Plug Information Gaps and Uncover Unique Insights into Processes
What are Virtual Sensors?
A set of real-time model outputs based on dynamic and static inputs which can be used in operational and financial decision-making and automated control.
Typically based on both dynamic and static inputs, the combination of physical modeling and machine learning is applied to gain an increased understanding of the current condition of assets, and what the future condition will be under a given set of control variables.
They differentiate from “soft sensors” as they are processed in the cloud thus avoiding instrumentation investment, and so benefit from constant retraining and re-calibration.
Made for Your Mining Challenges
Lack of Data Certainty Limits Process Efficiency
Mine sites typically have limited ability to assess their data quality, and time is often wasted dealing with multiple versions of the same data circulating in various systems and spreadsheets.
Physical sensors placed in harsh environments are easily damaged and often go out of calibration.
How it Helps
Digital Sensors for Current and Future Process States
By using a combination of physical modeling and machine learning, the virtual sensors increase understanding of processes’ current conditions and what future states will be under a given set of control variables.
Combined with the material handling model they provide values for the parameters of interest never seen before (including uncertainty).
What you Get
Greatly Increased Process Visibility
The set of virtual sensors improves understanding of the process and its parameters.
They are capable of predicting, simulating, and combining various data streams in real-time to uncover hidden states & real-time process KPIs.
Virtual Sensors in Action
The IntelliSense.io Virtual Sensor Portfolio has been developed and refined over several years at multiple customer sites, creating an ‘out-of-the-box’ product that can be rapidly deployed to deliver value within weeks.
Using Virtual Sensors it is possible to gather data in places where physical sensors would not be possible, for example, within a Grinding Mill. Virtual Sensors can also reduce the need for additional sensors along water pipelines and other equipment.
tonnes of material processed
petabytes of data processed
Mine to Market: Value Chain Optimization
Our suite of real-time decision-making applications use Scientific AI to optimize each process; from mine to market.
Our Material Handling model connects these applications to drive even greater efficiency.
Our process optimization apps can be deployed on a specific process bottleneck or expanded across the entire value chain.
They are powered by our Scientific AI Decision Intelligence Platform, brains.app.