A model has a simple job: be an accurate representation of the real-life process or plant it was developed to simulate. In the quest for increased model fidelity and efficiency, the choice between first-principle and empirical models has long been debated. There is, however, a third option that combines the strengths of both: hybrid models. Let’s delve into three scenarios where hybrid models prove their worth.
When You Need to Extrapolate
Empirical models thrive on data, but what if your dataset is limited or doesn’t fully represent the process? Hybrid models step in to bridge this gap. Take, for instance, sampling campaigns in industrial plants. Often, these yield only a snapshot of data, making it challenging to predict all possible process dynamics. Hybrid models offer flexibility, blending empirical insights with fundamental principles to provide reliable extrapolations.
When Speed is Essential
First-principle models offer deep insights but are notorious for their complexity and computational demands. Enter hybrid models, often offering a more reasonable solution time. In applications like process control, where fast simulations are crucial, hybrid models shine. They strike a balance between speed and accuracy, making them ideal for rapid decision-making and training scenarios.
When the Underlying Phenomena are Not Well-Understood
Especially when modelling aspects such as chemical behaviour, understanding every underlying phenomenon isn’t always feasible. For example, many reaction mechanisms are not understood well enough or are too complex to be modelled from first principles. Here, hybrid models excel by incorporating both empirical data and fundamental principles. They offer a pragmatic approach, accommodating uncertainties and complexities while providing actionable insights.
Hybrid modelling represents the convergence of precision and adaptability. By blending the strengths of first-principle and empirical models, it offers a versatile solution for diverse challenges. Whether you’re navigating limited data, need quick simulations, or are grappling with complex phenomena, hybrid models are the way forward.
Ready to harness the power of hybrid modelling for your process? Fill in the form and one of our experts will connect for a tailored solution.