagnosis system helps define causes of upset conditions that lead to coppercathode contamination.Master of Applied Science (M.A.Sc.) in Natural Resources Engineerin

Editorial Advanced Techniques for Computational and Information Sciences

New techniques in computational and information sciences have played an important role in keeping advancing the so called knowledge economy. Advanced techniques have been introduced to or emerging in almost every field of the scientific world for hundreds of years, which has been accelerated since the late 1970s when the advancement in computers and digital technologies brought the world into the Information Era. In addition to the rapid development of computational intelligence and new data fusion techniques in the past thirty years This special issue is to facilitate dissemination of recent research outcomes resulting from applying innovative and advanced techniques in computational and information sciences to various scientific and engineering disciplines. The papers included in this special issue were selected from submissions to both Mathematical Problems in Engineering (MPE) directly and the 2014 International Conference on Information Technology, Computation and Applications (ICITCA2014) held in Anyang of China in December 2014, which followed the success of ICITCA2013 Categorically, there are 15 papers in the broad area of digital audio, video, and image processing and pattern recognition. S. Zhao et al. presented a variational Bayesian superresolution approach using adaptive image prior model. All these papers have made new contributions to the broad areas of computational and information sciences

Machine vision in measurement and control of mineral concentration process

This thesis considers machine vision in the context of the mining, mineral and metal industry (MMMI). Even though MMMI might be seen as a rather conservative industry branch, in many cases it is not. One motivation for constant research and development is the large amount of ore processed on a yearly basis, which means that even a slight improvement in performance can lead to substantial economical benefits. Another point, related more closely to the thesis, is that the development in camera and information technology has enabled the integration of machine vision based applications into many different industry branches, MMMI being one of them. Machine vision and its utilization in measurement and control of a modern flotation plant is studied in detail. The research was started in the late 90's with the development of an image analysis platform for flotation froths, which was later extended to cover multiple flotation cells. The resulting image analysis based variables were studied and new results regarding their usefulness both in single and multi-camera settings were obtained. The most important variables are shown to the plant operators and used in closed loop control. Furthermore, an image history database and a tool for its utilization were created, as well as a new type of froth level measurement technique introduced. The research done with the image analysis of flotation froths provided strong evidence of the importance of the froth colour as an indicator of grade. This motivated further studies carried out with a spectrophotometer, which is a more accurate instrument for colour measurements. As a result, a new type of on-line measurement technique was created to be used as a supplement to existing X-Ray fluorescence (XRF) analyzers to reduce their typical sampling interval of 10-20 minutes to a virtually continuous measurement. Another field of research presented is the particle size distribution analysis of crushed ore from a moving conveyor belt in a contact-free manner, for which two new measurement techniques are presented. This information, when measured already in the mine, can be used in the flotation plant to gain better grinding results, and geologists can use it in mine planning

Design of hydrometallurgical stages for reprocessing artisanal mine tailings from Madre de Dios

The objective of this work was to design hydrometallurgical stages to reprocess artisanal gravimetric table concentration tailings from Madre de Dios. These tailings are currently considered waste, even though they still contain gold and rare earths. The scope is limited to the design and technical-economic assessment of leaching stages of gold and rare earths, as well as their subsequent recovery and the neutralization of effluents. Leaching stages were experimentally evaluated in a laboratory scale through a three-step experimental design. Each stage evaluated one independent variable on two levels and its effect on reagent consumption and gold or rare earths extraction. The first step considered gold extraction through thiourea leaching with ferric sulfate as oxidizing agent in an equivalent molar proportion to thiourea. The second step evaluated the inclusion of a hydrochloric acid leaching stage at 80°C before gold leaching. Finally, the third experimental step included a pretreatment at 80°C with potassium hydroxide before the other two stages. According to experimental results, rare earth extraction was too low to consider it further, while thiourea leaching achieved 86.8% of gold extraction after 1 hour, using a thiourea concentration of 8 g/L on a 40% solids slurry. Based on these results, the designed batch process included the following stages: thiourea leaching, sedimentation of solids, cementation of gold using zinc powder, effluent neutralization, and zinc dissolution with sulfuric acid to recover zinc-free, high purity solid gold. Reagent and energy requirements were estimated for 40 kg batches of tailings. Approximately 0.5 g of gold are obtained per batch. An economic assessment indicated that for a five-year projection on which 500 batches are processed yearly, the net present value of the project is $2555.37, and the internal rate of return is 27.2%. A sensitivity analysis revealed that the project can remain profitable if the capital expenditure and the cost of reagents are modified within a ±20% range, while the price of gold and the number of yearly batches can only be reduced by 7.5% and 11.4%, respectively