Virtual Mine Research Articles

Autonomous mining through cooperative driving and operations enabled by parallel intelligence

Autonomous mining is promising to address several current issues in the mining sector, such as low productivity, safety concerns, and labor shortages. Although partial automation has been achieved in some mining operations, fully autonomous mining remains challenging due to its complexity and scalability in field environments. Here we propose an autonomous mining framework based on the parallel intelligence methodology, employing self-evolving digital twins to model and guide mining processes in the real world. Our framework features a virtual mining subsystem that learns from simulating real-world scenarios and generates new ones, allowing for low-cost training and testing of the integrated autonomous mining system. Through initial validation and extensive testing, particularly in open-pit mining scenarios, our framework has demonstrated stable and efficient autonomous operations. We’ve since deployed it across more than 30 mines, resulting in the extraction of over 30 million tons of minerals. This implementation effectively eliminates the exposure of human operators to hazardous conditions while ensuring 24-hour uninterrupted operation.

Virtual mining of polymer monomers for photodetectors application and regression-aided reorganization energy prediction

Predicting and understanding the charge transport properties of organic semiconductors is a crucial target for constructing efficient electronic devices such as photodetectors. To this end, reorganization energy (Re) is a key parameter for molecular design, which provides quantitative strength of electron-photon coupling process (charge transport parameter). Although modern density functional theory-based approaches can accurately simulate intramolecular RE trends, such computations are time-consuming and costly. In this study, we present machine-learning tools for the accurate and fast prediction of intramolecular Re. These tools are effectively used for screening of new compounds with low internal Re. Moreover, virtual mining of polymer monomers is performed for photodetector applications. Machine learning models are developed with molecular descriptors (features). Regression analysis is used to show how two or more variables are related. The dependent variable yields the best outcomes and helps to predict Re. This research shows that chemical designs can accurately predict the REs of organic semiconductor materials. Out of five different regressions models, gradient booster regression gives the best prediction capability (R2 = 0.761). Moreover, chemical similarity analysis (CSA) is used to search for a new group of molecules with high performance. Library enumeration is used for structure determination and for studying the properties of these small molecules. The fitness score of newly designed quinoxaline is more than 0.9, which shows best outcome. The new strategy holds immense potential for the virtual mining of polymer monomers for photodetectors applications and regression-aided reorganization energy predictions.

MetaMining: Mining in the Metaverse

Mines are one of the important energy sources in the world. Due to mining areas are often affected by adverse weather and environmental conditions (sand, dust, extreme cold, heavy snow, etc.), the efficiency and security of mining are low. As a new, efficient, prospective mode, Metaverse has been studied and successfully applied in various industries. However, there is still no research about its usage in mines. In this article, we apply Metaverse to mining and propose MetaMining. We present its definition and analyze the functions it could perform. Furthermore, we propose its development phases and architecture. Specifically, MetaMining is a mode, which aims to achieve high-efficiency, high-security mining in the physical world through the interaction between the physical world and the virtual world. Its development phases involve three steps: 1) digital twins; 2) digital natives; and 3) surreality. Its architecture consists of three components: 1) human world; 2) virtual mining system; and 3) physical mining system. In addition, we analyze key technologies and possible challenges in the construction of MetaMining.

New Game Artificial Intelligence Tools for Virtual Mine on Unreal Engine

Currently, the gamification of virtual reality for training miners, especially for emergencies, and designing the extraction of minerals in difficult technological conditions has been embodied in the Virtual Mine software and hardware. From a software development point of view, Virtual Mine is indistinguishable from other virtual reality games, and this offers a chance to use the potential of rapidly developing game software in mining, including engines, 3D modeling tools, audio editors, etc., to solve a wide range of game development tasks. The chosen direction will optimize the work of developers by providing a tool for developing game artificial intelligence to solve problems that require implementing the behavior of game agents without using a rigidly defined choice of scenarios or chains of these scenarios. The aim of the work is to expand the possibilities of working with game artificial intelligence on the Unreal Engine game engine to make it more functional. As a result, a tool has been obtained that can be used to optimize the time and improve the quality of the development of game artificial intelligence for Virtual Mine using flexible development approaches. The asset editor was developed, application modes and their working tabs were defined, and a graphical node system for the behavioral graph editor was created. A system for executing a behavioral graph is given; algorithms for its operation and features for executing nodes of a behavioral graph are presented.

The study of virtual reality influence on the process of professional training of miners.

Virtual reality technologies are actively applied for the organization of professional training in various industries, as well as in distance learning. However, numerous studies show the presence of a large number of negative factors that limit the effectiveness of using these technologies (united by the concept of "cybersickness"). The study, identification and reduction in the influence of these negative factors will increase the immersiveness and quality of the professional training process. Within the framework of this study, several hypotheses have been put forward regarding the negative and positive impact of VR technologies on the process of professional training, the coal and mining industry has been chosen as the subject area. Thus, the problem of effective training of miners for activities in regular and emergency situations is considered, in the latter case, VR technologies would allow forming the necessary set of skills and knowledge about actions in emergency situations. To confirm the declared hypotheses, an experimental group of 30 people was formed, corresponding to the trained miners by age characteristics. Based on the analysis, a list of quantitative and qualitative metrics for evaluating interaction with virtual reality was formed, the software of virtual scenes for two tasks (moving simple objects and a set of exercises in a virtual mine) was developed. The experimental group repeatedly performed these exercises, which allowed us to analyze the dynamics of changes in the average values of quantitative and qualitative metrics. The data obtained were processed by statistical tests (Shapiro–Wilk, Kruskal–Wallis, Mann–Whitney), which allowed us to assess the impact of the selected configurations (with and without VR) and the number of attempts on the selected metrics. The obtained results partially or completely confirmed the declared hypotheses and allowed us to form a list of recommendations for the organization of high-quality professional training using virtual reality technologies.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10055-022-00687-7.

Optimization of truck allocation in open pit mines using differential evolution algorithm

The allocation of trucks in open pit mines is a field with great potential for optimizing resources and applying advanced computer modeling techniques, mainly because many companies still choose to use manual allocation, which is premised on the decisions made by the operator, being subject to common failures and not reaching the maximum potential that the equipment can provide. Therefore, this work focuses on optimizing the allocation of trucks in order to increase production, reducing queue time and keeping ore grades within proper limits. The proposed algorithm was based on the differential evolution technique, where two types of mutation operators were used: rand/1/bin and best/1/bin, thus verifying the most suitable to solve the problem. The trucks were allocated in the ore loading and unloading process, aiming to improve the production capacity in a virtual mine. The results brought a convergence to the maximum global production, in addition to which, the allocation of unnecessary transport equipment to the planned routes was avoided. The two mutation operators compared had certain advantages and disadvantages, each better adapting to certain types of situations. The proposed technique can still be extended to other areas, for example, in the transport of grain on the road network or in the implementation of an allocation in freight cars that transport grain.

Development of Three-Dimensional Models of Mining Industry Objects

Visualization of objects using computer programs can greatly facilitate the external presentation of projects of coal industry enterprises. Modern computer graphics make it possible to embody very realistic models, and since the creation of 3D objects takes less time than their implementation. 3D technologies allow you to present the model from all angles and eliminate the shortcomings identified in the process of its creation. Successfully implemented tasks in this work include: creation of a 3D model of the selected RoadHeader; importing the developed model into the Unity 3D game engine; create and customize animations for the above harvester model inside Unity. The 3D model created in this work for visual display of the equipment used in the development of coal deposits in the mining industry contributes to the improvement of the process of training specialists and work planning when training personnel to work with drilling equipment at the training stage. The presented development can serve as a visual representation in the study of the principles of mining machines, as well as in the creation of 3D objects and scenes for the tutorial “Virtual mine”.

Numerical Simulation of Shearer Operation in a Longwall District

This paper presents a relatively simple method to analyze potential methane hazard and preventive methods based on a computer simulation of the airflow and methane emission on the longwall face and in the goaf. The presented approach considers the operation of a longwall shearer and conveyers and their possible impacts on both direct emissions of methane and migration from adjacent goafs. In this work, an attempt was made to control the advance speed of the virtual mining system based on sample mining data in the longwall 841A area and the abandoned longwall 841B at the Bielszowice Hard Coal Mine. The objective of this study was to verify the suitability of the adopted control algorithm. The results obtained from computer simulations of the mining operation with the developed control algorithm are presented in graphics of methane concentration, shearer advance speed and the speed control system parameters.

Cryptocurrency As An Alternative Currency In Malaysia: Issues and Challenges

Fintech (or financial technology) is the current driving force behind innovations in the financial services industry. One of the most debated innovations is cryptocurrency, or digital currency, which uses blockchain technology to make a direct electronic payment between two people possible, without going through a third party (like a bank) or expensive intermediaries in order to save costs. This future money is pressurising central banks to manage the looming threat of redundancy as it overshadows fiat currency in a world of infinite fintech possibilities. Bitcoin, being the first decentralised cryptocurrency, will be the focus of this research. This digital currency is not produced by minting money in an unlimited supply, but through a virtual mining process designed to control the supply of money and make it more valuable. The increasing pace in financial innovation is pushing regulators to make a change in the way they define money and what money can be. Traditionally money is used to serve as a medium of exchange, legal tender for repayment of debt, standard of value, unit of accounting measure and a means to save or store purchasing power. Bitcoin may not fulfill all the functions of money but its scarcity value, anonymity (or pseudonymity), transparency, and autonomy from the government, make it attractive to users who are speculators, traders, merchants, consumers and netizens disenchanted with fiat money. Despite the alluring features of Bitcoin, it is not spared from potential abuses such as webcrimes, tax evasion, fraud, online black markets, money laundering and terrorism financing. In this paper, a forensic examination of Bitcoins benefits and risks will help regulators decide whether to adopt cryptocurrency and provide an appropriate framework to regulate it based on other jurisdictions approach. This paper recommends that Malaysia should fully embrace cryptocurrency due to global trends - the Islamic Development Bank is developing Shariah compliant contracts using blockchain technology; China is leading the drive to develop its own national cryptocurrency to complement fiat money; and a Shariah-compliant cryptocurrency has already entered the market backed by gold (Onegram). Financial and regulatory architectures in Malaysia should accommodate these changes to remain relevant. In addition, future research is recommended focusing on developing a Shariah compliant national cryptocurrency that is unique to Malaysia.

Modeling of Vanished Historic Mining Landscape Features as a Part of Digital Cultural Heritage and Possibilities of Its Use in Mining Tourism (Case Study: Gelnica Town, Slovakia)

The study provides a methodology for 3D model processing of historic mining landscape, and its features as mining digital cultural heritage with the possibility of using new visualization means in mining tourism. Historic mining landscapes around the towns of Gelnica (eastern Slovakia) had been chosen for the case study. The underground mining spaces around Gelnica, which are currently inaccessible to clients of mining tourism, were processed using 3D modeling. Historically, correctly processed 3D models of mining spaces enable customers of mining tourism to virtually travel not only in space, but what is most important, in time as well. The up-to-date computer-generated virtual mining heritage in the form of 3D models can be viewed via the Internet from different perspectives and angles. The models created this way are currently the latest trend in developing mining tourism.

Development of an optimal draw control strategy for a sublevel caving operation at Malmberget mine

ABSTRACT This paper addresses the identification of the optimal draw control strategy for a sublevel caving (SLC) operation at Malmberget mine in Sweden. Two mathematical models, a probability model and an economic model, were created using five datasets: bucket weights, bucket grades, extraction ratio, mine economics parameters and production constraints. The probability model was used to generate a set of simulated bucket weights and corresponding bucket grades which acts as a ‘virtual mine’ environment. The economic model assesses the economic impact of loading at the draw point. Two approaches to draw control were tested using the ‘virtual mine’ created by the probability model. Based on the results of the simulation tests, an optimal draw control strategy is suggested for a field test at the mine. The new draw control strategy optimises further the loading operation at Malmberget mine. The paper shows a roadmap for optimising draw control strategy for SLC operations.

How Much Practice Is Required to Reduce Performance Variability in a Virtual Reality Mining Simulator?

Virtual reality allows researchers to explore training scenarios that are not feasible or are potentially risky to recreate in the real world. The aim of this research was to examine whether using a tutorial session prior to using the mining simulator could adequately reduce the performance variability and increase the consistency of participant performance metrics. Eighteen participants were randomly assigned to a tutorial or a non-tutorial group. The tutorial group completed a five-minute tutorial that introduced them to the basics of the machine and virtual reality environment. All participants then completed five sessions in the simulator lasting five minutes each. Personality scores were recorded and participants answered questions to test their situational awareness after each session. Performance metrics such as number of collisions and perception response time were recorded by the simulator. A Wilcoxon signed rank test was used to determine at what point a significant difference in performance metrics was apparent across the five sessions. A mixed effects multilevel regression was done to evaluate the change in variability across time. There were no significant correlations between the personality questionnaire scores and the number of collisions or the perception response time. Both groups demonstrated high standard deviation scores for collisions and perception response time, but the tutorial group had decreasing variability across time. Both groups began to exhibit more consistent scores in the simulator after 10 min of use. Situational awareness questions require some refinement prior to further testing.

Industry 4.0 in the mining industry: global trends and innovative development

The article shows that the implementation of Industry 4.0 will be implemented in many sectors of the global economy, including the coal industry. In Russia, “positive” experience has been accumulated in connection with the implementation of the Smart Mine and Smart Cut projects. The purpose of this article is to identify trends in the innovative development of the mining industry, as well as to systematize the basic elements of the Industry 4.0 project on basic mining processes. In the process of the study, the authors of the article prepared an enlarged systematization of technological solutions for the mining industry, which corresponds to the main directions of the global Industry-4.0 project and allows us to proceed with the formation of a technological platform that includes the need to implement the projects “Virtual Mine of the Future” and “Virtual Section of the Future”.

A dynamic information platform for underground coal mine safety based on internet of things

Mechanical and intelligent mining technology reduces labor intensity, accidents and casualties while improving mining efficiency in underground coal mines. Such advanced mining methods are gradually realized in China with increasing application of an Internet of Things (IoT) technique. In this study, a dynamic information platform is established for underground coal mine based on IoT technology. The platform aims to satisfy various objectives of potential users through combining four subsystems, including data acquisition, transmission, analyzation and application systems. To achieve such functions, six functional layers are composed of the platform, including the supporting layer, perception layer, transmission layer, service layer, data extraction layer and application layer. The platform can monitor and record working conditions data of the coal mine production systems as well as location information of underground equipment and miners. Based on cloud computing techniques, big data related to underground coal mining can be analyzed quickly, and critical data associated with user requirements can be extracted precisely. The platform provides a 3D virtual mine system, safety diagnosis system, safety inspection system and emergency rescue system for coal mines. With the established platform, the danger-spotting and subsequent decision-making capabilities of users can be greatly enhanced to ensure underground mining safety.

Bauxite orebody demarcating and virtual mining for mining optimization within an underground bauxite seam, Southwest China

Bauxite grade is an important property determining the ore reserve, mining value and mining technology parameters within an underground bauxite seam, which is a prominent difference between bauxite and coal mines. An orebody demarcating approach was proposed to determine the top and bottom boundaries of bauxite orebody, which consists of the following steps: one-dimensional inverse distance weighted interpolation for grade distribution along each sampling trench or borehole, delimiting for orebody boundary according to the industrial constraints of bauxite grade, and two-dimensional biharmonic spline interpolation for boundary surfaces. Then, the 88,983 m3 bauxite orebody having an average thickness of 1.4565 m was demarcated in the 1102 longwall panel. The application of the fully mechanized longwall mining in the bauxite seam in Wachangping bauxite mine has been inferred to be feasible from the following comprehensive considerations: occurrence properties of orebody, geomechanical parameters of ore-rock, geological and hydrogeological conditions and overburden stability. In addition, the optimal cutting height on each longwall slice ranging from 0.9773 to 2.4276 m was determined by the proposed virtual mining procedure. The designed 1.2138-m-diameter cutting drum can satisfy the industrial requirements for the low-waste and high-recovery mining of underground bauxite, in which the average ore loss rate and roof waste mixing rate are merely 0.2367 and 5.1355%, respectively.

Fully Mechanized Longwall Mining in an Underground Bauxite Deposit: A Case Study

Based on the boundary surfaces of bauxite deposit modelled by the two-dimensional biharmonic spline interpolation, it was ascertained that the fully mechanized longwall mining of bauxite is feasible in terms of lithological characteristics, orebed occurrence, physical–mechanical properties of ore–rock, faults, Karst caves, permeable fissures, etc. Furthermore, a comprehensive target function consisting of waste-rock interfused ratio and ore loss ratio was used to obtain the optimal cutting height of shearer by a virtual mining procedure that was proposed to numerically perform the fully mechanized longwall mining. For the 1102 longwall mining panel in Wachangping Bauxite Mine, the optimal cutting height of shearer ranges from 1.16 to 2.32 m supplied from two 1.16-m-diameter cutting drums, which meets the requirement of low-waste and high-recovery mining to achieve high-grade and low-loss exploitations of underground bauxite. These efforts can improve the application and popularization of fully mechanized longwall mining in non-coal underground mines.

Construction and Visualization of 3D Landscape

Abstract In order to know the operation condition before working, and reduce the risk of serious accidents of coal production industry. In this paper, by analyzing the characteristics of the environment of underground mine, using virtual reality technology, combined with the 3d Max modeling techniques and VC++ calling the OpenGL graphics interface techniques, the models are well optimized based on progressive mesh LOD algorithm for edge collapse. Using high-tech computer technology to simulate realistic mine production environment and realization of a friendly man-machine interactive virtual mine application system.

Building Computational Virtual Reality Environment for Anesthesia

Traditional anaesthesia training is considered as a time-consuming task since trainees are required to go through an extended period of knowledge learning and practice their skill in the supervision of experienced anaesthetists. In this paper, a Computational Virtual Reality Environment for Anesthesia (CVREA) is proposed, which can significantly improve the training and learning performance of trainee anaesthetists in an efficient way. Virtual reality, big data, data mining and machine learning techniques will be explored and applied in this system. CVREA consists of two main parts: (1) an immersive and interactive VR-based training platform for anaesthetists. It allows trainees to hone their clinical skills in a virtual environment without placing risk to patients. (2) a knowledge learning system which records and collects clinical data with greater richness. Knowledge learning algorithms will be developed to explore these data in order to help data processing and facilitates knowledge discovery in anaesthesiology.

The last developments of the digital economy and bitcoins as a ‘stress test’ for the EU VAT system

The BEPS project highlights the relevance of the challenges posed by the digital economy. The OECD notes that, because the digital economy is increasingly becoming the economy itself, it is not feasible to ringfence it from the rest of the economy for tax purposes. To a certain extent, this risk is inherent to the EU VAT system. In this article, the author uses virtual currencies as a ‘stress test’ for the actual EU VAT system, with the aim of pinpointing some of its weaknesses in respect of the digital economy. Having summarised the recent developments within EU and OECD context, the author proposes an analysis of what virtual currencies and mining activities ‘are’ under the current VAT, and how they could be treated. Finally, the author proposes and analyses a set of possible solutions. He goes from solutions based on the interpretation of current VAT legislation, which could be accepted by the CJEU in the pending case Hedqvist (C-264/14), to an innovative idea that would imply a radical change of the c...

VIRTUAL MINING MODEL FOR CLASSIFYING TEXT USING UNSUPERVISED LEARNING

In real world data mining is emerging in various era, one of its most outstanding performance is held in various research such as Big data, multimedia mining, text mining etc. Each of the researcher proves their contribution with tremendous improvements in their proposal by means of mathematical representation. Empowering each problem with solutions are classified into mathematical and implementation models. The mathematical model relates to the straight forward rules and formulas that are related to the problem definition of particular field of domain. Whereas the implementation model derives some sort of knowledge from the real time decision making behaviour such as artificial intelligence and swarm intelligence and has a complex set of rules compared with the mathematical model. The implementation model mines and derives knowledge model from the collection of dataset and attributes. This knowledge is applied to the concerned problem definition. The objective of our work is to efficiently mine knowledge from the unstructured text documents. In order to mine textual documents, text mining is applied. The text mining is the sub-domain in data mining. In text mining, the proposed Virtual Mining Model (VMM) is defined for effective text clustering. This VMM involves the learning of conceptual terms; these terms are grouped in Significant Term List (STL). VMM model is appropriate combination of layer 1 arch with Analysis of Bilateral Intelligence (ABI). The frequent update of conceptual terms in the STL is more important for effective clustering. The result is shown, Artifial neural network based unsupervised learning algorithm is used for learning texual pattern in the Virtual Mining Model. For learning of such terminologies, this paper proposed Artificial Neural Network based learning algorithm.