Advanced Process Modelling

High-quality information for decision support

Advanced Process Modelling^® involves applying detailed, high-fidelity mathematical models of process equipment and phenomena, usually within an optimization framework, to provide accurate predictive information for decision support in process innovation, design and operation.

Advanced process models are used to explore the process decision space to enable better, faster and safer decisions by reducing uncertainty. The approach differs significantly from that of traditional process simulation.

Example

Integrated oil company Repsol applied advanced process modelling techniques to a new petrochemical process design that involved a complex multitubular catalytic reactor and an extensive separation system, connected by major recycles.

The combination of advanced process models and optimization techniques resulted in an improvement in process economics of tens of millions of Euros over the original design, which was performed using traditional process simulation techniques.

There are many similar examples in all areas of the process industries. Typical application areas are those that involve complex physical and chemical phenomena, such as reaction engineering, crystallization or complex separation processes, as well as areas such as fuel cell component and system design.

These processes are often at the heart of the value chain, and thus where the most value can be realised.

What does Advanced Process Modelling involve?

Advanced process modelling is a combination of three elements:

• mathematical models based on chemical engineering first-principles theory
• experimental data – laboratory, pilot or operating plant – used to fit the empirical parameters in the model (or ‘validate’ the model)
• advanced solution techniques – for example, optimization – to exploit the rich information in the model and its predictive capability.

Much of the predictive power of advanced process models results from the combination of first-principles chemical engineering, physics and chemistry with observed (“real-life”) data.

A properly-constructed model will have predictive accuracy well beyond the area in which it was fitted, allowing – for example – scale-up, optimization of processes for different operating conditions.

Where are models applied?

Unlike traditional process flowsheeting, or so-called multiphysics models, advanced process models are applied:

• at multiple scales, from micron-level catalyst models to plant-wide optimization
• across the process lifecycle, from initial experimentation and conceptual design through engineering design to plant operation – and in some cases, decommissioning
• across system-wide applications. An example is the whole-chain carbon capture toolkit being developed by Siemens PSE for techno-economic decisions across whole networks of plants.

What are the benefits?

Having accurate models that predict performance over a range of conditions allows you to explore the decision space rapidly, effectively and at relatively low cost. The result is:

• better decisions: better process designs, better equipment designs, better product designs, better operations
• faster decisions: faster and more confident scale-up, faster process development, accelerated innovation at all levels
• safer decisions: better compliance with health & safety requirements, better environmental compliance, more effective management of risk associated with introducing new technology.

These can all add up to significant competitive advantage, and can be achieved at relatively low cost – especially when compared with building pilot plants or prototypes.

A key benefit for many organisations is the ability to capture, deploy and transfer corporate knowledge effectively across the organisation, including streamlining workflows between R&D experimentation and the engineering design process.

What is SPSE’s role?

Siemens PSE is the leading supplier of advanced process modelling technology and services to the process industries.

Our gPROMS^® modelling and optimization platform provides a sophisticated, modern software environment created specifically for construction, validation and execution of high-accuracy models, and the company is a pioneer in the growing application of model-based engineering.