AI in mining – Possibilities and Opportunities
Think of a mine, and most people picture a dark underground tunnel with bare metallic shafts and beams of light attached to helmets cutting through the subterranean darkness. To a layman, it might seem almost absurd to picture new-age technology like artificial intelligence (AI) finding application in an activity such as mining.
The beauty of AI, however, lies in the fact that it is designed to mimic the human functions of learning and problem-solving. This means that it can be deployed in any situation where incremental, iterative learning needs to be deployed for problem-solving.
At the same time, the popular stereotype of the mine as a primitive, obsolete space could not be farther from the truth. Modern mines deploy state-of-the-art technologies and the most advanced processes to maximize their output, minimize their environmental impact, and ensure the safety of their employees.
Despite this, the mining industry faces significant challenges to which AI seems to offer the best solutions at the moment. Let’s understand how.
AI in Mining – Cutting-Edge Technology Meets an Old-School Industry
Like agriculture, the mining of ores and minerals is foundational to our survival as a civilization. Industries of critical importance use these minerals as raw materials. However, mines across the globe are beset with numerous challenges while trying to maintain sustainable operations.
Minerals are, for the most part, exhaustible and non-renewable resources embedded deep inside the earth that require complex technology and highly skilled labor to extract. Price volatility, stricter environmental compliance regulations, and a human resource crunch for staffing mines have put increasing pressure on the sector over the last two decades.
AI-led operational intelligence can ensure better business outcomes and, at the same time, minimize the environmental impact of mining.
How Can the Mining Sector Overcome Operational Challenges With AI?
Some of the key pathways through which AI can help the mining sector get back on track are:
1. Faster Data Processing
In most mines, data is still collected manually. This means slower processing and a greater margin of error. Using machine learning-based methodologies would not only deliver data results faster but also reduce the overall error involved.
2. Better Health and Safety Standards for Workers
AI can help mining companies implement better health and safety standards for their workers. One of the most effective ways to do this is to develop simulating platforms using AI-backed techniques to train workers better.
AI can be used to create highly realistic simulated environments in which workers can practice their skills. More skilled mine staff means lesser potential for mishaps in a real-world environment and greater risk mitigation.
3. Using Neural Networks to Power Predictive Maintenance
Artificial neural networks can be utilized to know when mining equipment needs to undergo maintenance. The technology can also be used to gauge the quality of excavated ores with a higher degree of precision. The system can be trained to look for specific characteristics and allow only those materials which fulfill this criterion. This will lead to a reduction in operational costs and avoid unwarranted human error.
4. Develop Optimized Discovery Tactics
One of the reasons AI has a bright future in mining is that it lays down a detailed blueprint even for those organizations that are just starting out in the sector. AI helps mining companies identify key opportunities and industry trends so that they can plan out their long and short-term strategies.
It also helps to align the goals of these organizations with the ground realities of the industry, thus enabling better outcomes.
How Can AI Help Achieve Sustainable Mining Operations?
AI is driving sustainable operations in the mining sector in the following manner:
1. Reducing the Overall Environmental Impact
The mining industry has been faced with increasing pressure over the last few decades to reduce its ecological footprint and ensure greater compliance with environmental regulations. With AI-backed solutions, the industry can achieve these goals faster and with fewer hiccups.
To take just one example, at present, the ventilation stage in mining is the stage where maximum energy is utilized. With the help of AI-enabled process systems, faulty procedures can be tweaked, thus reducing energy emissions.
2. Infusing Innovation
Traditionally, the mining sector has abided by tried and tested methods to cut losses. Being a capital-intensive sector, it has shown greater resistance to new technologies due to higher operational costs, thereby stifling innovation.
Artificial intelligence allows mining companies to use machine learning and neural networks to adopt innovation in the ways ores are mined at much lower overhead costs.
Which AI Is Good AI: The Need for Explainable AI
So far, we have understood that bringing artificial intelligence into the mining industry can positively impact. But what type of AI?
AI typically works in the mining sector by collecting large amounts of data about a mine’s operations and processes and using it to developing a data model, which is then analyzed for understanding challenges, setting goals, and streamlining processes. The insights gleaned from such a system would not be available if we don’t make use of explainable AI solutions that can be understood by humans.
Since AI has components of human cognition, it can easily diverge from the problem at hand and give solutions that even its programmers can not comprehend, or what is known as black-box AI.
Another reason why we stress explainable AI is that the mining industry usually hires AI solutions from third-party providers rather than developing their own in-house solutions. This causes issues when the technology needs to scale up, and the company does not have adequate infrastructure in place. Worse, such AI solutions might be biased, leading to a host of compliance issues.
Industry leaders should ensure that, for the time being, only explainable AI is utilized to mitigate risks while maintaining business proficiency.
Eugenie’s Human-Centric Approach to Achieving Operational Reliability
Eugenie has been at the forefront of leveraging the power of explainableAI to empower decision-makers with the benefits of root-cause analysis and predictive insights. Our human-centric AI has been built keeping the core philosophy of congruence between humans and machine capabilities at its heart. As such, we deploy user-centric designs to improve operational and machine reliability.
Eugenie considers technology an ally to enhance human intelligence and capabilities, resulting in more efficient explainable models that can solve some of the mining industry’s most pressing problems. With our twin in-house proprietary AI solutions – Ray-Finn and Papillon, we enable businesses to both analyze voluminous, time-series data as well diagnose bottlenecks through real-time monitoring and AI-powered predictive insights.
The result is increased productivity, reduced industrial waste, and enhanced machine reliability, all with the same set of assets.
There is little doubt that artificial intelligence holds immense promise for the future of mining. It is already driving out human error, using data to boost decision-making, streamlining operations and increasing efficiency, helping mining companies minimize their ecological impact, and ensuring the safety of frontline mine workers. AI, however, is not the silver bullet to all of the mining industry’s problems.
To learn more about how Eugenie has helped mining and minerals companies ensure a profitable transition to operational sustainability, talk to us today or feel free to reach out to us at firstname.lastname@example.org.
The industry as a whole needs to open up to modern operational practices that promote efficiency in the short to medium term and allow it to shift from a resource-centric to a people-centric approach in the longer term. AI can act as a powerful enabler and a potent catalyst to help the mining industry get there.
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