AI in Mining: How BHP is Achieving 3 Gigalitre Water Savings and Autonomous Operations

BHP, one of the world's largest mining companies, has deployed artificial intelligence across its global operations—from mineral exploration in Australia and the United States to autonomous shiploaders at Port Hedland and smart safety systems at its Escondida copper mine in Chile. The results demonstrate AI's capacity to address mining's most pressing challenges: operational efficiency, safety, resource conservation, and environmental sustainability.

At Escondida alone, AI technology has saved more than 3 gigalitres of water and 118 gigawatt hours of energy since FY2022. Across BHP's West Australia Iron Ore operations, predictive analytics run on most load and haul fleets globally, reducing equipment failures and minimizing costly downtime. These implementations show AI's evolution from experimental technology to essential operational infrastructure in mining.

AI Across the Entire Mining Value Chain

Mining generates vast quantities of data from sensors, monitoring systems, geological surveys, equipment telemetry, and operational processes. AI systems analyze these data streams to identify patterns, optimize processes, and enable informed decision-making across the entire value chain—from exploration and extraction through processing, logistics, and customer delivery.

BHP's AI applications span multiple operational domains:

• Exploration: Machine learning algorithms analyze geological data to predict mineral deposit locations, reducing unnecessary drilling
• Extraction: Real-time algorithms assess geological conditions and optimize extraction techniques to maximize yield and minimize waste
• Predictive Maintenance: Analytics models monitor equipment health and predict failures before they occur
• Energy Optimization: Machine learning identifies opportunities to reduce power and water consumption
• Autonomous Operations: AI-controlled vehicles and machinery operate in high-risk areas, improving safety
• Environmental Monitoring: Acoustic monitoring and satellite imagery combined with AI detect environmental changes and endangered species

This comprehensive deployment reflects AI's capability to address diverse challenges within a single industry, delivering measurable improvements in efficiency, safety, and sustainability.

Precision Exploration: Finding Copper Deposits with Machine Learning

The global energy transition requires massive increases in copper, nickel, lithium, and other critical minerals. Discovering new economically viable deposits is increasingly challenging as easily accessible resources are depleted. AI is accelerating exploration by analyzing geological data more comprehensively than traditional methods allow.

BHP has used machine learning coupled with human expertise to discover new copper deposits in Australia and the United States. The technology processes extensive geological datasets—surveys, soil compositions, historical extraction records, geophysical measurements—to identify patterns indicating potential mineral deposits.

This capability reduces exploration costs and environmental impact by minimizing unnecessary drilling and excavation. Rather than systematic drilling across large areas, AI narrows search zones to locations with the highest probability of containing economically viable deposits.

Advanced Detection: Muon Tomography and Deep Scanning

BHP is advancing beyond conventional exploration technologies to muon tomography—using cosmic radiation to scan and map underground deposits faster and more accurately than traditional methods. Muons are subatomic particles that penetrate deep underground, providing information about subsurface density variations that may indicate mineral deposits.

BHP's alliance partner, Ivanhoe Electric, utilizes machine learning and data analysis to detect sulphide minerals containing copper, nickel, gold, and silver at depths exceeding 1.5 kilometers. Ivanhoe Electric's proprietary Typhoon™ electrical geophysical surveying transmitter generates highly stable signals that produce exceptional signal-to-noise ratios. Their CGI machine learning algorithm and data inversion software interprets this data with precision that minimizes land disturbance and preserves ecosystems.

This technological approach addresses both operational and environmental objectives—identifying deposits more accurately while reducing surface impact from exploration activities.

Predictive Maintenance: Preventing Failures Across Global Fleets

Maintenance represents one of the largest operating cost drivers in mining. Unexpected equipment failures create production disruptions, safety risks, and expensive emergency repairs. Predictive maintenance shifts from reactive repairs or time-based schedules to condition-based interventions informed by AI analysis of equipment data.

BHP operates predictive analytics models across most load and haul fleets globally and materials handling systems. A small team in BHP's maintenance center of excellence develops and maintains these models, which provide real-time and long-range indications of machine health and potential failures.

The models analyze data from sensors monitoring:

• Vibration patterns indicating bearing wear or misalignment
• Temperature fluctuations suggesting cooling system issues or excessive friction
• Hydraulic pressure changes signaling pump or valve degradation
• Power consumption variations indicating motor or drive system problems
• Operational patterns that stress components beyond normal parameters

Case Study: West Australia Iron Ore Materials Handling

At BHP's West Australia Iron Ore operations, one materials handling facility experienced ongoing vibration and handling impacts threatening to shorten the structure's lifespan. BHP's technical centers developed a scalable framework processing hundreds of gigabytes of sensor data to diagnose and solve the problem.

The analysis identified root causes of the vibration issues and enabled targeted interventions. More importantly, the framework allowed BHP to identify other locations in fixed plant structures with similar risk profiles, implementing preventive changes before problems manifested.

This proactive approach delivers multiple benefits:

• Scheduled maintenance during planned downtime rather than emergency repairs
• Extended equipment lifespan through timely interventions
• Reduced risk of catastrophic failures that threaten worker safety
• Lower maintenance costs through planned rather than emergency work
• Improved production reliability and consistency

Energy and Water Optimization: 3 Gigalitres and 118 Gigawatt Hours Saved

Mining is energy and water intensive, creating both cost pressures and environmental concerns. AI's ability to optimize resource consumption directly addresses both challenges. BHP CEO Mike Henry highlights the results at Escondida: "Artificial intelligence technology at processing plants within our Escondida copper mine in Chile has helped save more than three gigalitres of water – as well as 118-gigawatt hours of energy, since FY22."

These savings are substantial:

• 3 gigalitres of water equals approximately 1,200 Olympic-sized swimming pools
• 118 gigawatt hours of energy could power roughly 16,000 average homes for one year

The AI technology provides real-time options enabling operators to implement water optimization plans and delivers real-time analytics on large volumes of energy usage data. The system identifies anomalies and automates corrective actions to optimize concentrators and desalination plants' energy and water consumption.

How AI Achieves Resource Optimization

Machine learning algorithms analyze operational data to improve process efficiency:

Energy Consumption Analysis: AI identifies patterns in energy usage across different operational modes, shifts, and external conditions. The system recognizes when equipment operates inefficiently and recommends adjustments.

Water Circuit Optimization: In mineral processing, water circuits recycle water through multiple stages. AI optimizes flow rates, chemical dosing, and separation processes to minimize fresh water requirements while maintaining processing efficiency.

Predictive Adjustments: Rather than reacting to consumption increases, AI predicts when changes in ore characteristics, weather conditions, or production rates will require adjustments to maintain efficiency.

Automated Responses: For routine optimizations, AI implements adjustments automatically rather than requiring constant operator intervention. This enables 24/7 optimization that human operators cannot maintain.

These capabilities are particularly critical in water-scarce regions like Chile's Atacama Desert, where Escondida operates, and support mining's path toward net-zero emissions by reducing energy consumption and associated greenhouse gas emissions.

Autonomous Operations: Remote Control and Self-Driving Systems

BHP's West Australia Iron Ore operations demonstrate AI-enabled autonomy at massive scale. As one of the world's largest and lowest-cost iron ore producers, WAIO operates multiple mines and mine hubs in the Pilbara region, all connected by railway to port facilities with conveyors, loaders, and trains controlled through a remote operations center.

The operational complexity exceeds human capacity for real-time optimization across all decision points. BHP uses AI as a decision support system where team members make ultimate decisions supported by AI's computational capabilities.

Automated Shiploaders: 280 Million Tonnes Annually

BHP operates eight automated shiploaders at its Port Hedland export facility, controlled remotely from the Integrated Remote Operations Centre in Perth. These shiploaders load approximately 1,500 bulk ore carriers annually, exporting roughly 280 million tonnes of iron ore to global customers.

Automation has increased production by more than one million tonnes annually through:

• Greater precision in loading operations
• Reduced spillage and product loss
• Faster load times per vessel
• Equipment optimization and reduced wear

The shiploaders operate with minimal human intervention, handling vessels worth millions of dollars in cargo with precision that manual operations cannot match consistently.

Fully Autonomous Mine Trucks

In 2024, BHP converted all mine trucks at its Spence operation from manual to fully autonomous. This conversion has unlocked safer and more efficient operations:

Safety Improvements: Autonomous trucks eliminate human drivers from one of mining's highest-risk activities. No drivers means no risk of operator fatigue, distraction, or error in heavy equipment operation.

Operational Efficiency: Autonomous trucks operate continuously without shift changes, breaks, or fatigue-related performance degradation. They follow optimal routes consistently and maintain steady speeds that maximize fuel efficiency.

Predictive Performance: Autonomous vehicles generate continuous data streams about their operation, enabling AI systems to optimize routes, speeds, and loading/unloading procedures in real-time.

Safety Enhancement: Smart Hard Hats and Wearable Monitoring

AI-integrated wearable devices monitor miners' health and safety conditions in real-time, providing alerts when concerns arise. These systems track heart rate, fatigue levels, exposure to harmful substances, and environmental conditions.

At Escondida, BHP integrated smart hard hat sensor technology that measures truck driver fatigue by analyzing brain waves. The system seeks to prevent accidents related to driver drowsiness—a major cause of incidents in mining operations where drivers operate heavy equipment during long shifts, often at night.

When the system detects fatigue indicators, it alerts both the driver and supervisors, enabling intervention before fatigue leads to accidents. This represents a fundamental shift from reactive incident response to proactive risk prevention.

Wearable monitoring extends beyond fatigue detection:

• Heat stress monitoring in high-temperature environments
• Toxic gas exposure alerts in underground operations
• Fall detection and emergency location identification
• Proximity alerts near dangerous equipment or exclusion zones

Environmental Monitoring: AI and Acoustic Technology

BHP's environment team utilizes AI to support and extend subject matter expertise in ways traditional methods cannot match. The demands for rapid, scalable, and precise environmental reporting exceed the capacity of conventional monitoring approaches.

Applications include:

Acoustic Monitoring: AI analyzes audio recordings to detect endangered species calls, enabling wildlife monitoring across vast areas without constant human presence. Machine learning models recognize specific species' vocalizations, tracking population movements and habitat usage.

Satellite and Drone Imagery: AI combines imagery with machine learning for object detection—identifying invasive weeds, monitoring vegetation health, detecting unauthorized access, and tracking rehabilitation progress.

Outlier Detection: AI identifies anomalies in environmental monitoring data that may indicate problems requiring investigation—unusual water chemistry, unexpected emissions, or habitat disturbances.

Pattern Recognition: Machine learning identifies long-term trends in environmental data that inform adaptive management strategies and demonstrate regulatory compliance.

These capabilities enable environmental monitoring at scales and frequencies that human teams cannot achieve, supporting both regulatory compliance and corporate sustainability commitments.

AI as Decision Support: Augmenting Human Expertise

Across BHP's operations, AI functions as decision support rather than autonomous decision-making. Humans make final decisions, but AI's computational capacity analyzes variables and presents options more comprehensively than humans can process independently.

This approach recognizes that:

• Mining involves complex judgments requiring human experience and intuition
• Operational contexts include factors AI systems may not fully capture
• Accountability for decisions must rest with human decision-makers
• Worker acceptance of AI requires maintaining human agency

At WAIO's remote operations center, operators manage railway networks, processing facilities, and port operations spanning hundreds of kilometers. AI systems process operational data and present recommendations, but operators decide whether to implement those recommendations based on their understanding of current conditions, upcoming maintenance plans, weather forecasts, customer requirements, and numerous other factors.

This human-AI collaboration leverages the strengths of both: AI's capacity for rapid analysis of vast datasets, and human operators' contextual understanding, judgment, and adaptability to unexpected situations.

The Competitive Advantage of AI-Enabled Mining

BHP frames AI not just as an efficiency tool but as essential for competitive survival. As CEO Mike Henry states, the opportunities for AI to deliver greater cost efficiency are significant, but more importantly, AI makes work environments safer.

A safer, more productive mine site represents competitive advantage in multiple dimensions:

Cost Competitiveness: Reduced maintenance costs, optimized energy consumption, and improved productivity lower operating costs—critical in commodity markets where price takers must minimize costs to remain profitable.

Safety Performance: Lower injury rates reduce workers' compensation costs, improve workforce morale and retention, and enhance corporate reputation with regulators and communities.

Environmental Performance: Reduced water and energy consumption, minimized land disturbance, and comprehensive environmental monitoring support sustainability commitments and maintain social license to operate.

Operational Reliability: Predictive maintenance and optimized operations reduce unplanned downtime, ensuring consistent production that meets customer commitments.

Innovation Capacity: Organizations developing AI capabilities build technical expertise that enables continuous improvement and adaptation to emerging challenges.

The Path Forward for Mining AI

BHP's implementations demonstrate that AI in mining has moved beyond pilot projects to enterprise-wide deployment delivering measurable operational improvements. The technology addresses mining's fundamental challenges while supporting the industry's role in the global energy transition.

For mining companies evaluating AI adoption, BHP's experience suggests several key considerations:

Comprehensive Deployment: AI delivers maximum value when implemented across the value chain rather than in isolated applications. Exploration, extraction, processing, logistics, and environmental monitoring all benefit from AI capabilities.

Data Infrastructure: Effective AI requires quality data inputs. Investments in sensors, connectivity, and data management systems are prerequisites for AI success.

Human-AI Collaboration: Positioning AI as decision support rather than replacement builds workforce acceptance and leverages both computational power and human judgment.

Measurable Objectives: Define clear performance metrics—water savings, energy consumption, equipment uptime, safety incidents—and measure AI impact against these benchmarks.

Scalable Frameworks: Develop solutions that can be applied across multiple sites and operations rather than one-off implementations for specific problems.

The global energy transition requires massive increases in mineral production while reducing mining's environmental footprint. AI provides tools to achieve both objectives—finding deposits more efficiently, extracting resources with less waste, optimizing energy consumption, and operating more safely. BHP's results demonstrate these capabilities at scale, providing a roadmap for the mining industry's AI-enabled future.