FAQs
What is process modelling?
Process modelling is the use of mathematical and data-driven representations to predict how physical processes behave under different operating conditions for design, optimisation, and risk analysis, as a basis for practical engineering and operational decisions.
What is the difference between steady-state and dynamic modelling?
Steady-state modelling describes a process at fixed conditions, while dynamic modelling represents how variables change over time during startups, disturbances, and control actions which is critical when evaluating operability and control behaviour.
Why is dynamic modelling important for complex processes?
Dynamic modelling captures interactions, delays, and non-linear responses that steady-state models cannot, making it essential for operability studies, control design, and risk reduction particularly in complex industrial systems.
What is hybrid modelling?
Hybrid modelling combines first-principles process models with data-driven or empirical methods to represent real processes accurately and efficiently. It is used when physics-based models are too complex or slow, and data-driven models alone are limited by available data. It is also sometimes called semi-empirical or grey-box modelling. Hybrid models are especially useful when data is sparse, when fast simulation is required, or when underlying process phenomena are not fully understood. In practice, hybrid modelling is often chosen to balance model fidelity with development time and data availability.
What is a digital twin?
A digital twin is a time-based digital representation of a specific physical process or asset that is connected to live operating data. It evolves as the physical system changes and is used to monitor performance, predict future behaviour, and support operational decision-making. In its full form, a digital twin represents an individual asset rather than a generic process and may influence how the system is operated. Not every application requires a full digital twin, and value depends on how the model is used.
What is the difference between a process model, digital model, digital shadow, and digital twin?
These terms describe different levels of model integration and purpose. A process model or digital model is a standalone physics-based, data-driven, or hybrid simulation that is not connected to live data. It is typically used for process design, scale-up studies, scenario analysis, and optimisation. A digital shadow is a model that is connected to live plant data and reflects current operating conditions, but it does not send information or decisions back to the process. It is mainly used for monitoring, diagnostics, and prediction. A digital twin is a model of a specific physical asset that receives live data and can provide feedback that supports or influences operational decisions. It evolves with the asset over time. In process engineering, full digital twins are typically reserved for assets where the investment in integration and model fidelity can be justified. For many process applications, digital models or digital shadows are more practical and equally effective. The appropriate choice depends on selecting the level of model complexity required to solve the problem reliably and efficiently.
How does modelling reduce technical and financial risk?
By testing designs and operating strategies virtually, modelling identifies failure modes, performance limits, and cost-drivers before implementation, reducing rework, downtime, and capital risk.
What are the benefits of using models during R&D?
Process models are beneficial during R&D because they reduce uncertainty and shift learning earlier in the project lifecycle. They allow teams to explore how variables interact, identify risks, and test scenarios virtually before committing time, capital, and physical resources. By guiding experiments and focusing effort where it matters most, models reduce technical and financial risk, improve sustainability by limiting unnecessary testing, and accelerate progress from concept to pilot and beyond. They also provide a shared reference across disciplines, improving collaboration and decision-making. Over time, digital models capture and preserve R&D knowledge, evolving as new data is added and supporting better decisions throughout future project stages.
How are bespoke process models developed?
Bespoke models are built in close collaboration with project teams, using client data, actual plant configuration, and defined business objectives, then calibrated and validated against operating or experimental results.
What are the advantages of bespoke models over generic software?
Bespoke models reflect real equipment, data, and operating practices, delivering higher accuracy and more relevant support when decisions depend on real-world behaviour.
