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Manufactures & Factories

Robotic Process Automation

Empowering autonomous flow meter control - reducing time taken for proving of meters

Robotic prehension of objects
For:
Customers, 3 rd parties, end users, community
Scope:
Outputting the end effector velocity and rotation vector in response to the view from a red green blue depth (RGB-D) camera located on a robot's wrist.
Goal:
Improve Operation Efficiency
Adaptable factory
For:
Component suppliers (sensors, actuators), machine builders, system integrators, plant operators (manufacturer)
Scope:
(Semi-)Automatic change of a production systems capacities and capabilities from a behavioural and physical point of view.
Goal:
Improve Operation Efficiency
Empowering autonomous flow meter control - reducing time taken for proving of meters
For:
Process industries; humans
Scope:
Calibration of control devices
Goal:
Other
Powering remote drilling command centre
For:
Oil and gas upstream sector; environment, humans
Scope:
Oil and gas upstream (Deployed in 150 oil rigs and 2,5 billion+ data points each)
Goal:
Other
Order-controlled production
For:
Customer, producing companies, broker
Scope:
Automatic distribution of production jobs across dynamic supplier networks
Goal:
Other
Robotic task automation: insertion
For:
Incorrect AI system use; new security threats
Scope:
Robotic assembly
Goal:
Other
Robotic vision scene awareness
For:
Customers, 3 rd parties, end users, community
Scope:
Determining the environment the robot is in and which actions are available to it.
Goal:
Improve Operation Efficiency
Value-based service
For:
Customer (product user), platform provider, service provider, product provider
Scope:
Process and status data from production and product use sources are the raw materials for future business models and services.
Goal:
Other

Empowering autonomous flow meter control - reducing time taken for proving of meters

For:
Process industries; humans
Goal:
Other
Problem addressed
Reduce the time taken for trial and error methods to set the variable frequency
device (VFD) and flow control valves (FCV) setpoints.
Scope of use case
Calibration of control devices
Description
Cerebra was integrated with the system considering the flow
of fluid. The customer can choose between the available
options of a high flow rate, low flow rate or multi-viscous
flow. Then, with the master meter in the loop of testing, the
meter from the field was introduced to analyse how much
aberration there was and then prove it more efficiently. Since
it took some time to get the exact VFD and FCV percentage
values to achieve the desired flow rate, Cerebras Prognostics
Engine was introduced. Based purely upon machine learning
algorithms, the data models for the VFD and FCV percentages
were used to predict the values to be chosen, with an
accuracy of about 98 %. Since the system was closed-loop,
these predicted values were automatically registered on the
valves monitors, which only required small tweaking in the
end, thus reducing human labour.
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