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By H. Dudycz, A. Skoczylas, P. Stefaniak, P. Pyda

In the era of Industry 4.0, technologies such as Big Data (BD) and the Internet of Things (IoT) play a key role, especially for multi-site manufacturing companies. In such companies, the challenge is to collect and process massive amounts of data from industrial devices and extract key information from this data in real-time for ad hoc decision-making. The ability to analyse data from industrial devices translates directly into streamlined production processes, cost optimisation, prevention of unplanned events, and safety during operation. In this context, particular attention should be paid to Big Data Analytics (BDA), whose main source is the Industrial Internet of Things (IIoT). The purpose of this paper is to propose an IT system architecture incorporating BD and the IoT for a mining company. The paper presents results from a literature review focusing on available BD and the IoT solutions for industry (mining in particular) for the purposes of the acquisition, integration, and advanced analysis of data. The advantages and disadvantages of the different solutions were indicated and gaps in the accessibility to dedicated functionality are identified. Based on the analysis of a multi-site mining company, the main challenges faced by such companies planning to implement BD and IoT solutions are identified. Also, a proposal for an IT system architecture incorporating BD and IoT for a mining company is described in the paper. The contribution of the article consists of the identification of the challenges faced by mining companies planning to implement BD and the IoT solutions, and the proposal of an IT system architecture that allows BDA whose main source is the IIoT.

Sep 1, 2021

By M. A. Mahboob, T. Celik, B. Genc, I. Atif, S. Ali

"Mapping of hydrothermally altered areas, which are usually associated with mineralization, is essential in mineral exploration. In this research, open source reflectance spectroscopy data from the multispectral moderate-resolution Landsat 8 satellite was used to map altered rocks in the Gauteng and Mpumalanga provinces of South Africa. The unique spectral reflectance and absorption characteristics of remotely sensed Landsat data in the visible, near-infrared (NIR), shortwave-infrared (SWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum were used in different digital image processing techniques. The band ratios (red/blue, SWIR 2/NIR, SWIR 1/NIR), spectral band combinations (Kaufmann ratio, Sabins ratio) and principal component analysis (Crosta technique) were applied to efficiently and successfully map hydrothermal alteration minerals. The results showed that the combination of spectral bands and the principal component analysis method is effective in delineating mineral alteration through remotely sensed satellite data. The validation of results by using the published mineral maps of the Council for Geoscience South Africa showed a good relationship with the identified zones of mineralization. The methodology developed in this study is cost-effective and time-saving, and can be applied to inaccessible and/or new areas with limited ground-based knowledge to obtain reliable and up-to-date mineral information.IntroductionMany developing nations depend on exploration and exploitation of mineral resources to sustain their economic growth. Usually, the traditional mineral exploration techniques require enormous finances, prolonged time, and tremendous manpower, particularly in areas that are not easily reachable (Maduaka, 2014). Furthermore, mineral exploration required state-of-the-art techniques and expertise along with geological, geochemical, and geophysical datasets, which may not be easily available or may be lacking where access is problematic (Kaiser et al., 2002; Bemis et al., 2014). Modern remote sensing technology has proved to be one of the highly efficient and robust techniques used for mineral exploration. The use of remote sensing satellite images for geological mapping and mineral exploration usually involves studying the physicochemical properties of rocks and weathering soils, such as mineralogy, landforms, geochemical signatures, and the spatiall distribution of lineaments (Bhattacharya et al., 2012)."

Mar 1, 2019

By E. Y. Baafi, H. Fan, W. Zeng

We present a discrete event simulator, TSJSim (Truck-Shovel JaamSim Simulator), for evaluating the stochastic and dynamic operational variables in a truck-shovel system. TSJSim offers four truck allocation strategies: Fixed truck assignment (FTA), Minimizing shovel production requirement (MSPR), Minimizing truck waiting time (MTWT), and Minimizing truck semi-cycle time (MTSCT) including the genetic algorithm (GA) optimization and the frozen dispatching algorithm (FDA) optimization rules. Multiple decision points along the haul routes for all the trucks close to the decision points were included in the model. The simulation results indicate that the trends associated with production tons and queuing time utilizing the four truck allocation strategies (MSPR, MTWT, FDA, and GA) all demonstrated similar patterns as the fleet size varied. As the system fleet size increased, the system production tons under these strategies at first increased significantly and then remained relatively constant; the queuing time relating to these strategies showed a positive relationship with the system fleet size. The bunching time decreased when the truck allocation strategies were applied in the model. In the simulated truck-shovel network system with multiple traffic intersections, by assigning the trucks at the intersections, both productivity and fleet utilization increased.

Dec 13, 2022

By B. Maybee, J. Venter, J. Wesseloo

The selection of open-pit slope angles involves high-value decisions with small changes in slope angle, often representing significant changes in net present value. The existing methods used to define design acceptance criteria have evolved from factor of safety to probability of failure (P[F]) to risk consequence to risk frontiers, which represents current state of the art. Current design acceptance measures are based on references to published tables representing the experience and judgement of their authors, who did not specify the purpose of the measures contained therein. As the purpose of the published design acceptance criteria is not specified (i.e., definite stability, marginal stability, optimization, etc.), such tables cannot be used to achieve optimum slope angles that maximize profitability. This paper develops a design acceptance criterion that maximizes profitability by defining a formulation for optimum risk that balances expected risk and reward. The model developed is titled the Mining Risk Model (MRM), and is applied to open-pit slope angle selection through an equation for optimum probability of downside (P[D]O) that balances the upside or opportunity, the P[F], and the downside impact. This formulation for optimum risk is unique, as many authors have presented objective functions for their risk models that can be optimized, but none of the sources reviewed contained a formulation for optimum risk. The MRM is sufficiently flexible to allow the design performance measure that drives P[F], and hence P[D]O, to be selected based on the intended goal. Furthermore, the essential information that must be known to quantify optimum risk is defined. This allows users to determine what information to collect for optimum risk decisions. A further benefit of the MRM is that slope angle decisions and pit shells can be ranked to select the best option, and a threshold is provided that separates acceptable from unacceptable decisions. Finally, the workflow and information required to determine optimum risk are presented.

Sep 9, 2024