Digitalization and automation have revolutionized all major industries, including the mining sector, in the past few years. With automated tools, data analytics, and the evolution of sustainable practices, significant improvements have been recorded in miners' safety, operational productivity, and relationships with local communities.
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A Brief Overview of Major Innovations in Mining Automation
The use of automated tools and efficient systems ranges from autonomous trucks, robot-based drilling systems, and automated trains to Internet of Things (IoT)-based sensing systems, data analysis platforms, predictive analytics platforms, and intelligent logistics and supply-chain systems.
The efficient human-robot-based modern collaborative systems have boosted the intelligent automation tools and smart mining systems domains and reduced the maintenance time, hazards, and mining accidents.1
Automated Haulage Trucks/Haulage Systems: Key to Digitalization
Mining 5.0, based on the key principles of the Fifth Industrial Revolution, requires the application of autonomous haulage systems (AHS) and transportation systems in open-pit mines.
Among the leading companies, Hitachi, Waytous, and Baidu Apollo are at the forefront of modern digitalized haulage systems.
Hitachi: World leader in AHS
Hitachi, formed in 1910, has become a key company focusing on smart mining operations, intelligent safety systems, and smart equipment.
Smart mining operations involve using tools with digitalized sub-systems, such as modern sensors, and analytics platforms for efficient decision-making.
Hitachi primarily focuses on remotely operated mining instruments, while intelligent safety systems have been developed to shield miners from collisions and equipment malfunctions.2
A brief overview of the Hitachi AHS
Hitachi AHS is suitable for large mining projects and increases the efficiency of small-scale projects.
The company has successfully integrated the Global Navigation Satellite System (GNSS) into its AHS, where the trucks and transport vehicles are controlled using a centralized digital control system and wireless communication systems.3
These smart mining dump trucks do not move in case of any abnormality. However, much progress is still required to impart intelligent decision-making capability to them without human intervention. For effective communication between the operator and the trucks, a strong internet connection is required.
The AHS uses permission control technology which allows autonomous trucks to carry on their own prescribed routes without getting in each other’s path. The path for the mining trucks is segmented into small patches, and the smart system allows only one truck to travel on the patch.
When the truck has traveled the length of the permitted section, the central control system receives a request from the truck’s system to move on to the next section. The truck is only allowed to move onto the next patch when the control system allows it.
The modern AHS system by Hitachi has been tested by Stanwell Corp., an Australian company where it efficiently performed mining operations.4
Novel Technologies for Augmenting Modern AHS
Other recent modern AHSs are equipped with lidar systems, infrared and thermal cameras, and novel 3D millimeter-wave radar. The lidar systems and cameras are used for static and moving objects, while the 3D mm-wave radars provide information regarding velocity and accurate x-y positioning of dynamic objects.
The latest 4G mm-wave radar technology, integrating multiple inputs and multiple outputs (MIMO), has also been experimented with. This technology provides additional information, including the elevation level of objects, and, owing to advanced electromagnetic scattering attributes, enables the detection of blocked static objects. These modern AHSs with novel wave radar technology work seamlessly in foggy conditions and detect parked haulages in advance.5
With advancements in modern technology, we can expect autonomous trucks to become more intelligent, enabling rapid detection of obstacles and acting promptly based on the changes in localized mining environments.
High-precision positioning systems and advanced 3D detection algorithms make automatic trucks much more intelligent, improving productivity and enabling robot-based operations.6
Automated Inspection and Monitoring Units: Enabling Safe Mining Operations
Data-driven mining operations integrated with Artificial Intelligence (AI) have transformed the recent landscape of monitoring and inspection units utilized for mining operations.
Autonomous robots enabling surveying, mapping, and remote monitoring in mining projects are being extensively researched. A prime example is Micro Aerial Vehicles (MAVs) equipped with hyperspectral imaging systems, which efficiently access and monitor parameters at inaccessible locations at mining sites.
Novel Virtual Reality and Digital Twin Technology Monitoring System
Researchers have used virtual reality (VR) and digital twin (DT) technology to efficiently monitor critical systems and mining equipment. This modern system is fast and overcomes several limitations of traditional visual inspection-based systems.
Virtual reality (VR) and digital twin (DT) technologies facilitate remote communication and the precise analysis of monitoring parameters. The process involves defining monitoring conditions by pre-defining normal values of variables, such as temperature, noise, and speed, which are tracked for fluctuations. These conditions are tailored to meet the needs of both on-site and off-site maintenance engineers, while the DT expert manages the technical execution.
A case study has demonstrated the effectiveness of this framework in remotely monitoring the condition of a conveyor belt, highlighting its potential for innovative and efficient maintenance solutions.
The system efficiently monitored vibrations within the normal range of 80-110 Hz, sending warnings and errors as soon as the parameters varied.7 This proves that digital technologies like VR are useful in enabling remote monitoring of mining parameters to ensure safety and improve working conditions.
TireSight: The Most Popular Autonomous Inspection Platform
Tire problems and out-of-service trucks are common problems in mining vehicles.
Kal Tire has successfully utilized AI to develop TireSight, which integrates modern imaging systems with digital technology, significantly boosting the quality and leading to massive improvements in the frequency of mining truck tire inspection.
The system uses Kal Tire’s innovative Tire & Operations Management System (TOMS), operated by a team of experts that provides solutions and suggestions based on the particular type of mining activity.
What makes AI-driven TireSigh the best?
Specialized thermal imaging cameras are strategically positioned in the system to provide the best angles and enable precise monitoring and inspection. Unlike manual inspections, which cover only about half of a stationary tire due to its size, the TireSight system captures three to five full rotations of each tire as the truck passes by. This setup ensures that every part of the tire is thoroughly inspected, providing comprehensive monitoring and improving maintenance accuracy.
The AI system monitors the values by reviewing the thermal imaging scans of tires to identify hot spots and tread damage. TOMS is the central insight hub for TireSight, processing detected anomalies.
A team of experts reviews alerts and validates the anomalies. It also oversees automated work orders generated by TOMS to ensure timely tire maintenance is conducted.8
This efficient autonomous inspection system ensures that technicians do not waste their time inspecting each tire; instead, the defective tires are passed on to the maintenance and repair teams, ensuring the safety of vehicles and people working in critical locations at different mines. This automated system enables a 20% reduction in inspection time, significantly boosting uptime and productivity.
How is AI Promoting Automation and Sustainability in the Mining Industry?
In recent years, AI-based autonomous robots and equipment have taken over several mining tasks. Machine learning (ML) and AI bring advanced decision-making capabilities crucial for autonomous mining operations, particularly in areas requiring intelligent automation.
Experts have recently utilized these technologies for mapping, exploration, and mine planning. In 2024, AI systems and smart autonomous platforms were developed to analyze hyperspectral imagery and identify and classify geological structures, producing detailed environmental models of mining sites.
Using data from automated mining equipment, an AI-based IoT platform enables ML algorithms to model, predict, and optimize various mining processes. This integration paves the way for smarter, more efficient, and sustainable mining practices.1
Use of Generative AI for Improving Mining
Recent notable developments include Generative AI (Gen-AI) with Natural Language Processing (NLP) and chat-bots. Like various other industries, Gen-AI has been at the forefront of document drafting and providing massive aid during final edits.
Experts have found that Gen-AI efficiently streamlines the communication process involving various stakeholders, such as the local community and the government. Gen-AI's ability to explain complex mining procedures using simple words while focusing on the benefits to the local community aids in building people's trust, uprooting a major challenge the mining industry faces.
MineBot: A novel chat-bot for mining-related activities
The complex rules and legislation related to the mining industry can be overwhelming, and thoroughly comprehending various aspects of different laws is not possible for everyone. In this regard, chatbots are the only efficient solution utilizing NLP to answer text queries. Experts have used the AI-driven conversation platform RASA to develop a mining chatbot titled “MineBot”.
Researchers tested the MineBot with different questions related to mining laws and major developments in the legislative field, and it answered user queries with a decent accuracy of above 87%. Furthermore, the AI model understood the user input and generated an accurate response in under two seconds.9
This chatbot enables companies to develop project reports and perform operations following mining laws and regulations. It has sparked considerable interest in the industry, with future chatbots expected to answer user queries related to mining in multiple languages and implement IoT capabilities to inform users regarding the ongoing real-time mining operation conditions.
Brief Overview of Companies Driving Automated Mining
Companies have discovered that the future of mining can be made safe only by integrating intelligent autonomous systems during each phase of the mining process. Software-controlled intelligent mining bots and equipment are augmenting human efficiency and promoting safety.
Aptella: A Top-Tier Machine Guidance Solution Company
Aptella's high-precision machine guidance solutions use the latest intelligent positioning, geospatial, and high-precision systems for mining operations.
The smart systems enable customizable solutions to customer problems, as opposed to a general out-of-the-box complex output that cannot be implemented to bring about the required improvements.10
Major Automated Mining Products and Platforms by Aptella
Another major product by Aptella, designed especially for automated mining operations, is the towable multimodal mining trailer. The efficient drag-and-drop system can be deployed at any mining site, minimizing downtime and enhancing productivity.
Aptella's HubX mobile payload platform significantly advances automated remote mining operations. The system enables autonomous scanning and mapping, allowing a single team of experts to monitor multiple remote mining sites. It promotes efficient monitoring while causing no harm to the local environment, making it a key tool for sustainable mining practices.
Aptella’s vision to manufacture smart mining equipment, such as intelligent dozers and autonomous drills, has made it a frontrunner among modern mining technology manufacturers.
Firms, such as SRK Consulting, use AI systems and big data to aid in the shift toward automated mining. It uses big data analytics and ML to understand the vast volumes of mining data worldwide and develop predictive models to forecast water requirements and earthquake occurrences.
Other companies like Epiroc are at the forefront of mining automation, leading advancements in equipment and solutions tailored for modern mining operations. The facts and figures estimate that the mining automation domain will be a billion-dollar industry by 2026, making various companies invest heavily in developing efficient solutions and making a large return on investment (ROI).11
Major Challenges in Mining Automation
While automation plays a key role in mining’s future, several challenges must be resolved.
Due to hardware limitations, modern mining software systems have encountered several problems when integrating into existing frameworks. This severely affects AI-driven solutions, which are already notorious among the general public for their privacy and cybersecurity concerns.12
Modern AHSs also face several limitations regarding the number of monitored and controlled trucks. This is due to the limitations of wireless communication connections and network losses at remote operational sites.
The imaging systems and sensors in autonomous intelligent equipment malfunction due to dust and debris, severely affecting their performance in open-pit mines. Furthermore, due to the focus on ecological preservation and sustainability, mining laws are becoming stricter daily, limiting the use of various technologies.
Conclusion
The year 2024 has seen the development of various automated and smart systems for mining operations, such as AHS, AI-based automated mining models, data-driven mining innovations, and IoT-based smart mining equipment.
Despite the legislative and technical challenges, researchers are making consistent breakthroughs, paving the way toward zero downtime and net-zero emissions and strengthening the stakeholders' belief in the automated mining domain.
Dive into an autonomous mining market report here
References and Further Reading
- Long, M. et al. (2024). Equipment and Operations Automation in Mining: A Review. Machines. 12(10). 713. Available at: https://doi.org/10.3390/machines1210071
- Hitachi (2024). Mining & Minerals. Digital Solutions for Smart Mine Operations. [Online]. Available at: https://www.hitachienergy.com/me/en/markets/industries/mining-and-minerals [Accessed on: December 01, 2024]
- Yang, J. et al. (2024). Autonomous Mining Transportation Systems: Integrating 4D Mmwave Radar for Enhanced Detection of Obstructed Static Objects. IEEE Transactions on Intelligent Vehicles. Available at: https://www.doi.org/10.1109/TIV.2024.3463968
- Hamada, T. et al. (2020). Hitachi. Autonomous Haulage System for Mining Rationalization. [Online]. Autonomous Driving Technology for Connected Cars. Available at: https://www.hitachihyoron.com/rev/archive/2018/r2018_01/10a07/index.html [Accessed on: December 01, 2024].
- Wang, C. et al. (2024). Real-Time ThroughWall Multiperson 3-D Pose Estimation Based on MIMO Radar, IEEE Transactions on Instrumentation and Measurement. 73. 1-11. 2510911. Available at: https://www.doi.org/10.1109/TIM.2024.3373058.
- Chen, L. et al. (2024). High-precision positioning, perception and safe navigation for automated heavy-duty mining trucks. IEEE Transactions on Intelligent Vehicles. 9(4). 4644-4656. Available at: https://www.doi.org/10.1109/TIV.2024.3375273
- Plavšić, J. et al. (2024). VR-based digital twin for remote monitoring of mining equipment: Architecture and a case study. Virtual Reality & Intelligent Hardware, 6(2), 100-112. Available at: https://doi.org/10.1016/j.vrih.2023.12.002
- KalTire (2024). TireSight: Autonomous inspections, expert analysis, prioritized service. [Online]. Available at: https://www.kaltiremining.com/en/tire-management-services/tiresight/ [Accessed on: December 03, 2024].
- Deepalakshmi R. et al. (2024). MineBot: Intelligent Mining Query Assistant. 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). 1-6. Available at: https://www.doi.org/10.1109/ICRITO61523.2024.10522279
- Aptella (2024). Automation and Positioning Tech: Mining Machine Guidance. [Online]. Available at: https://www.aptella.com/mining-machine-guidance/ [Accessed on: December 05, 2024].
- The Mining Magazine & Mining Monthly (2024). Unlocking the Future: The Automated Mine. [Online]. Available at: https://www.miningmagazine.com/partners/partner-content/4366615/unlocking-future-automated-mines-2024-report [Accessed on: December 06, 2024].
- Chen, L. et al. (2024). Sustainable Mining in the Era of Artificial Intelligence. IEEE/CAA Journal of Automatica Sinica. 11(1). 1-4. Available at: https://www.doi.org/10.1109/JAS.2023.124182
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