As control engineers, we’re accustomed to seeing processes through the lens of inputs and outputs. We design our control schemes around this structure and build models accordingly. But what happens when the control scheme changes? Suddenly, we find ourselves revisiting our models to fit the new framework—a time-consuming and complex process.
Enter Equation-Based Models (EQM). 🌟
Unlike traditional models, EQMs don’t rely on predefined inputs and outputs. Instead, they are defined by a series of equations—linear or nonlinear—that relate all the variables. These equations could be based on first principles, machine learning or other system identification techniques.
This allows us to overlay control schemes on top of the model, choosing manipulated and controlled variables freely. Modelling is no longer dependent on the control strategy.
For large-scale projects, like Digital Twin implementations on Ethylene plants or Optimization in refineries, EQMs offer tremendous advantages. They make projects not only easier to implement but also more flexible and maintainable over time. Trust me, we’ve seen it firsthand! 👷♂️👷♀️
Curious about how EQMs can streamline your next big project? SpiroDigitalTwin is what you need. Let’s discuss! 🔗💡
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