Our ;monitoring, modelling and prediction capabilities give us a great understanding of where an asset operation is and where it can go. From here we start to apply real-time optimisation techniques to make and sustain significant improvements and thus systems performance.
Once there is an accurate digital replication of an operation’s behaviour, it can be used to find opportunities to improve. This can be done through what-if analysis at:
- An operations level
What if I changed this operational setting or set-up? - An asset level
What if I changed the behaviour of this asset through maintaining, repairing, refurbishing, replacing or renewing with new technology? - A system-level
What if I changed the configuration of my system?
How do we do it?
At Synengco, we can calculate the asset’s working efficiency using a real-time, whole-of-system simulation running on instrumented data. We can then learn the cause and effect that operator control points have on system efficiency.
Synengco has built and deployed our SentientSystem® as a platform for Real-time Optimisation (RTO) on critical infrastructure with Distributed Control Systems (DCS) capabilities all around the world. Our customers use the platform to capture and operationalise asset knowledge. This ensures that best-practice decision making is automated to reduce costs and increase capacity.
- SentientSystem®calculates the assets working efficiency using a real-time, whole-of-plant simulation running on instrumented data.
- It then learns the cause and effect that operator control points have on system efficiency.
- Optimisation algorithms are then applied to maximise whole-of-system efficiency by modifying operator control points subject to constraints.
Synengco has been working closely with critical infrastructure clients for 20 years using our four pillars of capabilities: Model, Monitor, Predict and Optimise.
The high criticality, dynamics and complexity mean we are constantly faced with difficult and unique problems to solve, ensuring that our development stays cutting-edge. Real-time optimisation can reduce operating costs and greenhouse gas emissions through autonomous “machine learned” control.
Using a digital twin of your asset with our predictive analytics we can simulate different modes of operation and find the most energy efficient way to keep your asset working.
To learn more, read our case studies on real-time optimisation