ISSUES WHICH CAN BE SOLVED WITH AI
AI finds its applicability across multiple business functions throughout the mining value chain like Mining Operations, Logistics, Exploration, etc. AI-powered robotic devices can perform core operation activities like drilling, blasting, loading, hauling, etc.
Improved safety and operational efficiency
What if you had a machine monitoring solution that could identify the signs that a piece of equipment is about to fail and flag it for inspection/repair days in advance?
More often than not, when a piece of equipment is about to fail, it exhibits certain signs – excessive vibration, irregular noises or lower output, for example. However, with AI (particularly machine learning) and sophisticated sensors, those signs can be monitored and identified in real-time.
Most mining operations today utilise a wide array of sensors to gather information on machinery, but while all the information is useful, compiling it and extracting insights in real-time is difficult.
With machine learning algorithms, that data can be analysed at scale and in real-time to assess machine performance and health. For example, the machine learning algorithm can use historical equipment data, environment status, weather and current usage to predict when a machine needs maintenance or might fail.
The benefits of this level of analysis are twofold: not only are operatives protected from catastrophic machine failure that might compromise safety, but operations suffer less downtime as maintenance can be scheduled in advance and new equipment brought in to cover. This leads to lower overall costs (as maintenance is planned for) and greater performance.
And this technology is not just applicable to machine monitoring – but also to every aspect of the mining operation.
Mining is incredibly expensive.
The sheer amount of moving parts, in terms of both manpower and machinery, along with the infrastructure, maintenance and other associated expenditure mean mines can cost in the tens of millions to operate.
With activities going on 24/7, productivity, operative health and efficient machine capability are absolutely essential. Any inefficiencies or concerns within the operation will lead to further costs, wasted resources and risks to employees.
But what if activities could be streamlined and optimised to ensure maximum productivity and capacity without wasting resources or compromising employee safety?
This is entirely possible. There are already sophisticated mine monitoring solutions available but the next logical step to drive efficiency is to combine mine monitoring with AI. As it stands, mine monitoring solutions enable mine managers to protect, monitor and assess every aspect of the mine operation – from equipment status and environment monitoring to personnel health and wireless tracking.
However, by integrating AI with mine monitoring solutions, operations go beyond simply monitoring and can start to analyse activities and quickly identify areas for improvement. Thanks to low-cost sensors used across mining, there is a plethora of data available for AI to utilise to better understand how mining operations can be improved. For example, mine monitoring solutions can monitor airflow within the mine and AI solutions can then evaluate airflow against machine conditions (whether they are exposed to possible degradation hazards) and operator health to identify optimum air levels. This results in more efficient usage of air, and dust suppression systems, and protects equipment, reducing costs overall as maintenance is less frequent.
Right now, mine managers have access to huge amounts of real-time monitoring data – the challenge is to use it quickly and effectively to make a difference. And that is what AI delivers: a new level of insight available in real-time that can transform operational efficiency and workforce safety.
As mining operations become more data-driven, Artificial Intelligence in the mining industry will become more prevalent. With the ability to help optimise processes, safeguard operatives and reduce costs, AI is a cost-effective, scalable and powerful solution that should be considered by mining operations around the world.
To guarantee the safety and protection of miners, sophisticated connected technologies and real-time monitoring solutions are vital. Discover more about the role of AI in hazardous industries by downloading our latest eBook:
Geotechnical assessment is utilizing machine learning via advanced fragmentation algorithms for automatic assessment within a short interval of time. These AI-powered algorithms remove the manual processing done by geotechnical engineers. The 3D mapping data is passed through machine learning algorithms to recognize spalling, cracked shotcrete, plate deformation & mesh bagging.
Safety hazards – mine vehicles
AI finds its applicability in the autonomous operation of heavy equipment operating in mines across the globe. The existing technologies in Fleet Management systems are limited to use of GPS and few sensors like LiDAR. AI-powered autonomous vehicles augment the conventional features by combining the sensor inputs with the deep learning AI systems to enable safe routing of vehicles in real time with increased accuracy and precision. AI-driven autonomous vehicles will also eliminate the risk of safety hazards caused by human drivers due to fatigue, etc. The success of the AI-powered vehicles lies with AI system trained on a humongous data pool of potential situations that might occur in real life. The system must be capable and robust enough to handle unexpected situations.
Benefits of AI in Mining
The ability to instantly gather and analyse environmental and equipment data, and carry real-time risk and area assessments is a big benefit to large-scale operations, particularly those like mining, when operatives are working in a compact, changing and potentially dangerous environment.
One of the biggest, and most obvious, risks of any mining operation is the need to haul large pieces of equipment around often confined spaces, and within close proximity of human workers.
According to the health and safety executive, there were four fatalities in the mining and quarrying industries in 2016/17, highlighting that serious risks still do exist in these sectors despite a renewed focus on safety.
Health and safety has become a much more high-profile issue in recent years but, while more is being done to make mines safer, they remain hazardous and dangerous environments and every advantage is needed in the fight to keep workers as safe as possible.
Gas detection sensors and air flow and ventilation monitoring sensors, for example, can monitor the levels of toxic and flammable gas in an environment and alert workers, and ensure that airflow and ventilation is kept to optimal levels.
IoT and AI can be a huge boost to this equipment by enabling it to supply and analyse real-time data instantly, and send warnings to individual miners, no matter where they are located.
Using this kind of technology to assess a new or unexplored environment with automated machinery is also a massive safety benefit and can protect miners from injury or long terms health hazards.
Assessing environments in this way also removes the need for human exploration, or for workers to be moved into a new environment until it has been deemed safe, while also removing the requirement to move large items of equipment around unnecessarily.
AI technology can also predict when machinery is likely to require repairs or maintenance and removes the danger of equipment malfunctioning while it is being used and potentially causing injury.
Control and display monitoring units already carry out machine and equipment monitoring, but giving this equipment the ability to predict problems, rather than only alerting to problems after they’ve happened is the next step to improved worker safety.
These benefits are not being lost on the mining industry, and many businesses are already taking steps, or are planning to, in the next few months and years.
About 40% of mining organisations have plans to implement IoT solutions within the next 18 months, according to a report into the future of IoT in enterprise, with 44% of citing health and safety as the main reason for investigating these solutions.
Artificial intelligence and the science of robotics can be put to use in mining and other fuel exploration processes. Not only that, these complex machines can be used for exploring the ocean floor and hence overcoming the human limitations. Due to the programming of the robots, they can perform more laborious and hard work with greater responsibility. Moreover, they do not wear out easily.
More efficient operations
The mining industry is a large and diverse eco-system.
UK-listed mining companies had a total market capitalisation of $425 billion, according to figures published by UK Trade and Investment, more than any other financial market in the world.
Mining involves various operational techniques, and equipment and technology, depending on the mineral being explored for and extracted.
Because of this, operations can be extremely expensive, while the potential for waste and inflated costs due to inefficiency is great.
Even before mining operations begin, exploring and discovering where to actually set up can be time-consuming and expensive, but new technology, which can monitor environments and report back on ground materials make this process much simpler and more efficient.
Similarly, data collected during the initial operation or set up can ensure everything is being managed correctly, and that safety is maximised based on the information gathered on environmental and structural analysis.
Mine layouts and vehicle paths can be worked out and assessed above ground and ensure routes are optimised for exploration and extraction, while flagging any potential safety hazards well in advance of workers getting near them.
Doing this also allows for mining operations to be more agile and responsive, with problems or hazards being identified well in advance of them becoming an issue and reducing the prospect of projects stalling while solutions are found after work has already started.
More efficient energy and cost savings
Mining operations consume large amounts of energy and resources during projects and any kind of inefficiency in equipment or working practice can exacerbate this energy waste greatly.
It is evident, therefore, that using IoT enabled devices and AI to monitor, collect and analyse data in real time vastly reduces the chance of energy, time or cost wastage by ensuring all elements of the mining operation are running at maximum efficiency.
Ensuring equipment is operating at full capacity and giving early warning when equipment is failing means repairs can be made sooner, meaning reductions in downtime later on or once equipment stops working altogether.
Similarly, environments can be constantly monitored for atmospheric or structural changes and workers can be warned or evacuated and solutions found before anyone is put at risk or operations have to be shut down and re-evaluated.
Mining is a vast and complex part of the world economy and operations carry a great deal of risk, both in terms of commercial and risk of injury or death. New technology is making it easier to ensure greater worker safety during digging and exploration operations while commercial efficiency is becoming much easier to attain.
Companies are under an obligation to ensure environments are operating properly and safely for workers, and using technology in a better way is a key means of achieving that.
Interesting Projects & Applications of AI in Mining
Mining is a global industry that is fundamental to every product we use. A vital component of the mining industry is efficiency because most of the production revolves around transforming matter into different forms.
Finding gold deposits
Goldspot Discoveries Inc. uses artificial intelligence for improving mineral exploration. The current practice of finding gold deposits is more an art than a science, thus Goldspot Discoveries Inc. intends to change that by developing AI systems capable of ingesting different data from which to discover potential gold deposit locations. AI is also used to understand better the environment and the terrain where new development will take place. In this space, Drone Deploy uses drones and computer vision to understand better the environment and the terrain where exploitation is to begin.
Artificial intelligence is not limited to systems capable of going through vast amounts of data. Some companies aware building intelligent systems for other phases of the operation. For example, the diamond mine Renard, in Quebec. There we see a smart system for waste sorting and disposal. This system is primarily used to improve the quality and quantity of the diamond recovery process. The algorithms use data from sensors and X-rays to increase the diamond recovery rate which helps recovering at least 96% of the weight of all diamonds larger than 1mm.
Sorting system for minerals and ores
Within the same space, Tomra, a Norwegian mining company, also developed a smart sorting system for minerals and ores. They are using computer vision and other AI algorithms powered by colour sensors and X-rays data..
Finally, another successful example is Rio Tinto who now uses a fleet of autonomous vehicles inside the mine. These vehicles can be remotely operated and managed.