To be able to run good workshops with our clients during the restrictions of Covid-19, we have developed Kairos Scenario Maker for testing process models built for Kairos - Control Room Assistant.

With our new tool, it is easy introduce different hypothetical errors to demonstrate and test how the fault propagates through the plant process. A live session with the tool will also provide better understanding of how Kairos can assist your production to safer operation and higher efficiency.

We can also, from historic data, test how plant trips could have been avoided, as well as letting you train your operation team on plant behaviors. The tool helps demonstrate how the causal reasoning works, which is important to ensure that the artificial intelligence becomes explainable.

By using Kairos Scenario Maker User Interface, observations (sensor status) are selected for scenarios chosen to be tested, by simply assigning values outside their limits to see and understand how the AI reasoning works.

SCENARIO MAKER USER INTERFACE

The behavior of your plant caused by these introduced "upsets" (top ranked causes and consequences), are presented in Kairos - Control Room Assistant on screen.

CONTROL ROOM OPERATOR SCREEN  - KAIROS CRA 

Last week, Equinor decided to move forward into phase two with Kairos Control Room Assistant for the Johan Castberg field. Despite challenges with the Covid-19 situation, the project has managed to stay on track and deliver to expectations. The project has adapted to the new situation quickly and found a good way to interact via Teams. To improve the workshops of modelling, a new software tool that supports the creation of fault scenarios has been developed. This tool will be used interactively to test, demonstrate, and explain the behavior of the model, including the Artificial Intelligence in the Control Room Assistant.

Fra Energi24 den 3. juni 2020:

Kairos Technology utvikler teknologi som bruker kunstig intelligens for å styre produksjonen av olje og gass på en mer effektiv måte.

Multilevel Flow Modelling (MFM) is a modern method for modelling functions and goals of complex industrial systems. There are increasing needs for these digital twins modelling the process knowledge. Typical industrial systems in need for such a system is the oil and gas facilities and power production plants.