Artificial Intelligence and Machine Learning in Mining, Energy and Resources

Artificial Intelligence and Machine Learning are transforming the mining and energy sectors by enhancing yield, speeding up production, and improving workflow and operational efficiency. Their adoption has notably improved safety by enhancing risk management and safety procedures, facilitating real-time monitoring, and enabling predictive analytics to preemptively address hazards, aligning with the industry’s goal of achieving zero harm.

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Predict and Prevent OH&S Incidents Using Artificial Intelligence and Machine Learning

Monitor

Monitor Real-time IoT and Machine Vision-based alerts (including personal protective equipment, hazards, and mobile equipment risk) to ensure safety compliance. Use Digital Twins for real-time visualisation and scenario modeling.

Predict

Predict OH&S incidents by creating forward-looking machine learning-based ‘Dynamic Employee Risk Profiles’ to calculate employee’s chances of being involved in an incident on any upcoming day.

Intervene and Prevent

Prevent incidents by retraining, proactively removing employees from potentially dangerous situations, and sending live compliance alerts to employees to immediately eliminate dangerous working conditions.

Real Time + Sensor Based IoT Monitoring

Noise, Vibration, Movement, Lighting, Hydration

Hydration, Fatigue, Inattention

Temperature, Machine Speed, RPM, Torque, Coolant Temperature

Chemical Detection, Air Quality, C02

Real-time data on Recent Hours Worked, Overtime/Sick Leave, Role Risk Rating, Years of Experience

Machine Vision Based Monitoring

Monitor proper use of safety equipment

Understand complex video events in real-time

Optimise Operations & Plant:

We can expedite the delivery of your outcomes using IP and accelerators.

Optimise Throughput and Smart Preventative Maintenance

Artificial Intelligence (AI), ML, Machine Vision, and Data Science-based mining circuit optimisation, using real-world physics simulators, deep learning (ML), and gaming engines to understand and monitor mining circuit inputs, settings, and materials to optimise throughput. Sensors can also be deployed to monitor asset health and alert operators for conditions that typically preceded downtime events in the past. Similar projects generate ROIs greater than 900% or $90m annually in some cases.

Location Awareness

IOT-based Location Awareness and Analytics to understand team locations, asset utilisation, and interactions. This results in the optimisation of plant, equipment, and people (including cost reductions, time-saving by minimising proximity for next likely use, scheduled cleaning, and maintenance).

Asset Safety

Predict catastrophic equipment failure that could lead to injury or fatality by understanding causes of failure, the likelihood of failure, and predict failure events. Identify defective equipment using machine vision and IoT and determine safe asset use via statistical analysis and optimise asset safety. Use Critical Control Monitoring to identify asset and process failures in advance.

Risk Culture, Safety Culture, Engagement & Psychological Wellbeing Surveys

Various surveys across mining to identify areas of risk or concern based on questions around Risk Culture, Safety Culture/Compliance, Engagement and Phycological Health. Results facilitate drilling to problem departments, locations and sited to focus training and remediation activates and maximise training/remediation outcomes.

Automated Site Assessments

Machine Vision to generate and deliver automated building and site progress data and safety insights. The project involves using robots, drones, and other autonomous equipment to monitor sites to minimise human movement/risk and maximise reporting accuracy.

Mining and Resources Specialisation

We’ve collectively delivered exceptional value/ROI in Mining and Resources:

Dr. Joe Walsh

Physics, PHD, Dip. Management, Dip. QA; Over 50 International Presentations, Over 100 Peer Reviewed Research Papers;

Head of Data Science and Data Engineering

Kris Hampel

Eng (Hons) Major in Mining Engineering; Various mining roles including Drill & Blast Engineer (Downer EDI), Engineering Consultant (MEC Mining)

Project Lead and Technical Consultant

Rad Anandakumar

Comm, MBA, GAICD; Various mining projects (South 32, Newcrest, FMG, MMG, St Barbara, Evolution Mining, RIO, PYBAR, Anglo Gold, Adaro)

Management Consultant & Data/AI Strategist

Collective Mining Project Experience

Reducing mining risks through predictive analytics around OH&S to prevent workplace incidents (WA HQ Mining).
, ML, Machine Vision and Data Science based mining circuit optimisation platform, using real world physics simulators, deep learning (ML) and gaming engines generating annual savings in excess of $50m (VIC HQ Mining).
Machine Learning to reduce copper production circuit wastage (WA HQ Mining, Minerals, Metals).

Mining – Preventative maintenance using temperature, speed, vibration, moisture and other variables (VIC HQ Mining).
Mobile Equipment Safety (PH Mining & Energy– in progress).
PoC ingestion, reporting and analysis for production levels against geological and blast fragmentation models with integration for fleet telemetry (QLD HQ Mining).
IoT Edge solution to prevent excess time on site whilst core samples were being analysed (VIC HQ Mining).

Drill and Blast Optimisation to balance blasting cost and production dig rate to deliver cost reductions (NSW HQ Mining).
Data Estate Modernisation, Azure, GCP, AWS, data lake, data warehouse, Power BI, Databricks, ML, AI, automating data from historians and mining equipment, master data management, real-time streaming and more (Various Mining, Minerals, Energy, Metals).
Risk Culture, Safety Culture, Engagement & Psychological Wellbeing Surveys.

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info@aiconsultinggroup.com.au