“Most miners use less than 1% of their data to generate insights”
Unplanned downtime is a huge expense for the mining industry, with Arcweb reporting that 82 percent of machine failures occurring randomly. With an average cost of downtime in the industry at $180,000 per incident, a cost which accumulates if the root cause is not treated. This cost of downtime is in addition to an average loss of production of $130,000 for every hour of failure (Boltstress). Preventative maintenance and time-based methodologies lead to large operating costs with little impact on unplanned downtime.
OPUS learns from all available real-time mine data to provide insights for predictive maintenance, alerting operators to a failure often days or weeks in advance. Contributing factors and root causes allow for accurate maintenance planning and execution, avoiding unplanned downtime and lost production.
Our AI platform OPUS, automates data modelling and model production, with no-coding requiredRead more
With model confidence and accuracy averaging 99%, users have confidence to make decisionsRead more
Our flexible data hosting options accommodate the most remote minesitesRead more
Built to integrate with legacy systems and equipment agnostic so you can obtain insights from your dataRead more
Client supply of historical data and set-up of real-time streaming
Data ingestion and real-time data streaming connection
Client training and model generation
Models in production. Client starts delivering business value
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.Read More
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.Read More
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.Read More
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.Read More
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.Read More
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.Read More
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Downtime is bad for businesses - in fact, McKinsey estimates that "outages typically consume between a third and half of theRead Full Article
Discover the necessary steps to introducing a Predictive Maintenance Program at your plant and realizing the potential valueRead Full Article
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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