brains.app is a real-time decision-making platform for the natural resource industry powered by Artificial Intelligence and Industrial Internet of Things (IIoT) technology. It provides a wide range of capabilities, on its own or with optimization as a service application, for the entire mine to the market value chain.
One of the core pillars of brains.app platform is functions - the computational engines that provide users with new information required to reach the desired outcome. Examples of such engines include Thickener Neural Network Model, Gas Holdup Model, and many others. Full brains.app platform deployment enables users to maximize the value from these sophisticated models and their continuous calibration.
The Fluorite'20 release introduces a set of new brains.app Platform features to improve data visualization techniques.
Along with an opportunity to run sophisticated optimization models available in brains.app inside OT environments.
We introduce brains.app lite - a new deployment configuration that enables customers to realize the benefits of optimization models running on local servers. It is most appropriate for deployments with strict security requirements limiting access to external cloud-based resources, such as inside OT networks with access to control systems.
You can deploy brains.app lite a cut down version of the platform that includes only components required for processing data and providing optimization with limited visualization and configuration functionality. In the hybrid deployment, new models can be run in parallel on the cloud, tested, and validated before being deployed locally through secure channels such as VPN.
brains.app lite enables customers to realize the benefits of optimization models running on local servers, inside OT networks with access to control systems to close the loop.
LIVE 2 State Indicator Widget
This dashboard widget is a data visualization method that displays a different color and sound depending on the state of the data.
This widget helps to emphasize the brains.app binary metrics such as an alert or a LIVE notification with color and sound.
The SAG Mill operators can now get notified with this audiovisual alert every time brains.app anticipates an overload, to be able to respond faster.
The Thickener Operator can also be alerted when the Bed Pressure is predicted to reach an upper limit, so they can adjust the control variables with the brains.app recommendations.
Learn more about configuring the Widget - How to set up a 2 state indicator widget?
Contextual Data Type -Interval Data and Interpolation Hold
brains.app support a varied set of Data Sources that covers the geological, mining, and processing information required to generate the variables used in the rest of the platforms.
It now supports two new variable data type and they are:
- Interval Data
- Manual Data with (Data Interpolation-HOLD)
Interval Data Type :
Support of the interval data type allows manual entry of sequential dated data to capture asset information within arbitrary time intervals.
Learn more about How to input interval data from the dashboard?
Manual (Data Interpolation) Data Type :
Data Interpolation is the process of estimating unknown values by constructing new data points within the range of a discrete set of known data points. When data is interpolated, a constant line is constructed between two known data points.
This data type when applied holds the value until the next change to construct trends for easier evaluation and differentiation.
In the Grinding Application, the SAG Mill Operators can add the liner wear measurements done LIVE as contextual data to brains.app with Data Interpolation and hence be able to track the Liner measurements assuming the continuous wear over time.
Learn more about the configuration with How do I represent my metric data points with straight lines?
Time Shift Equations
You can use time shift function for equation based metrics. General form of the time shift function is:
where ‘time_shift’ is measured in minutes and it can be negative, to reference time that is ‘time_shift’ minutes in the past relative to the current time, or it can be positive to reference time that is ‘time_shift’ minutes in the future relative to the current time.
To illustrate the potential value of the time shift function, we can create two metrics that calculate maximum and minimum values in the last ten minutes for the “Flow Rate (Feed) (Predicted Neural Network)” metric from the above example. Please note that we use “min” and “max” functions to calculate values for new metrics along with the use of time-shift up to 10 minutes in the past.
Below is an example of a line widget displaying all three metrics: source feed, max value, and min value in the last 10 minutes.
Learn more about using the Equation Function.
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