Underground Mining Vehicle Research Articles

Battery electric vehicles in underground mines: Insights from industry

The implementation of battery electric vehicles (BEVs) in underground mining is relatively recent. BEVs offer several advantages over diesel machines, including enhanced working conditions through reduced noise and heat and the lack of toxic exhaust gases or diesel particulate matter. In addition to reducing greenhouse gases, they have the potential to reduce ventilation and air conditioning costs. Nevertheless, there are certain concerns about BEVs, in areas of productivity, fire safety, economic viability, user-friendliness, and potential electrical-related issues. To address these, two surveys were conducted, one among underground mine management and the other among mine personnel to ascertain their opinions and experiences of BEVs. The results indicated the primary motivators for mines to adopt BEVs were to create a healthier working environment and reduce carbon emissions. The factors hindering the implementation were high costs and the lack of proven reliability. Mine personnel appreciated BEVs for their quietness and reduced fluids and components; however, they had concerns about fire safety and limited battery duration. This study presents the extent of BEV use in underground mining and associated fire incidents and a summary of the survey results.

Autonomous Positioning for Mobile Vehicles Based on Visual‐Inertial Fusion in Challenging Dark Roadway Scenes

ABSTRACTAccurate positioning for autonomous driven underground mining vehicles (UMVs) and coal mine robots (CMRs) is indeed one of the cores in the intelligentization of coal mining. Completely different from positioning on the ground and in parking scenes, there will be great difficulties in realizing the accurate active positioning for UMVs shuttled in dark and narrow roadways. We propose an effective visual and inertial fusion positioning method for autonomous CMRs and roadheaders in challenging roadway scenarios based on the odometer‐aided inertial navigation system and visual pose estimation system. Velocity information of the odometer is adapted to restrain the error accumulation of inertial positioning based on a Kalman filter. The hybrid visual feature detection algorithm is put forward to improve the accuracy and robustness of visual observation information in a dark environment. Autonomous experiments for CMRs and roadheaders are separately performed in the narrow roadway and dark passageway to demonstrate the applicability of our localization method. The proposed approach outperforms the subsystems and existing methods in accuracy and has outstanding stability.

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at ±0.109 m, which basically meets the path planning needs of underground mine vehicles.

Field Trials and Fire Safety of Battery Electric Vehicles in Underground Mines: A current snapshot of the advantages and challenges of the use of battery electric vehicles in underground mines

Field Trials and Fire Safety of Battery Electric Vehicles in Underground Mines: A current snapshot of the advantages and challenges of the use of battery electric vehicles in underground mines

Design Optimization of Underground Mining Vehicles Based on Regenerative Braking Energy Recovery

This article addresses the issue of energy waste resulting from frequent braking of underground mine cars and proposes an optimization design to address this. The proposed solution involves the installation of a regenerative braking device within the mine cars to capture and reuse the energy wasted during braking. This implementation improves the endurance capabilities of the underground mine cars. The article begins by analyzing the working characteristics of underground mine cars and proposing a design optimization method based on regenerative braking energy. Subsequently, a regenerative braking device specifically designed for underground mine cars is introduced. Finally, through physical modeling, a comparison is made between the energy consumption of the underground mine cars before and after the installation of the energy recovery system, allowing for an estimation of the actual benefits of energy recovery. The results demonstrate that the regenerative braking system successfully recovers approximately 60% of the braking energy during operation, resulting in an improvement of around 20% in the endurance capabilities of the underground mine cars. This significant enhancement contributes to the improved energy utilization efficiency of coal mine electric cars, reducing system energy consumption and lowering CO2 emissions.

Research on a Speed Guidance Strategy for Mine Vehicles in Three-Fork Roadways Based on Vehicle–Road Coordination

Aiming at the traffic congestion problem of mining vehicles in the intersection area of three-fork roadways in coal mines, this paper proposes a speed guidance strategy based on a vehicle–road cooperative environment to adjust the running state of mining vehicles in the three-fork roadway system. The proposed speed guidance strategy can realize the safe and effective passage of underground mining vehicles to the greatest extent. Taking a three-fork roadway in a coal mine as an actual case, the operation of mining vehicles in the three-fork roadway is optimized and scheduled. Through the joint simulation of PTV VISSIM 4.3 traffic simulation software and PYTHON 3.7, the travel time, number of queuing vehicles, and delay time of mining vehicles passing through the three-fork roadway entrance under the two conditions of no speed and speed guidance in the coal mine are simulated and compared. The results show that after using the proposed speed guidance strategy for scheduling, mining vehicles can quickly pass through the three-fork roadway intersection. The travel time is reduced by 18.4%, the number of queuing vehicles is reduced by 41.5%, and the delay time is reduced by 24.1%, which effectively improves the transportation efficiency of underground mining vehicles.

Robust Localization for Underground Mining Vehicles: An Application in a Room and Pillar Mine.

Most autonomous navigation systems used in underground mining vehicles such as load-haul-dump (LHD) vehicles and trucks use 2D light detection and ranging (LIDAR) sensors and 2D representations/maps of the environment. In this article, we propose the use of 3D LIDARs and existing 3D simultaneous localization and mapping (SLAM) jointly with 2D mapping methods to produce or update 2D grid maps of underground tunnels that may have significant elevation changes. Existing mapping methods that only use 2D LIDARs are shown to fail to produce accurate 2D grid maps of the environment. These maps can be used for robust localization and navigation in different mine types (e.g., sublevel stoping, block/panel caving, room and pillar), using only 2D LIDAR sensors. The proposed methodology was tested in the Werra Potash Mine located at Philippsthal, Germany, under real operational conditions. The obtained results show that the enhanced 2D map-building method produces a superior mapping performance compared with a 2D map generated without the use of the 3D LIDAR-based mapping solution. The 2D map generated enables robust 2D localization, which was tested during the operation of an autonomous LHD, performing autonomous navigation and autonomous loading over extended periods of time.

Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach

An approach based on an artificial neural network (ANN) for the prediction of NOx emissions from underground load–haul–dumping (LHD) vehicles powered by diesel engines is proposed. A Feed-Forward Neural Network, the Multi-Layer Perceptron (MLP), is used to establish a nonlinear relationship between input and output layers. The predicted values of NOx emissions have less than 15% error compared to the real values measured by the LHD onboard monitoring system by the standard sensor. This is considered quite good efficiency for dynamic behaviour prediction of extremely complex systems. The achieved accuracy of NOx prediction allows the application of the ANN-based “soft sensor” in environmental impact estimation and ventilation system demand planning, which depends on the number of working LHDs in the underground mine. The proposed solution to model NOx concentrations from mining machines will help to provide a better understanding of the atmosphere of the working environment and will also contribute to improving the safety of underground crews.

Explosion characteristic of CH4–H2-Air mixtures vented by encapsulated large-scale Li-ion battery under thermal runaway

Explosion-proof diesel-powered vehicles are the main auxiliary transport vehicles in underground coal mines, although many of these have been replaced by battery electric vehicles (BEVs) nowadays. The explosion-proof structure and performance of the power supply of the BEVs should meet the IEC 60079 standard, however, and the explosion-proof cavity where the batteries are placed should be able to withstand a static pressure of not less than 1.5 MPa. In this paper, a nail penetration experiment is carried out on an encapsulated lithium-ion battery (LIB) pack under an atmosphere consisting of air, 9.5% methane, and 12.5% mixed combustible gas, and the temperature and the pressure data of the thermal runaway LIBs in the explosion-proof tank are comprehensively analysed. Moreover, the ignition explosion test of the gas released by thermal runaway of LIBs in different environments is conducted. Finally, the explosion process is simulated by the chemical reaction kinetics model in Ansys Fluent. The experimental and simulation results can guide the structural design of the explosion-proof tank of the power supply in extreme working environments, and provide an important reference for explosion-proof protection design and overall integrated safety design of LIB power supplies for explosive special environments.

Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine

The underground mining industry is at the forefront when it comes to unsafe conditions at workplaces. As mining depths continue to increase and the mining fronts move away from the ventilation shafts, gas hazards are increasing. In this article, the authors developed a statistical polynomial model for nitrogen oxide (NOx) emission prediction of the LHD vehicle with a diesel engine. The best-achieved prediction accuracy by the 4th order polynomial model for 11 and 10 input variables is about 8% and 13%, respectively. It is comparable with the sensors’ accuracy of 10% at a stable regime of loading and 20% in the transient periods of operation. The obtained results allow planning of ventilation system capacity and power demand for the large fleet of vehicles in the deep underground mines.

Modeling and Simulation of Unmanned Driving System for Load Haul Dump Vehicles in Underground Mines

This paper proposes the modeling and simulation of the unmanned driving system for underground load haul dump vehicles based on Gazebo/Ros. Firstly, the kinematics model of the load haul dump vehicle is derived. Then, the model of each part of the load haul dump vehicle is established based on SolidWorks and the model of the load haul dump vehicle is established by connecting the parts through a unified robot description format (URDF) file. Finally, the laneway model is established by using alpha shape to realize the modeling of the operating environment of the load haul dump vehicle. The speed, angular speed, bucket lifting, and bucket flipping of the load haul dump vehicle are controlled using PID. The experimental results show that: The control errors of the speed and angular speed of the load haul dump vehicle are 0.283 m/s and 0.010 rad/s, respectively. The control error of the lifting bucket is 0.025 m and that of the flipping bucket is 0.015 m. The angular velocity control error of the simulation system relative to the actual system is 0.330 and 0.106 m/s, respectively. The error between the SLAM of the simulation system and the actual system and the measured value is 0.917 and 3.44 m, respectively. The control performance of the load haul dump vehicle in the simulation system is good. Therefore, automatic driving algorithms can be studied and tested in this simulation platform.

Analysis of diffusion behavior of harmful emissions from trackless rubber-wheel diesel vehicles in underground coal mines

To define the diffusion behavior of harmful exhaust substances from diesel vehicles and support safety risk assessments of underground coal mines, we performed a multi-species coupling calculation of the emission and diffusion of harmful substances from a trackless rubber-wheel diesel vehicle. A computational fluid dynamics (CFD) model of the diffusion of harmful emissions was hence established and verified. From the perspective of risk analysis, the diffusion behavior and distribution of hazardous substances emitted by the diesel vehicle were studied under 4 different conditions; moreover, we identified areas characterized by hazardous levels of emissions. When the vehicle idled upwind in the roadway, high-risk areas formed behind and to the right of the vehicle: particularly high concentrations of pollutants were measured near the rear floor of the vehicle and within 5 m behind the vehicle. When the vehicle idled downwind, high-risk areas formed in front of it: particularly high concentrations of pollutants were measured near the floor and within 5 m from the front of the vehicle. In the above cases, the driver would not breathe highly polluted air and would be relatively safe. When the vehicle idled into the chamber, however, high-risk areas formed on both sides of the vehicle and near the upper roof. Forward entry of the vehicle caused a greater increase in the concentration of pollutants in the chamber and in the driver’s breathing zone compared with reverse entry.

Concept and Architecture for Applying Continuous Machine Learning in Multi-Access Routing at Underground Mining Vehicles

Autonomous moving vehicles facilitate mining of ore in underground mines. The vehicles are usually equipped with many sensor-based devices (e.g., Lidar, video camera, proximity sensor, etc.), which enable environmental monitoring, and remote control of the vehicles at the control center. Transfer of sensor-based data from the vehicles towards the control center is challenging due to limited connectivity enabled by the multi-access technologies of the communication infrastructure (e.g., 5G, Wi-Fi) within the underground mine, and the mobility of the vehicles. This paper presents design, development, and evaluation of a concept and architecture enabling continuous machine learning (ML) for optimizing route selection of real-time streaming data in a real and emulated underground mining environment. Continuous ML refers to training and inference based on the most recently available data. Experiments in the emulator indicated that utilization of a ML-based model (based on the RandomForestRegressor) in decision making achieved ~5–13% lower one-way delay in streaming data transfers, when compared to a simpler heuristic model.

Modeling and Simulation of Unmanned Driving System for Load Haul Dump Vehicles in Underground Mines

Modeling and Simulation of Unmanned Driving System for Load Haul Dump Vehicles in Underground Mines

Dynamics of glass windows in mining vehicles under the impacts of rock pieces: numerical and analytical comparison for computational models validation

This study represents the results of linear dynamics analysis of glass plates subjected to rock pieces impacts occurring in underground machines’ windows. The aim of the work is to provide analytical and numerical solutions, obtain frequencies and plate displacement, and compare results of stress calculation for different models. The work performs finite element method (FEM) computations within a modal analysis in 3D statement including a mesh-size convergence analysis. Given approach is a basement for estimation of safety work conditions for operators in cabins of underground mining vehicles when glass windows are subjected to rock bursts and damages.

Improvement of Working Conditions and Opinions of Mine Workers When Battery Electric Vehicles (BEVs) Are Used Instead of Diesel Machines \u2014 Results of Field Trial at the Kittil\xe4 Mine, Finland

A major part of the European Union’s (EU) project Sustainable Intelligent Mining System (SIMS) is investigating the development of diesel-free/carbon–neutral underground mines in order to ensure sustainable underground mining in the future. Replacing diesel machines with electric vehicles in underground hard rock mines has been widely acknowledged by the mining industry worldwide as a critical step to improve working conditions by reducing diesel exhaust–related contaminants, to reduce mine ventilation electrical power cost by reducing mine airflow quantity, and to reduce mine greenhouse gas emissions. All of these are major requirements to achieve sustainable future underground mining practices. A field trial of Epiroc’s 2nd generation of Battery Electric Vehicles (BEVs) at Agnico Eagle Finland’s Kittilä mine was conducted during 2019–2020. Vehicles tested were MT42 mine truck, ST14 Load-Haul-Dump (LHD), and Boomer E2 jumbo drill rig. This paper outlines the improvement of the working conditions observed in the field trial, and the opinions of the mine personnel at Kittilä mine on using BEVs instead of diesel machines. Measurements of atmospheric contaminants and air temperatures taken during the field trial clearly demonstrated a significant improvement of working conditions when BEVs were operating as opposed to diesel machines. This field observation was supported by the opinion of the majority of the Kittilä mine workers. However, some remaining concerns must be addressed before BEVs can replace diesel machines.

Recognition of LHD Position and Maneuvers in Underground Mining Excavations—Identification and Parametrization of Turns

The Inertial Measurement Unit (IMU) is widely used in the monitoring of mining assets. A good example is the Polish underground copper ore mines of KGHM, where research work with the use of the IMU has been carried out for several years. The potential of inertial sensors was ensured by the development of advanced analytics using machine learning methods to support the maintenance management of an extensive machine park and machine manufacturer in adapting various construction elements to mining conditions. The key algorithms developed in the field of inertial data concern: identification of cycles and components of the haulage process operations, identification of dynamic overloads, technical diagnostics of rotating elements, assessment of road conditions (bumps, slopes, damages), assessment of the technical condition of the pavement, assessment of the operator’s driving style, and finally the machine location in the mining excavation. One of the key operational contexts, necessary in the development of analytics for underground mining vehicles, is the identification of the turning moment of the machine at the intersection together with the determination of the driving direction and the turn angle. In the case of a mine with a room-and-pillar system, where the excavation system has the Manhattan structure, it is possible to use many simplifications to correctly estimate the machine motion path. The identification of the spatial context and the turning maneuver is of key importance both in the development of the machine location system, but also in multi-dimensional analyzes, including the analysis of dynamic overloads or the assessment of the operator’s driving style and work safety. The article presents a comparison of several mathematical models used for the machine turn detection problem, which were trained and tested on the real-life industrial data recorded using IMU during a single working shift of the self-propelled machine.

Concentrations and Size Distributions of Particle Lung-deposited Surface Area (LDSA) in an Underground Mine

ABSTRACT Ultrafine particles produced by diesel-powered vehicles in underground mines are largely unaccounted for in mass-based air quality metrics. The Lung Deposited Surface Area concentration (LDSA) is an alternative to describe the harmfulness of particles. We aim to study concentrations and size distributions of LDSA at various locations in an underground mine as well as to evaluate the applicability of sensor-type measurement of LDSA. This study was conducted in an underground mine in Kemi, Finland, in 2017. Our main instrument was an electrical low-pressure impactor (ELPI+) inside a mobile laboratory. Additionally, five diffusion-charging based sensors were tested. The environment was challenging for the sensors as the particle size distribution was often outside the optimum range (20–300 nm) and dust accumulated inside the instruments. Despite this, the correlations with the ELPI+ were decent (R2 from 0.53 to 0.59). With the ELPI+ we determined that the maintenance area had the lowest mean LDSA concentration (79 ± 38 µm2 cm–3) of the measured locations. At the other locations, concentrations ranged from 137 to 405 µm2 cm–3. The mode particle size for the LDSA distribution was around 100 nm at most locations, with the blasting site as a notable exception (mode size closer to 700 nm). Diffusion-charging based sensors—perhaps aided by optical sensors—are potential solutions for long-term monitoring of LDSA if dust accumulation is taken care of. Our research indicates worker exposure could be reduced with the implementation of a sensor network to show which locations need either protective gear or increased ventilation.

The Passive Fire Protection of Mining Vehicles in Underground Hard Rock Mines

Vehicle fires in underground mines pose a certain risk for which measures to prevent or mitigate the fires are highly important. This paper presents a study on passive fire protection measures to counter this risk, where data from full-scale fire experiments, cone calorimeter tests and statistical studies were applied. It addressed an overall question: what passive fire protection measures could be implemented with respect to the design of mining vehicles? Relying entirely on one type of passive measure may only be adequate for a specific stage of a fire and additional measures may be warranted for the other stages. Threshold distances for fuel components were calculated, and it was found that the combination of hydraulic hoses and electrical cables resulted in larger threshold distances, contributed to by the higher flame heights of the hydraulic hoses and the lower critical heat flux of the non-fire-resistant electrical cables. A pool fire along the underside of the vehicle will pose a certain risk, and passive fire protection measures should be focused at ignition prevention, making high demands on the insulation/shielding along the underside. Tyre fires could be prevented by steering the flow of flames and fire gases away from the tyres. An increased knowledge of such possible passive fire protection measures would improve the fire safety in underground mines.

Velocity-Free Localization of Autonomous Driverless Vehicles in Underground Intelligent Mines

The rapid progress of science and technology has created favorable conditions for the development of autonomous driverless vehicles. However, the complex conditions in the cities greatly limit the application and promotion of autonomous driving technology. To solve the problem, the underground mine is recommended to apply as a pilot for the promotion of autonomous driverless vehicles. The autonomous vehicles are all connected to the internet to form a vehicle-to-vehicle (V2V) system. Because of the multi-levels and multi-stopes in underground mines, the key point is to obtain the precise locations of the vehicles in real-time. Therefore, the microseismic monitoring, image recognition technology, artificial intelligence training, and smart sensors are comprehensively utilized, based on the internet of things and cloud computing, to locate the autonomous driverless vehicles. Virtual sources localization and pencil lead break tests are conducted to simulate and eliminate the effectiveness and accuracy of the velocity-free localization method. Virtual sources localization demonstrate that the velocity-free localization method is accurate and reliable. Results of pencil lead break tests show that the average locating errors of X coordinates, Y coordinates, and absolute distance are 0.4318 cm, 0.1136 cm, and 0.5188 cm, respectively. The application and promotion of the autonomous driverless vehicles in underground mines can not only solve the problems of deep mining and reduce the frequent disasters caused by the harsh conditions but also can protect the life and property of the workers, as well as provide technical support for the safe and efficient recycling of deep resources.