Process simulation and modelling

What is the difference between them?

"Process simulation is a subset of process modelling"

The 'process lifecycle'

A process can be considered to have a lifecycle, from the time it is conceived (conceptual design), through the various R&D and engineering phases to commissioning and operation and finally decommissioning

A powerful general-purpose process modelling tool such as gPROMS has a role to play in every one of the stages, not just simulation of the process end engineering designs.

Process simulation and process modelling refer to different things.

Process modelling is the art or activity of building a mathematical model of the process (or of a product, for that matter) by describing its fundamental physical and chemical relationships – without specifying how they are to be solved.

Process simulation is merely one of the activities that you can perform with that process model.

Process simulation is often an exercise in 'molecule accounting', and as such is often performed by relatively junior engineers.

Construction of a high-accuracy process model, on the other hand, requires deep modelling and process expertise, and is usually performed by an experienced specialist – sometimes working in conjunction with R&D personnel.

What does this mean in practice?

Applying the many different capabilities of a true process modelling tool such as PSE's gPROMS to first-principles models allows you to gain high-accuracy predictive information for a unit or process.

By contrast, much process simulation is carried out using "off-the-shelf" models that provide little competitive advantage, or purely steady-state models that do not capture the complexity of process operation.

Having said that, process simulation is a valuable and essential activity, which can be significantly enhanced by using high-accuracy customer models of the process to capture corporate knowledge and gain true competitive advantage.

What can you do with a true process model that you can't with a simulation?

Consider, or example, a detailed model of a fluidised bed reactor and its surrounding flowsheet.

With this model you can of course perform steady-state and dynamic simulation runs to see what happens if feed conditions are varied. What is more, you can do that with a high-accuracy custom model that closely reflects your actual process rather than somebody else's.

However with the model you can also:

estimate parameters using gPROMS model-based data analysis and validation techniques on that model, against experimental data. This can enhance predictive accuracy significantly, and provides information that can be used in formal risk analysis.
design experiments to refine the parameter estimations and reduce the risk associated with measurement inaccuracy.
perform optimisations – dynamic or steady-state – on the model, to directly calculate optimal trajectories or values rather than undertaking lengthy trial-and-error invetigations.
generate linearised models for use in control design applications or Model-based Predictive Control (MPC), gain scheduling or any other activity that requires linear models.
because this is a model and not a simulation, simulate 'backwards' to find out what feed or unit values give rise to the desired product qualities, at no additional cost in terms of execution time or complexity of model.
generate an equation-set object (ESO) for other software – for example, plant-wide optimisers – to use

Only with a process model will you be able to perform all the activities required to model across the process lifecycle, from conceptual design and laboratory experimentation through detailed engineering design to operation.