Hybrid Digital Twin for monitoring and tuning gas treatment unit

Laurent Chec & Bruno Trebucq, Datadvance SAS and CGI France

Abstract:

The proposed paper focuses on an innovative solution to integrate machine learning and artificial intelligence capabilities into a digital twin strategy for monitoring a continuous production asset.

This innovative solution is the result of a collaboration between CGI and Datadvance within CGI's Global Innovation Centre (Toulouse, France), which aims to help industrial players around the world realise the benefits of Industry 4.0 and its enabling technologies, including the Internet of Things (IoT), augmented reality and digital twins.

In the concept of Industry 4.0 and cyber-physical systems, digital twins are based on predictive models and the valorisation of operational data. They provide the key information to monitor and operate physical assets to maximise efficiency.

However, 'predictive models' can be of very different origin and quality, and there is usually no single model required to build the right digital twin. The challenge is then to build, combine and deploy all these models at scale into an appropriate hybrid Digital Twin.

The proposed process allows real-time interaction with a gas production unit. The aim of monitoring such a unit is to detect anomalies, optimise production, reduce energy consumption, and control maintenance.

The predictive model replaces the simulation model for rapid recommendations, typically for control optimisation. It speeds up the generation of results when latency is critical, while maintaining sufficient accuracy.

We will show how the hybrid digital twin is assembled and how it interacts with the physical asset, from data collection and streaming with the IoT, to the feedback loop and operator support with the dashboard.

This project illustrates different scenarios ranging from simple asset monitoring to more complex asset tuning and optimisation under changing operating conditions.