AI-driven Automation in Mining Industry

Overview

Infosys partners with mining enterprises to leverage Artificial Intelligence (AI), machine learning, and cognitive computing to determine when, where and how to mine most efficiently. AI-driven automation enhances digital control systems and programmable logic controllers to boost reliability and accuracy at mining sites.

Nia, our AI platform, consumes data in diverse forms and formats – geological, topography, geo-mechanical, engineering, mineralogy, and well logging data – to augment each phase of mining. It accelerates prospecting, discovery and exploration by predicting target zones and using soil samples from a few test holes to classify the total surface area / rock face / subsurface materials. AI algorithms streamline ore fragmentation assessment, pre- and post-blast surveys and site inspections in underground and open pit mines using satellite imagery, aerial photographs and 3D maps.

AI programs combine core drill data, sample analysis results and survey reports to recommend techniques for maximizing ore deposits. Moreover, it guides geologists and engineers in extraction planning and optimization. Advanced analysis of composite samples enables quantification of ore reserves as well as impurities in the output. The data helps streamline processing and sorting procedures to conserve energy and minimize truck rolls.

We use multivariable modeling to predict and address constraints in the development and construction phases. Our AI platform solutions accelerate returns on newly discovered ores by providing insights to extract and process minerals and metals efficiently and safely. It empowers digital mines built with sensor network technologies to capitalize on IoT data for real-time surveillance and operations management.

Infosys combines robotic automation and industrial IoT to manage autonomous drilling systems and hauling fleets. In addition, it helps mining enterprises use fleet motion metrics to minimize asset idle time and prevent equipment collision. Significantly, our machine learning algorithms can be trained to extract contextual data from complex documents, identify risks, and respond to user queries.

Nia, our AI platform, applies machine learning techniques to make accurate predictions in the absence of historical data.

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Success story: Asset-specific analytics helps mining company rationalize costs

Operational intelligence from our vehicle telematics and data analytics-based fleet management system guides decisions on capital expenditure.

Challenges & Solutions

You should adopt automation to save time, ensure workplace safety, and boost enterprise productivity.

Tools to analyze surface and subsurface areas enable better targeting across grade zones, reduce the number of exploratory drilling holes, and renew exploration in defunct mines.

You need to leverage robotic systems such as drones, unmanned aerial vehicles, and self-driving haul trucks to operate in physically demanding environments.

Intelligent automation and intuitive dashboards simplify cost / labor-intensive operations and ensure control of autonomous drilling equipment and field assets.

You require advanced software to increase throughput and recovery, and automate risk assessment and management.

Machine learning algorithms rationalize parts inventory, minimize work overrun, and prevent asset downtime / unscheduled maintenance by predicting overload events, component failure, and the lifespan of industrial equipment.