Generate value from your latent data

“Most miners use less than 1% of their data to generate insights”


Mining and Resources Digital Transformation

Increase uptime and improve asset performance

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.

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Product Features

Why our customers choose VROC

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Auto AI

Our AI platform OPUS, automates data modelling and model production, with no-coding required

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Accurate Predictions

With model confidence and accuracy averaging 99%, users have confidence to make decisions

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Flexible Data Hosting

Our flexible data hosting options accommodate the most remote minesites

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Built to integrate with legacy systems and equipment agnostic so you can obtain insights from your data

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AI Implementation

Time to value realisation

Client Setup

Client supply of historical data and set-up of real-time streaming

Week 1

Data ingestion and real-time data streaming connection

Week 2 & 3

Client training and model generation

Week 4

Models in production. Client starts delivering business value

Mining and Resources Use Cases

Optimizing Continuous Mining Processes

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.

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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.

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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.

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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.

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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.

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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.

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View Case Studies

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Get started with VROC today

Ready to embark on a pilot project or roll-out the innovation enterprise wide? Perhaps you need assistance integrating your systems or accessing your data? We have a solution to help you as you progress through your digital transformation.