The COGNITWIN Toolbox is structured according to a defined Digital Twin pipeline, comprising a set of different Digital Twin (DT) supporting components. These component will typically be connected and configured together in different ways for different pipeline instances in various application contexts.
As shown in the figure, four main steps for the pipeline have been defined, corresponding to 1) DT data acquisition tools & services, 2) DT representation tools & services, 3) DT analytics tools & services, and 4) DT visualization and control tools & services that will be explained below.
Digital Twin Pipelines
Emission (Gas Treatment Center) temperature and fan control
Demonstrated through a pipeline implementation in a Hydro Aluminum plant.
Temperature in tapping stream and Slag in refining ladle
Demonstrated through a pipeline implementation in an Elkem Silicon plant.
Predictive Maintenance of Machinery
Demonstrated through a pipeline implementation in a Noksel Steel plant.
Rolling Mill Tracking System
Demonstrated through a pipeline implementation in a Saarstahl Steel plant.
Hybrid model for ladle refractory wear
Demonstrated through a pipeline implementation in a Sidenor Steel plant.
Boiler fouling management
Demonstrated through a pipeline implementation in a Sumitomo Boiler installation.
Digital Twin Data Acquisition Tools & Services
Cybernetica OPC-UA Server
Big Data Pipelines Deployment Framework (BDPDF)
Digitial Twin Representation Tools & Services
Trusted Factory Connector (IDS)
Digital Twin Analytics Tools & Services
First Order Models
Cybernetica Cognitive CENIT
Digital Twin Visualization and Control Tools & Services