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Mining

MINING Test Subheader

The mining industry, historically characterized by its intensive labor and high operational costs, stands on the cusp of a technological revolution with the integration of Artificial Intelligence (AI). The advent of AI in mining heralds a new era of efficiency, safety, and sustainability, transforming conventional practices into innovative, data-driven processes

Use Cases

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Predictive Maintenance

AI algorithms excel in predicting equipment failure, enabling proactive maintenance. By analyzing data from sensors and machinery, these systems can forecast breakdowns before they occur, minimizing downtime and extending equipment lifespan.

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Resource Optimization

AI-driven analytics facilitate more precise mineral exploration and resource estimation. Utilizing data from geological surveys, AI models can more accurately identify potential mineral deposits and assess the feasibility of mining operations, thus reducing exploratory costs and improving yield.

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Automated Operations

Autonomous vehicles and drilling systems, guided by AI, are increasingly prevalent in mining. These innovations not only enhance operational efficiency but also significantly reduce the risks associated with human-operated machinery in hazardous environments.

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Environmental Monitoring and Compliance

AI assists in real time monitoring of environmental parameters, ensuring compliance with regulatory standards. By analyzing data from various sensors, AI can predict environmental impacts, aiding in sustainable mining practices.

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Safety and Risk Management

The application of AI in monitoring and analyzing data from mining sites substantially elevates safety standards. AI systems can predict and mitigate risks, such as equipment failure or structural collapses, ensuring the safety of the workforce. AI worker safety applications ensure personal protective equipment (PPE) adherence, hazardous area entry detection, worker down, and dozens of other HSE use cases for saving lives, preventing injuries, and protecting the environment.

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Supply Chain and Logistics Optimization

AI enhances the efficiency of the supply chain in mining by predicting demand, optimizing routes for transportation, and managing inventory more effectively, thus reducing operational costs and improving delivery times.

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Data-Driven Decision-Making

By harnessing vast amounts of data, AI empowers decision-makers with deeper insights and predictive analytics, facilitating more informed and strategic decisions in mining operations.

Metrics

The number of fatal injuries in the mining, quarrying, and oil and gas extraction industry rose from 78 in 2020 to 95 in 2021, a 21.8-percent increase. Fatal injuries in the industry were above 100 in the three years prior to 2020: 112 fatalities in 2017, 130 fatalities in 2018, and 127 fatalities in 2019.

Rise of Mining Fatalities

Mining, metals, and other heavy-industrial companies lose 23 hours/month, equating to 1.2 million hours a year across the sector. At $187,500/hour, this adds up to $225 billion annually.

True Cost of Machine Downtime

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