Empowering Mine Operators: AI's Role in Shaping the New Era of Mining

mining machine in a mine

Intelligent solutions hold the key to revolutionizing mining operations by augmenting the capabilities of human operators. AI-based technologies are at the forefront of this transformation, offering unparalleled support in decision-making, automating repetitive tasks, facilitating seamless information sharing across functions, and enabling cost-effective scalability of solutions throughout the operational infrastructure. 

The mining industry is experiencing a surge in demand for minerals essential to electronics, but it is facing multiple challenges affecting its ability to boost production. One of the biggest challenges is that productivity relies heavily on human capacity and collaboration between humans and machines. This often leads to significant fluctuations in daily output.

Moreover, the mining industry is struggling to attract new talent due to its harsh working conditions, and the retirement of seasoned workers results in a shortage of expertise. Mining operations also involve workers and technical systems from multiple companies, which makes coordination and optimization challenging. System settings are often predetermined and cannot account for changes in equipment conditions or the quality of ore inputs.

Key challenges in the mining industry

AI can offer revolutionary solutions to enhance human labor in the mining industry. It is a tool that can be adapted to a great number of cases with little investment past the initial setup. While technological advancements can often bring uncertainty, new technologies in the mining sector aim not to replace humans but to uplift their productivity and the quality of their work. Humans are essential in ensuring safety, compliance, and knowledge of the industry.

Augmenting human operators: the need for adaptable and continuously improving systems in mining

The mining industry is undergoing a transformation that mirrors the changes that have revolutionized the telecommunications industry over the past three decades. Much like the shift in mobile phones from static models to digital platforms with evolving application ecosystems, mining is also transitioning from a rigid, unchanging system to a flexible and agile one.

In the past, the market value of mobile phones was determined by their built-in functionalities. Today, however, web-connected phones are valued for their ability to evolve as new applications and software updates enhance their capabilities without the need for hardware modifications. Such a paradigm shift has redefined the role of humans from creators of a singular, unchangeable product to innovators who continuously refine and adapt the product to meet market demands. This has been a massive shift both from a technical and a business perspective, a shift that has not happened overnight but gradually and keeps on evolving.

In mining, this evolution translates to the gradual integration of new functionalities into machinery and a shift in practices and mindsets to flexible and agile systems. Automation of functionalities in mining is not a new idea in itself. Automation of fully repetitive tasks has been in place for the past 20 years but it has been purpose-built for a specific task in a closed-off environment with limited capacity to adjust to change and integrate with other functions.

Driving change in the mining industry 

The mining industry is facing several challenges that require innovative solutions to improve productivity and attract a new generation of workers. These key challenges can be said to be driven by three factors: the amount of ore moved through the mine per investment, production downtime reduction, and value extracted per ton of ore.

Tons moved through the mine Reduction in production downtime Value extracted from a ton of ore
Worker productivity Equipment automation Dynamic system configuration
End-to-end system optimization Predictive maintenance Adaptive process control
Dynamic excavation Operator augmentation More sustainable use of materials

Boost production for maximum tons moved through the mine

Amidst a surge in demand for minerals essential to electronics, mining companies are under increasing pressure to boost production. It's more financially and environmentally efficient to ramp up the operations of existing mines than to establish new ones, which have higher incremental costs.

Reduction in production downtime through automation

Automating mining equipment makes the operation more predictable, benefitting the entire mine. By implementing predictive maintenance in mines, companies can reduce equipment downtime as maintenance needs are recognized well in advance. 

Additionally, the mining industry has harsh working conditions, making it less attractive as a career path. Operation augmentation helps reduce training needs and improves the field's attractiveness.

Value extracted from a ton of ore

Sustainability demands and continuously varying mineral needs stress the need for more efficient use of natural resources such as ore bodies and water. More sustainable use of materials reduces mine-level and company-level costs.

Harnessing AI for transformative solutions that address the challenges in the mining industry

AI stands out as a technology for its unparalleled learning and adaptation capabilities, offering a leap in technological evolution unlike any before. This adaptability enables AI to navigate complex and sometimes unforeseen operational challenges, providing insights that span individual tasks to organizational levels. 

Increasing productivity by freeing human capacity

The technological leap AI enables, when integrated within the mining industry, increases productivity and frees human capacity for complex tasks, control, and innovation. Such capabilities are invaluable across multiple scenarios in the mining sector, including:

  • Dynamic underground excavation: Implementing AI in excavating new underground mines revolutionizes how tunnel development and infrastructure build-out adapt to continuous environmental changes over the years. For example, in development mining, AI enables continuous optimization of drill patterns and blast design based on real-time drilling data, adapting to local variations in ore grade and rock hardness to maximize asset utilization. 
  • Value chain optimization: As more equipment and machinery become digitalized, there is now an opportunity to integrate data from these various sources and optimize mining processes as a whole rather than focusing on individual steps or phases of work. AI is crucial in evaluating operations across the entire value chain, thereby identifying opportunities to streamline processes and improve efficiency.
  • Adaptive tools and machinery: The incorporation of AI technology will revolutionize the creation of tools and machinery. For instance, Australian iron ore mines have been largely autonomous, from drills and trucks to trains and ports. However, the machinery deployed in the mines has been designed for a static environment, making it challenging to adapt to different, especially dynamic environments. By utilizing AI in creating machinery, it becomes possible to make them more adaptable to various mining environments worldwide.

Accelerated creation of new functionalities 

From a business perspective, AI introduces unprecedented scalability compared to traditional human-written software logic. Mining companies can quickly develop and deploy new features using shared datasets and algorithms, reducing development costs and accelerating innovation. However, it is crucial to keep in mind the product's lifecycle costs, as it is not helpful if the savings in operations have to be spent on keeping the new systems running.

With the digital landscape constantly evolving with new AI features, it is crucial for industry leaders not to get lost in the sea of solutions and innovate for the sake of innovating but to prioritize solutions that promise tangible benefits. Companies should begin by identifying areas where they can create significant new value and assess whether AI can assist them rather than starting with the question, "What can be done with AI?" AI should not be built one functionality, use case, or solution at a time. Instead, it should be seen as a more substantial change program that involves humans, systems, and processes. Having a single bigger change program is far easier and cheaper to execute than trying to push through several ones in a series.

Balancing out fluctuations in operational output

Just like how different tools aid drivers in driving safely, AI can act as a "smart mentor" for mining operators, providing personalized recommendations and assistive control as they perform tasks such as operating the fleet and drilling mines. AI can coach operators of different skill levels into top performers, resulting in improved safety and productivity. 

AI tools and cooperative fleet intelligence can reduce the cognitive load of mining operators, decreasing fluctuations in daily output. Similar to driving, if operators can focus on perceiving the environment rather than the mechanical aspects of their job, they can maintain better alertness, resulting in improved safety. As a result, the mine's productivity will not be as heavily dependent on human input, which can lead to more consistent and reliable results.

Bridging the skills gap

In the face of the inevitable generational shift within the mining workforce, the integration of AI becomes not just advantageous but essential. Capitalizing on the vast amounts of accumulated knowledge, AI solutions can be trained and validated by seasoned professionals to embody decades of experience, ensuring that the wisdom of today's experts informs the innovations of tomorrow. This approach not only helps bridge the imminent skills gap but also elevates the field's appeal to a new generation of talent by showcasing mining as a domain of cutting-edge technological advancement. Furthermore, AI presents opportunities in training, onboarding, and retraining personnel, reducing costs and improving compliance, safety, and production. 

Unlocking value with AI in mining: a systematic approach

The systematic expansion of AI in mining necessitates a foundation built on robust AI infrastructure, targeted development initiatives, and a concerted effort towards upskilling operators to work alongside these advanced systems. By adopting a methodical approach to AI integration, mining operations can achieve scalable, efficient, and adaptable solutions that empower humans to excel in their roles, ensuring the industry's sustained growth and relevance in an increasingly digital world.

Rethinking validation methods for AI in mining

The mining industry is a high-tech industry with advanced knowledge in both physical machinery and software. Like in many industries, new technological innovations are often introduced through innovation demos. These demos are tailored to tackle specific challenges in a test environment through pre-written code. This approach works well for testing solutions based on traditional technologies where the software is designed to respond to all possible scenarios, however, this is not the case with AI. 

AI-based systems operate on principles different from those of traditional software. Achieving a competitive edge with AI requires a transformation on many levels, starting with the validation method of the proposed solution. AI's biggest strength is its flexibility to learn new scenarios, and a traditional innovation demo environment does not provide the right setup to test and explore the opportunities offered by AI-based solutions. Companies should focus on where AI delivers tangible benefits in key business domains rather than isolated single-use cases.

Gradual industry changes in a dynamic societal context

The current generational shift is a destabilizing factor for the whole mining industry, but it can also be seen as an opportunity for disrupting it. AI technology can provide more reliable productivity and solve challenging labor conditions while enhancing human knowledge and skills through human-machine collaboration. By leveraging the industry knowledge of those currently in the industry with extensive experience, AI systems can be optimally trained to enhance already sound systems and make them as reliable as humans, if not more, when used together with skilled operators whose roles may look very different from today's roles in the field.

A clear trend that can already be seen in some of the biggest mines is the role of humans in mines. Humans are not working in certain areas in mines but instead in control rooms where their role consists of monitoring, training, guiding, and collaborating with machines on site. Such change is not created through one overnight update but rather through the incremental integration of functionalities with AI at their core, a gradual adaptation of the industry to the new practices, and the creation of new roles and responsibilities for humans to leverage their abilities optimally, leaving repetitive and laborious manual tasks to machines.

Ready to scale up your AI capabilities within the mining sector? 

We at Silo AI have been collaborating with key players in the mining industry to help bridge the gap between the industry's traditional ways of working and the demand for increased production efficiency and skilled labor. Our approach offers a novel way to implement AI solutions that accurately reflect the competitive edge and nature of AI technologies, tailored to meet our customer's needs and the diverse environments of their end-users.

We recognize that our clients are experts in their respective fields, and we bring our expertise in building AI-based systems to their environments. We believe that AI should not be an additional add-on to existing systems and products but an integral part of them. That's why we work closely with our clients to assess their product portfolio and determine how AI can enhance their offerings. 

By partnering with us, companies can effectively navigate the complexities of AI adoption and unlock product innovations with efficiency.  


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Get in touch with our AI experts.
Peter Sarlin, PhD
CEO & Co-Founder
+358 40 572 7670
Nico Holmberg, PhD
Head of Technology
Silo AI

Computer vision expert with experience in building deep learning based solutions for clients in various industries with use cases ranging from situational awareness systems for heavy industrial machines to advanced video analytics for safety and security. One of his main interests includes optimizing neural network models for deployment on embedded devices and AI accelerators for edge inference applications. Nico holds a PhD in Computational Quantum Chemistry from Aalto University, Finland. During his PhD studies his research focused on developing new methods to design effective materials for renewable energy applications. Author of 12 research papers with 600+ citations.

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