AI and IoT in Mining: Key Application Areas and Real-Life Examples

AI and IoT in the Mining Indusrty

In mines, people are still irreplaceable as some operations require manual handling. However, the human touch is getting less vital because of rapid advances in automation. AI and IoT play key roles here, and their contribution goes far beyond automation.

When mines go digital, they streamline operations, reduce errors, minimize costs, and generate better business outcomes. Still, many companies miss out on those benefits by using legacy technologies. Consider the following statistics:

The same applies to reducing operational costs. McKinsey predicts that by 2035 AI and IoT will save up to $390 billion for the field annually. Of course, revenue will grow accordingly.

However, according to a study conducted with 100 global mining companies, only half utilize IoT’s opportunities for cost savings. The other half of the companies still suffer losses from these missed opportunities.

Now, let’s zoom in on cases of success to see how vendors have implemented AI and IoT solutions and examine these implementations.

Application Area 1: AI and IoT in Mines to Prevent Downtime

Mining operations consume significant amounts of energy and resources. In fact, this industry accounts for 10% of total global energy consumption. So any inefficiency and stoppage have substantial costs.

To avoid this, service engineers install sensors on mining machinery. Then, engineers monitor operations in real-time as RFID tags, sensors, and labels generate data. Finally, all of this data transfers to the cloud for analysis.

The analysis provides insights into every stage of the mining process. Based on those insights, engineers adjust their operations to reduce the energy (and cost) wastage and prevent downtime and production delays.


Real-Life Example: Monitoring Underground Operations

Newtrax is the global leader in IoT for underground hard rock mines, designing IoT systems to monitor mines and provide insights on underground operations. Some time ago, Newtrax offered its telemetry system to a Canadian mine operated by the global mining company Glencore.

Glencore’s biggest challenge was the long haulage distance trucks had to travel to deliver ore from the mine. As a result, Glencore wanted to optimize operations by maximizing the load on each truck.

Glencore applied the Newtrax system to its fleet. Glencore engineers then started monitoring the production times of individual equipment units and calculating ore haulage utilization and loads per cycle. This process aimed to improve the mine’s efficiency.


Over time, within this mine, Glencore achieved:

  • An increase in loads per cycle of 5%
  • An increase in average tonnage of ore pulled during each outing from 55 tons to 60 tons
  • An increase in overall equipment effectiveness and productivity.
Softeq will help your customers implement a cost-efficient IoT solution to change the way mining happens.

Application Area 2: AI and IoT in Mining to Employ Predictive Maintenance

The mining industry has traditionally found it hard to predict system failures. This unpredictability makes it impossible to precisely determine ideal maintenance timeframes, directly leading to breakdowns, reduced asset uptime, and decreased productivity. These conditions have historically led to increased costs and reduced profitability.

Smart predictive maintenance can come in handy here. Sensor networks integrated into the mining equipment monitor every aspect of mining operations. They also monitor every critical asset parameter such as pressure, vibration, and temperature and trigger alerts before failures occur. 


Remote diagnostics also play an essential role in greater mine productivity. Detecting wear and tear of equipment helps project troubleshooting activities. For mining companies, it means more uptime and fewer financial losses.

Real-Life Example: Improving Maintenance Efficiency

The US-based company OSIsoft is a manufacturer of real-time data management systems. OSISoft’s flagship product, the PI System, uses continuous and historical data insights to determine when to launch equipment maintenance proactively.

Previously, the company relied on a maintenance-on-demand approach. However, this approach was too expensive and fell short of the company’s expectations for effectiveness. Thus, the Cortez Mine in Nevada used the PI system to enhance asset health monitoring for 34 haul trucks. The main goal of this project was to improve maintenance efficiency and cut operational costs.


Since Cortez Mine started utilizing the PI System, it has managed to:

  • Save at least $500,000 each month due to the ability to detect and address failures proactively
  • Reduce the total number of equipment failures by 30%
  • Achieve $600,000 in savings from single early fault detection for one piece of equipment.

Real-Life Example: Increasing Operational Efficiency

Litmus, a company from California, designs IoT platforms for monitoring and inspecting data. One of Litmus’ solutions provides industrial predictive maintenance. Recently, a global mining company turned to Litmus to increase ROI with better operational efficiency. The mining company previously gathered data from its assets with another system, which underperformed due to a lack of interconnectivity between industrial assets.

This global mining company started utilizing the Litmus Edge platform, which collects data directly from programmable logic controllers and sensors. Then, it collects and analyzes information related to machine running parameters. Finally, the system sends real-time alerts to the engineering team. These real-time alerts enable the team to identify deviations early on and take preventive steps. 


By applying the Litmus solution to existing infrastructure, this global mining company states that it:

  • Managed to avoid costs of unplanned downtime estimated at $100,000 per hour
  • Improved business efficiency by 20%
  • Increased ROI by quarter.

Application Area 3: AI and IoT in Mines to Reduce Energy Consumption

Regular technical maintenance of mining machinery consumes significant energy. For example, take the technical adjustments of mining haul truck valves. When companies rely solely on manual labor, this process takes at least 12 hours. In contrast, sensors help identify only those valves that require adjustments. Consequently, the adjustments take no more than three hours. For a large mine, this translates into millions of dollars in savings. 

Ventilation-on-Demand (VoD) is yet another example of reducing operational costs. Statistics prove that ventilation is the largest energy consumer in a mine. It accounts for up to 40% of the mine’s total energy consumption.

With smart VoD systems, companies save millions of dollars in annual energy costs. Sensors monitor air quality and flow and send this data to the VoD system. Then, the system only adjusts the mine’s ventilation in areas where personnel and gear are present. And when AI automatic ventilation adjustment enhances a VoD system, it helps significantly reduce energy consumption and costs.

Real-Life Example: Using Energy Efficiently

Goldcorp, the world’s biggest gold producer, has partnered with Cisco and AeroScout Industrial. They wanted to reduce energy costs and enhance worker safety. For this, the tech duo of Cisco and AeroScout Industrial incorporated a VoD system into Goldcorp’s mines.

The VoD system consists of environmental sensors, AI algorithms, and an interface. First, sensors collect data about the underground ventilation condition. Then, AI algorithms analyze the ventilation requirements in that zone. Based on that analysis, the system adjusts the fan speed and fine-tunes the ventilation. It helps provide the correct amount of air to areas where it is needed. 


As Cisco has introduced the VoD system to the Goldcorp mines, Goldcorp:

  • Cut electrical consumption by half
  • Reduced costs by 20%
  • Safely reduced the mine’s air intake by half (from 1.2cfm to 0.65cfm).

Looking Forward

When a mining business relies on smart technologies to connect people, equipment, and processes, it gets a transparent ecosystem with continuous and careful monitoring. These changes result in an improved budget, optimized operations, and increased productivity. 

You can also achieve these operational and process improvements with an experienced IoT and AI software vendor. And with its long and varied expertise in engineering, project design, and Industrial IoT, Softeq is your ideal partner for planning and implementing IoT in the mining industry.