Complex systems, those with many parts, present unique challenges in engineering, product, and process design. Though the individual components may be simple—such as processes on a manufacturing line, decision points in a supply chain, or components of a design—the behaviour of the system as a whole can be extremely difficult to predict and control. Small control changes can produce widely different outcomes, such as revenue, expenditure, efficiency, and failure rate. How can we optimize these systems to significantly improve performance? How can we predict the outcomes when testing in the real world is too risky or expensive?
At Synergetics, we help our clients achieve real gains in efficiency by creating advanced computer models capable of replicating the real-world behaviour of complex engineering, financial, and commercial systems. This enables our clients to test the expected outcomes of new processes, components, management and also control strategies.
Synergetics designed the system model shown above to represent a control system for a furnace. Each block represents a major subsystem, comprising many simple controls and processes. The lines connecting blocks represent the passing of information: from simulated control and instrumentation signals, to physical properties like fluid flow.
In the image above, we took an enormously complex industrial process, with hundreds of interconnected processes, and simulate how a key variable could be better controlled to gain efficiency. By gaining insight into the highly complicated process as a whole, we can both identify how to achieve our clients’ aims, and can show our clients how their new control strategy will perform best.
The figure below shows a visual prediction of how a proposed control strategy can successfully control an important process outcome. One can quickly test strategies and additional controls to improve the process output by modifying and tuning model.
The top graph shows the movement of variable to be controlled (yellow) and the target point (purple) over time. The control system developed to automate this process controls an input variable tracked in the bottom graph. This shows the successful stabilisation and control of the system.
Do you want to improve the efficiency of your own complex systems? Contact us and we’ll show you how.
For further examples of optimisation work, see our design and innovation service page.