Review of Automated Operations in Drilling and Mining
Abstract
:1. Introduction
2. Problem Statement and Motivation
3. Overview of Drilling Approaches
3.1. Cable Tool Drilling
3.2. Auger Drilling
3.3. Rotary Drilling
3.4. Diamond Drilling
3.5. Directional Drilling
- Whipstock: A special geometry that allows the drill bit to slide off its surface. The geometry’s angle is what directs the drilling bit [8].
- Bent-house PDM: The motor’s housing is at an angle, allowing the motor to drill at a specific, predefined trajectory [8].
- Best sub: In this configuration, there are hubs that connect two different bent subs at different angles. It provides a greater degree of freedom when drilling [8].
4. Automated Mining and Intelligent Machines
4.1. Rotary Steerable Drilling Systems
- Push-the-bit systems: Push-the-bit systems have a pump that pushes actuators fixated on the system’s external housing. These pneumatic systems utilize the polar array of these actuators, normally pads, to push the bit to the desired direction [10], as can be seen in Figure 1. The most famous implementations are the Schlumberger PowerDrive and Baker-Hughes Autotrak systems, as in [9,11].
- Point-the-bit systems: These systems vary from the push-the-bit systems in that the actuators reside on the inside of the configuration [10,11]. The most well-explored example of this is the Halliburton Sperry-sun Geo-Pilot system [9]. In these cases, the control of the direction of the bit is possible by using two eccentric rings, changing its angle directly using motors, as shown in Figure 2.
- Hybrid systems: Hybrid systems are, in essence, a combination of the previous two systems. They have multiple actuators that assist them in better controlling every aspect of the drilling procedure. In the Schlumberger PowerDrive Archer system, the bit’s shaft uses a universal joint and internal actuators to push to orient it at the desired angle [9,11].
4.2. Longwall
4.3. Automated Hauling
4.4. Charging Robots
4.5. Intelligent Rock-Drilling Jumbo and DTH Drill
5. Technological Infrastructure in Automated Mining
5.1. Sensors Used in Automated Mining
5.1.1. LIBS
5.1.2. LIDAR
5.1.3. Time-of-Flight Camera
5.2. Communication and Localization Systems Used in Automated Mining
5.2.1. Light-Based Localization Systems
5.2.2. Daisy Chaining of Fiducial Markers
5.2.3. Magnetic Induction
5.2.4. WiFi and LoRaWAN
5.3. Simulations Using Digital Models
5.4. Software Used in Intelligent Systems
5.4.1. e-Drilling’s Software Suite
5.4.2. Draco
5.4.3. Forestall
5.4.4. TIMining Aware
5.5. Collaborative Robotics in Mining
6. Possible Evolution of Intelligent Mining Machines
- Drill piston control for better bit-pointing accuracy: This approach promises better results when it comes to pointing the bit of an RSDS. It uses multiple pistons, valves, water, and stabilization clamp pads to better control the orientation and force applied by the bit [55].
- Photogrammetry in the mining industry: While Virtual Reality technology is being used today for minor tasks, it is being researched and worked upon extensively due to the presently unexplored advantages described in [56,57], especially with the improvement in modern cameras. Mining professionals are already adopting technologies from research, especially within the area of the digitization and mapping of mines with high-resolution cameras. Additionally, large-area scans for open-pit sites, which once required the use of special fiducial markers (ground control points) [58], are no longer needed with the introduction of Real-Time Kinematics and Post-Process Kinematics technologies [59]. Furthermore, there is ongoing research on multi-camera systems that can produce rock-mass digital models with a camera rig and can also support UAVs [25].
- Neural networks for augmented personnel safety: Neural networks can be used for a diversity of tasks. Research has shown that one of their uses can be the monitoring of the activity of personnel inside the mine to decrease accidents and exposure to hazardous areas [60]. One such example is the use of a YOLOv8 model (Figure 8) to determine the location of humans and hazardous areas [61]. YOLOv8 is a convolutional neural network that makes predictions of bounding boxes and class probabilities all at once.
- Virtual Reality simulation for multi-agent system action planning: Virtual Reality’s importance is already underlined, but the range of its applications cannot be stressed enough. Currently, there are ready-to-use software solutions that allow for the simulation of multiple robots in a virtual mining environment, expressed through the game development engine Unity3D [62]. Simulators of this kind may prove essential in mining procedure planning, especially in more complex systems with multiple interactions [57].
- AI-assisted DTs: Advancements that are currently being made in the field of artificial intelligence suggest that AI will be further integrated into FEA simulation software to provide better insights regarding mining data [63,64]. Surveys report that AI will be refined enough to be widely accepted in the industry by 2030 [65], providing a better bridge of communication between the real and digital worlds.
- Optimized haulage scheduling using Reinforcement Learning: Reinforcement Learning (RL) methods can be used to schedule the operation of fleets of autonomous haulers in real time utilizing model-free Q-Learning algorithms. In a simulated environment, this method resulted in a reduction in the wait time of each hauler and reduced the emission of greenhouse gases that resulted from the extensive fuel consumption of the machines. This reduction in fuel consumption was as high as 30% in comparison to fixed-schedule approaches while increasing productivity by 50% [66].
- Controlling equipment using Deep Reinforcement Learning: Deep Reinforcement Learning (DRL) can be used to control underground, individually operating machines as a substitute for human operators. Machines such as Load-Haul-Dump machines can be controlled with the use of a DRL framework by implementing a soft actor-critic (SAC) algorithm in a simulated environment, lowering energy consumption and increasing productivity [68]. In more detail, in [68], the energy consumption was found to be 21% lower for the autonomous vehicle in comparison with the energy consumed when a similar vehicle was operated by a human in the real world, and productivity was increased by 7%. Another example of operating multiple machines using RL, implementing proximal policy optimization (PPO) and SAC, can be examined in [67].
- Using DRL in mine production scheduling: DRL methods can even be utilized for the design of underground mine production layouts [69]. Utilizing DRL in the pre-production stages of the mining process allows for a better evaluation of the mineral grade since it utilizes uncertainty to create a stochastic design in comparison to deterministic designs. Deterministic designs are created by the more conventional geostatistical methods, such as mineable shape optimizers, which tend to lead to the under-performance of mining projects. A case study of underground stoping for gold production in [69] resulted in 8.3% higher expected profit and 3.4% more gold mined than the baseline that was provided by a mineable shape optimizer.
- Monitoring of the drilling rig: Research on monitoring drilling bit wear has been conducted to predict and analyze the drilling bit’s condition. These studies have shown that sound and vibration signals, as well as examining the geological and mechanical properties of rocks, can be used to survey the drilling bit’s wear condition [72].
- Intelligent control of the drilling: Computer vision and Deep Neural Networks for image classification are used to control the drilling rig and improve procedures such as drilling path planning and intelligent hole positioning [72].
- Measurement while drilling (MWD) and machine learning for rock monitoring: MWD is a technology that provides real-time measurements of drilling parameters, e.g., torque, rotation speed, and drilling speed. These parameters are used to determine the magnitude of the rock stress caused by the drilling operation with the aid of digital panoramic imaging technology for boreholes. Additionally, intelligent rock property sensing can be achieved by implementing machine learning techniques with the data provided by MWD [72].
- AI and big data technology in ventilation systems: Current research on ventilation systems focuses on acquiring ventilation parameters, such as wind volume and pressure, temperature, humidity, and the detection/concentration of toxic gases. To achieve this, research focuses on the proper placement of the minimum number of sensors that provide reliable data about those parameters. After obtaining these data, diagnostic methods are proposed based on learning algorithms and big data analysis, as well as airflow control methods [72].
- Intelligent rock stress monitoring: Rock stress during drilling operations causes the deformation of the surrounding rock. Therefore, real-time monitoring methods have been developed based on machine vision and learning algorithms that provide early warning and predictions about surrounding rock deformation and damage [72].
7. Future Trends and Visions in the Robotization and Digitalization of the Mining Industry
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
3D | Three-dimensional |
5G | Fifth-generation mobile network |
AHS | Autonomous Haulage System |
AI | Artificial intelligence |
DRL | Deep Reinforcement Learning |
DT | Digital twin |
DTH | Down the hole |
FEA | Finite Element Analysis |
GPS | Global Positioning System |
IoT | Internet of Things |
LED | Light-Emitting Diode |
LIBS | Laser-Induced Breakdown Spectroscopy |
LiDAR | Light Detection and Ranging |
LoRaWAN | Long-Range Wide-Area Network |
MWD | Measurement while drilling |
PPO | Proximal policy optimization |
RF | Radio Frequency |
RL | Reinforcement Learning |
RSDS | Rotary Steerable Drilling System |
SAC | Soft actor-critic |
SLAM | Simultaneous Localization and Mapping |
TFC | Time-of-flight camera |
TFS | Time-of-flight sensor |
UAV | Unmanned Aerial Vehicle |
UGV | Unmanned Ground Vehicle |
UHF | Ultra-High Frequency |
YOLO | You Only Look Once |
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Type | Topic | References |
---|---|---|
Drilling approaches | Cable tool drilling | [5,6] |
Auger drilling | [5,6,7] | |
Rotary drilling | [5,6] | |
Diamond drilling | [5,6] | |
Directional drilling | [8] | |
Automated mining intelligent machines | Rotary Steerable Drilling Systems | [9,10,11] |
Longwall | [12,13,14,15] | |
Automated hauling | [16] | |
Charging robots | [16,17] | |
Intelligent Rock-Drilling Jumbo and DTH Drill | [16] | |
Sensors in automated mining | LIBS | [18,19,20] |
LIDAR | [21,22,23,24,25] | |
TFS | [26,27] | |
Localization systems | Light-based localization systems | [16,28,29] |
Daisy chaining of fiducial markers | [30,31] | |
Magnetic induction | [21,32,33] | |
Communication systems | WiFi | [21,24] |
LoRaWAN | [21,34,35] | |
Digital model | DT | [36,37] |
Software in intelligent systems | e-Drilling’s software suite | [38,39] |
Draco | [40,41,42] | |
Forestall | [43,44] | |
TIMining Aware | [44,45] | |
Collaborative robotics | Caterpillar’s MineStar Command for Underground Systems (AHS) | [46,47,48,49] |
EH-RemoteHeadControl v2 by Elgór-Hanses S.A. | [15] | |
UGVs | [50] | |
UAVs | [50,51,52,53,54] | |
Research about intelligent mining machines | Drill piston control | [55] |
Photogrammetry | [25,56,57,58,59] | |
Neural networks | [60,61] | |
VR simulation for multi-agent system action planning | [57,62] | |
Research about intelligent mining machines | AI-assisted DTs | [63,64,65] |
Optimized haulage scheduling using RL | [66] | |
Controlling equipment using DRL | [67,68] | |
Using DRL in mine production scheduling | [69] | |
Development and research for environmental monitoring | [70,71] | |
Monitoring of the drilling rig | [72] | |
Intelligent control of drilling | [72] | |
MWD and machine learning for rock monitoring | [72] | |
AI and big data technology in ventilation systems | [72] | |
Intelligent rock stress monitoring | [72] | |
Quantum computing for open-pit profile optimization | [73,74,75] |
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Share and Cite
Kokkinis, A.; Frantzis, T.; Skordis, K.; Nikolakopoulos, G.; Koustoumpardis, P. Review of Automated Operations in Drilling and Mining. Machines 2024, 12, 845. https://doi.org/10.3390/machines12120845
Kokkinis A, Frantzis T, Skordis K, Nikolakopoulos G, Koustoumpardis P. Review of Automated Operations in Drilling and Mining. Machines. 2024; 12(12):845. https://doi.org/10.3390/machines12120845
Chicago/Turabian StyleKokkinis, Athanasios, Theodore Frantzis, Konstantinos Skordis, George Nikolakopoulos, and Panagiotis Koustoumpardis. 2024. "Review of Automated Operations in Drilling and Mining" Machines 12, no. 12: 845. https://doi.org/10.3390/machines12120845
APA StyleKokkinis, A., Frantzis, T., Skordis, K., Nikolakopoulos, G., & Koustoumpardis, P. (2024). Review of Automated Operations in Drilling and Mining. Machines, 12(12), 845. https://doi.org/10.3390/machines12120845