Optimizing Mining Operations with AI

Predictive maintenance is proving to be beneficial to gold mining companies. Connecting real time data from across multiple critical assets, such as pumps, fans, SAG and Ball mils, companies can get predictive insights using Auto AI. The AI models detect when equipment is deviating from normal operating conditions, alerting operators who can further investigate and plan necessary maintenance activities proactively, helping to increase reliability and production rates.

Increasing reliability and decreasing downtime for copper mines has a significant effect on profitability. Equipment such as mining shovels and mill motors can be included in overall site AI analysis to predict when equipment is deviating from normal operating conditions. The AI can predict a time to failure, along with root cause of failure helping mining operators schedule maintenance and order the correct spares. Maintenance implemented correctly and on-time can reduce on-going reliability problems and bring down maintenance costs.

Meeting increased demand and ensuring mining operations are sustainable are both critical for lithium miners. AI can help maintain the reliability of continuous mining processes, with early detection of equipment degradation and time to failure predictions. AI can also help operators optimize their processes, with the view to improving safety and reducing energy and water consumption.

Mineral processing plants use a range of complex processes that rely on heavy machinery and equipment. The reliability of this equipment is essential, as break-downs can bring a halt to production across the plant. AI can help mineral processing plant operators with real time monitoring and predictive analysis which detects equipment degradation and failures in advance. These critical insights can help avoid unplanned shutdowns and lead to early intervention, minimising costs and production loss.

The careful management of tailings storage facilities is essential to ensure on-going safety and environmental compliance. Through the continuous analysis of all available data, including water levels, drainage, overflow, discharge, structural integrity and even the weather, OPUS can provide tailings operators with real-time monitoring and future insights for improved safety and on-going compliance.

Reducing the carbon footprint and improving ESG reporting for mining operators is more important than ever. Auto AI can provide holistic analysis across an entire enterprise, giving insights to help guide strategic decisions as well as insights at an operational level to help reduce energy consumption, improve asset reliability and reduce wastage. Sustainability objectives can be modelled using the no-code AI wizard and users can build dashboards and reports, with up-to date insights for continuous improvement.