One of my favorite quotes, “The future is already here — it’s just not very evenly distributed” by William Gibson, reflects the idea that advancements and innovations that will shape the future are already present in certain regions, industries, or sectors. However, the distribution and accessibility of these advancements are uneven. A prime example of such innovation is Artificial Intelligence (AI).

AI involves developing computer systems capable of tasks that typically require human intelligence. These include learning, reasoning, problem-solving, perception, linguistic understanding, and creativity.

AI encompasses a broad range of methodologies, technologies, and subfields. In mining and mineral exploration, we have been applying Machine Learning (ML) and other AI algorithms since the 90s. Recent advancements in Deep Learning and Natural Language Processing have allowed the development of AI models that can not only enhance efficiency and safety but also pave the way for innovative practices that will redefine the sector.

This article delves into the current and future applications of AI in mining, spotlighting innovations that, while not universally adopted, have the potential to reshape the industry.

Current Applications
AI is revolutionizing the mining industry by enhancing operational efficiency, improving safety, and reducing environmental impacts. Below are some of the transformative applications currently being deployed:

Geometallurgy: The application of ML in geometallurgy, one of AI’s earliest uses in mining, involves analyzing complex relationships between ore attributes and processing plant performance. These relationships are then used to improve decision-making and optimize processing operations. More recently, some companies are using ML-based systems to adjust processing plant parameters in real-time, based on characteristics of the ore being mined. This leads to better recovery and throughput, enhanced supply chain efficiencies, and reduced energy consumption.

Exploration Targeting: AI algorithms can process vast amounts of geological data, outperforming traditional methods in speed and accuracy. ML algorithms are being used to generate exploration targets, assess their potential value, and optimize exploration strategies. This approach not only increases the chances of discovering new deposits but also reduces the environmental impact of exploration activities.

Core Logging: Using hyperspectral imaging for automatic core logging has been considered for decades. Over the last decade, some mining companies have spent millions on core scanning, storing vast amounts of data. However, these efforts often fell short of producing meaningful interpretations for effective geological modeling. Recent advances in deep learning have allowed this technology to start delivering on its promise.

Grade Control: Modern grade control optimizers apply AI algorithms for dig-line optimization. This is perhaps the lowest hanging fruit for the use of AI in a mining operation. The advantages of such approach have been documented for many years, but unfortunately the uptake of the technology has been slow. Software developers are starting to catch up, with new offerings now available by the major mining software providers.

Predictive Maintenance: Another existing application of AI in mining is predictive maintenance. By using ML algorithms to analyze equipment data, mining companies have been able to predict failures before they occur, minimizing downtime and extending the lifespan of fixed and mobile assets. This approach not only cuts costs but also boosts safety by lowering the risk of equipment-related incidents.

Automation and Robotics: The mining industry increasingly employs autonomous vehicles, robots and drones. These technologies, guided by AI, can operate in hazardous environments, performing tasks such as topographic surveys, drilling and transporting materials without human intervention. This not only improves safety, by reducing human exposure to dangerous conditions, but also enhances operational efficiency.

Data Mining: Commercial and company-owned AI platforms have been used to digest vast quantities of technical reports, financial disclosures, and press releases as well as mining news and technical articles. This creates an extensive knowledge base that can be consulted using natural language and advanced analytics. The most common users of such platforms are investors, analysts, deal makers, and exploration teams.

Stochastic Mine Planning: AI was applied in the early 2000s to develop the first stochastic mine planning method for open pit scheduling. Since then, a couple of decades of R&D sponsored by major mining companies, have developed a more sophisticated solution able to optimize whole mining complexes, from mine to market. The method can take a myriad of uncertainties into account. This method, in broad terms, uses a combination of stochastic optimization and AI techniques to obtain the optimal solution for the mine scheduling problem.

Worker Safety and Health Monitoring: AI-trained wearable devices are being used to monitor the health and safety of miners in real-time, detecting signs of fatigue, exposure to harmful substances, or physical stress. These systems can alert workers and managers to potential health and safety risks, significantly improving workplace safety.

Looking Ahead
The future of AI in mining is bright, with potential applications set to further transform the industry, making it safer, more efficient, and sustainable. Here’s a glimpse into what the future holds:
Resource modeling: Resource modeling is a process where typically 80% of the time is spent processing data, while the remaining 20% is used to analyze and refine the results. Soon, autonomous systems, guided by AI algorithms, will be able to flip these statistics around. This will allow geologists to spend considerably more time stress testing models and refining estimates, resulting in higher confidence estimates and models that can be updated seamlessly as additional information becomes available.

Digital Twins: Before the end of this decade, we will see the proliferation of AI-based systems to create and run digital twins of mining assets and even whole company portfolios. These digital models will allow us to model and simulate the behavior and performance of entire mining complexes. Embedded with stochastic planning capabilities, these digital twins will help us optimize operations, predict failures, and test different scenarios for improved decision making. Importantly, it will break the infamous mine planning cycle, that mines go through every year, and allow for near-real-time updates that respond to the ever-changing conditions of a mining operation.

Mine Design: Developing mine designs is a laborious and time-consuming task, which requires considering multiple parameters such as resource models, mining methods, equipment selection, infrastructure, capital costs, operating costs, and many others. Generative AI models will enable more efficient design creation and allow for the evaluation of hundreds or thousands of scenarios with ease. This ability is key in determining the more efficient and cost-effective mine design for a given orebody in a timely fashion.

Environmental Monitoring and Sustainability: The future of AI in mining includes advanced systems for environmental monitoring and sustainability. AI can be used to monitor the impact of mining activities on ecosystems, predict environmental risks, and develop strategies to mitigate negative effects. These applications of AI are going to help achieve sustainable mining practices and adhering to increasingly stringent environmental regulations.

Integration with Renewable Energy Sources: AI can optimize the integration of renewable energy sources into mining operations. By predicting energy consumption patterns and coordinating with renewable energy availability, mining operations can reduce their carbon footprint and energy costs.

Collaborative Robots: Future advancements may see the rise of co-bots in mining – robots designed to work alongside humans. These AI-driven robots could undertake tasks that are unsafe or unsuitable for humans, while humans focus on supervisory and decision-making roles, creating a synergistic workforce.

In conclusion, the application of AI in the mining industry is not just a trend but a fundamental shift towards safer, more efficient, and sustainable operations. As technology advances, the potential for AI to transform the sector grows, offering promising prospects for the future.

This evolution requires ongoing investment, as well as a commitment to training and upskilling the workforce to adapt to an ever-changing technological landscape. Embracing AI is not merely an option but a critical step forward for the mining industry, ensuring its growth, sustainability, and resilience in the years to come.

Source: Global Mining Guidelines Group – GMG.
Web site : https://gmggroup.org/