When you combine the words data and mining, you might think IT and technology-focused initiatives aimed at extracting value from data in the enterprise. But sometimes we mean applying the power of data and intelligence to the actual mining industry, where the primary value being extracted are resources from the earth. Even in this very physical of industries, artificial intelligence and machine learning are being applied to increase the efficiency, effectiveness, environmental and safety concerns, and other aspects to help continue to make mining a valuable enterprise. Mining has matured significantly as an industry over the past century. No longer are we mining with physical hand and animal labor, extracting crucial resources by pickaxe and oil lantern. Modern mines are now sophisticated, machine-intensive environments that put heavy equipment and computer-driven technology near the humans that need to operate them. Whether we're extracting minerals and ores such as copper, iron, gold, or energy-rich deposits such as coal, oil or gas, machines are needed to extract the mineral resources from the earth, transport them to the surface, prepare them for refining or transport, and move them to the destinations where they can be further refined. Because of how dependent we are on these natural resources for many parts of our lives, the operations behind mining have gotten quite complex.
The act of extracting minerals from the earth is quite disruptive to the environment. When we mine or drill, we are digging deep into the Earth and pulling out resources. We also have to extract the debris around it and manage piles of waste material generated from the mining process. Often, managing the environmental aspects of mining can be just as time and cost-intensive as extracting the resources themselves. Artificial intelligence is helping to transform the mining industry into a safer, more profitable, and more environmentally friendly industry.
Improving Resource Discovery and Planning With AI
Mining is a very costly undertaking. To minimize the initial investment, mining companies need to be very precise about where and how they dig. One of the ways that the mining industry is utilizing AI is to learn more about the terrain that they are working with. The computer can much more precisely map out and predict terrain than a human. Most of the time we have to dig our way to the resources in the first place. That requires significant investment. An error in mining in the wrong location can cost millions or billions of dollars. AI can help us better prevent those errors.
Artificial intelligence is also being used to identify new and potentially valuable areas to mine or drill. Through the use of pattern matching, predictive analytics, and even computer vision systems that can process map and geological data AI can analyze vast quantities of data to better predict where to find better resources. With better predictions comes better planning and a better return on investment.
AI is becoming a powerful tool for analyzing data of all kinds in the mining industry. Most industries use machine learning and artificial intelligence to analyze their data in operations, ranging from managing transport and logistics to human resources and supply chain management. Machine learning can spot patterns that are useful in reducing expenses, optimizing resources, and reducing waste.
Intelligent Drones and Autonomous Machines
Drones are increasingly being used in the mining industry, becoming a very powerful tool for a wide range of applications. Companies are using drones to scan over their mining operations, keeping an eye on quarry and waste piles, environmental issues, retention and leaching ponds, and pipeline infrastructure. Much of what can be seen with a drone cannot be seen with our eyes on the ground. From the sky, progress can be monitored as well as the mine's impact on the surrounding ecosystem. Using machine learning-based computer vision systems, these drones can analyze data collected from the imagery. This gives mining companies continuous, around-the-clock access and monitoring to their facilities in ways not possible with human operation.
Mines have always been dangerous places to work. However, to extract the resources we need, we’re moving to increasingly more hostile environments to get them. Whether we’re extracting coal or minerals miles under the earth, or oil and gas from deep-sea drills, or excavating land in arctic zones, we’re increasingly putting people into harsh environments. It is much more preferable to put machines and equipment into mines and greatly minimize or eliminate human labor from these harsh conditions. Through the use of AI-powered autonomous systems, mining and energy companies are making greater use of self-controlling machines in harsh environments. This equipment is then able to work without the presence of a human. It is also capable of going to many places that humans just can’t physically go.
Autonomous mining equipment is set to increase overall productivity. In addition, these machines can work around the clock without tiring while also minimizing costly and potentially fatal mistakes. If a machine is stuck in a mine, we can always retrieve it at a later time and date without worrying about it dying. We can’t do the same with a human. Because of this, Komatsu Mining has built a wide range of AI-powered autonomous equipment being used in a variety of hostile environments. Some of these machines are autonomous excavators while others are autonomous transportation and loading vehicles. Many modern mining companies operate the "digital mine" which connects the equipment, uses intelligent technology to decentralize control, and minimizes human labor. Autonomous vehicles are part of what is called a digital mine.
Another aspect of these autonomous machines is that they can perform some of the regular inspections needed on the mines. By attaching cameras and sensors to the equipment, companies can detect issues in the mine such as gas levels and structural instability. The more frequently that mines can be inspected and the less we need to send human inspectors into a mine, the safer the mining operation is.
Reducing Environmental Impact with AI
Mining by its nature is destructive and has a negative environmental impact. While it isn't possible to eliminate the negative environmental impact of mining, it is possible to significantly reduce this impact by managing how resources are extracted, transported, and treated. Cameras and sensors are being deployed around mines, both on the inside and outside to have constant surveillance. These devices can monitor excavation, extraction and general mining activities, keeping tabs on the spread of waste and harmful materials.
What makes AI monitored devices different from "dumb" devices is the analysis of the data. These AI-enabled devices are capable of instantly analyzing and interpreting large volumes of sensor data, and alerting when an issue is present. These systems can spot patterns that may be of concern. Regular tremors, temperature changes, and events across multiple parts of the mine can all be recognized by machine learning.
Artificial intelligence has had a broad impact on the mining industry. Mining equipment manufacturers and development companies are increasingly making use of AI to provide greater value and reliability to the industry while also increasing safety and reducing environmental impact. The impact AI is already having is being shown through safer mines, reduced impact on the environment, improved economics and profitability, and increased extraction of precious resources.
Ronald Schmelzer, columnist, is senior analyst and founder of the Artificial Intelligence-focused analyst and advisory firm Cognilytica, and is also the host of the AI Today podcast, SXSW Innovation Awards Judge, founder and operator of TechBreakfast demo format events, and an expert in AI, Machine Learning, Enterprise Architecture, venture capital, startup and entrepreneurial ecosystems, and more. Prior to founding Cognilytica, Ron founded and ran ZapThink, an industry analyst firm focused on Service-Oriented Architecture (SOA), Cloud Computing, Web Services, XML, & Enterprise Architecture, which was acquired by Dovel Technologies in August 2011.
Ron is a Parallel Entrepreneur, having started and sold a number of successful companies. The companies Ron has started and run have collectively employed hundreds of people, raised over $60M in Venture funding and exits in the millions. Ron was founder and chief organizer of TechBreakfast – the largest monthly morning tech meetup in the nation with over 50,000 members and 3000+ attendees at the monthly events across the US including Baltimore, DC, NY, Boston, Austin, Silicon Valley, Philadelphia, Raleigh and more.
He was also founder and CEO at Bizelo, a SaaS company focused on small business apps, and was Founder and CTO of ChannelWave, an enterprise software company which raised $60M+ in VC funding and subsequently acquired by Click Commerce, a publicly traded company. Ron founded and was CEO of VirtuMall and VirtuFlex from 1994-1998, and hired the CEO before it merged with ChannelWave.
Ron is a well-known expert in IT, Software-as-a-Service (SaaS), XML, Web Services, and Service-Oriented Architecture (SOA). He is well regarded as a startup marketing & sales adviser, and is currently mentor & investor in the TechStars seed stage investment program, where he has been involved since 2009. In addition, he is a judge of SXSW Interactive Awards and served on standards bodies such as RosettaNet, UDDI, and ebXML.
Ron is the lead author of XML And Web Services Unleashed (SAMS 2002) and co-author of Service-Orient or Be Doomed (Wiley 2006) with Jason Bloomberg. Ron received a B.S. degree in Computer Science and Engineering from Massachusetts Institute of Technology (MIT) and MBA from Johns Hopkins University.