The mining sector has entered an exciting but complex period of technology-driven transition. AI and new-generation innovations promise to enhance efficiencies in the mining sector, reducing carbon emissions, boosting profitability and ensuring safety. Dynamic and data-driven decision making to optimize planning and production leveraging AI technologies is gaining traction within the major mining houses in Africa and Middle East.
The challenge of this phase lies in managing the transition to new-order technologies in a way that helps improve their financial efficiency, while preserving the unique social value of mining activities including bio-conservation, local community safety, welfare and employment.
AI applications
Mining teams in Africa & the Middle East are considering or already leveraging AI to optimise mine planning and production operations. For instance, AI-augmented analysis of ore samples during exploration accelerates characterization of ore bodies and improves accuracy. AI is also used for predictive asset maintenance, which reduces the downtime of mining operations due to asset faults.
At the operations stage, leading miners are applying AI to geology and mine-planning data, transforming drilling and blasting effectiveness by custom-designing each blast. Careful positioning of drill holes and automated, high-precision delivery of micro-controlled explosives help optimise blasting. This cuts processing requirements and energy consumption down the value chain. This reduces costs, as well as emissions, minimising carbon impacts at source.
Rio Tinto, which operates the Richards Bay Minerals mining operation in KZN, uses AI to optimise its systems as part of its smart mining approach, generating ore-body models, organising equipment dispatch, and predicting and controlling blasts.
In Thabazimbi, Kilken Platinum is integrating an advanced AI system to control and monitor industrial processes at its platinum tailings retreatment plant, tracking production metrics and ensuring that safety protocols are upheld.
In a recent TCS AI for Business study conducted for Energy & Resources industry, including 48 mining companies from around the world, it was found that more than 92% of the organizations have AI implementations planned, in process, or already completed.
“Diesel-powered loading and hauling can be responsible for half the emissions across the mining value chain,” says Seema Mehra, TCS VP & Business Head for Energy, Resources, Utilities, Life Sciences and Healthcare, APAC & MEA. “We are working with forward-thinking miners to develop autonomous haulage with electric mining trucks, to help eliminate these emissions. Using AI to make electrified mine haulage practical is a huge step forward in decarbonising the pit-to-port operations of the mining value chain.
TCS is also exploring AI solutions to improve mineral recovery while reducing reagent usage during the beneficiation process. Such AI intervention in mineral processing helps reduce waste, energy consumption as well as carbon emissions.
AI could also make it possible to design flexible, modular mining processes where capacity can be scaled up or down in small increments. This could help mining become more agile, eliminating the boom-to-bust cycle of traditional, capital-intensive, high-capacity mining.
Can AI overcome automation challenges in mining?
Many mining companies in Africa and the Middle east are already using autonomous equipment for haulage, drilling, blasting, as well as inspections. Miners also regularly use remote operations centres to monitor and operate the assets safely and efficiently.
However, automating individual tasks often shifts the bottleneck from one part of the mining value chain to another. The biggest challenge in scaling up traditional automation using fixed-function robotics is that miners need awareness of their entire, dynamic operating environment. The use of AI in digital twinning and deep learning can make automation smart, flexible and adaptive.
Mines can present hazardous working environments. Besides the safety benefits of AI-augmented drilling, inspection, haulage and rail, mine safety is improved by AI-driven predictions of seismic activity at mine sites, so that people and machinery can be moved out of harm’s way in time.
AI can also predict potential safety incidents due to human negligence, and computer vision can check the integrity of blast holes and the presence of residual explosives.
When it comes to assessing and analysing critical documents, generative AI can identify and extract knowledge from engineering data sheets and drawings and predict possible problems due to unclear language or obsolete formats.
Industry priorities
Looking forward, Seema identifies four key truths about AI emerging in the mining sector:
- AI is accelerating four key mining priorities. AI can support all the 4 key mining priorities – safety, efficiency, enhancing production, and reducing emissions.
- AI remains an Assist and Recommendation system. AI can generate insights, but humans are the “middleware” between the insights and corrective and preventive actions.
- GenAI is making faster inroads in the corporate functions. Corporate functions seem to rapidly adopt GenAI, but there are significant benefits to be gained from using AI in mining and exploration processes, and AI initiatives are getting wider acceptability here.
- The speed of AI adoption depends on the data and digital foundation Organisations that invest in a secure digital and data management and an loT backbone are able to develop and deploy AI-models much faster.
“We’re excited to be delivering new ways of working to achieve the realisation of ‘enterprise-wise’ AI,” says Seema. “As innovation partners to some of the most forward-looking miners on the planet, we are excited to be working towards making AI real for scenarios with value across the enterprise. AI is being used to augment human and machine collaboration in mining, to make mining safer, more productive, and sustainable.”