PSE Process development services

Innovating, creating competitive advantage and generating IP

Petrochemical profitability is suffering badly in the face of poor demand, the ongoing impact of the credit crunch and the weakened oil price

Nigel Davis, ICIS Chemical Business, January 2009

PSE works closely with a number of organisations – process licensors and operating companies – to accelerate development of new processes or enhance the performance of existing ones.

We help companies to create and secure competitive advantage by applying PSE's Model-Based Engineering approaches to:

accelerate innovation by examining the design space and rapidly screening alternatives
manage the associated technology risk by accurately quantifying the effect of design decisions
verify and validate designs in realistic, high-fidelity simulated operating conditions
generate and capture intellectual property (IP) to support claims of competitive advantage.

How does it work?

New process development –
optimised 'green' process for terepthaldehyde production

Model-Based Engineering uses detailed predictive models of the process – validated against experimental, pilot or operating data – to accurately quantify the effect of design decisions.

These are used in conjunction with rigorous mathematical optimization techniques to achieve design objectives directly – while taking into account design and operating constraints – without the need for trial-and-error iteration.

For example, a detailed model of a reactor can be used to rank catalyst alternatives, in support of scale-up, to support detailed mechanical design, to design optimal operating policy, to design and test control schemes, and so on.

Often just the initial application of Model-Based Engineering techniques is sufficient to identify significant potential improvements. These can then be studied in detail.

It is important to remember that modelling is not a substitute for experimentation or pilot or demo testing. However it can greatly speed up these activities and enhance their effectiveness significantly.

What does PSE provide?

gPROMS helps us to minimise development risks during process design and operation

Arkema

PSE provides models and modelling technology, expertise and a set of well-developed methodologies built on the foundations of Model-Based Innovation and Model-Based Engineering.

These are summarised as:

The gPROMS advanced process modelling platform. This underpins the complex calculation required to generate the high-accuracy predictive information on which key design and operating decisions are based, as well as to integrate theoretical models and real-world data.

The state-of-the-art gPROMS advanced process models for catalytic reaction, complex separation, solution crystallisation, polymerisation and many other complex processes.

PSE's hybrid modelling technologies which combine modelling in gPROMS with computational fluid dynamics (CFD) tools for ultimate scale-up accuracy.

Consulting expertise gained through years of providing expert services across many process industry sectors.

The advanced methodologies described below, devised over many years of application to industrial problems.

Key methodologies deployed include the following:

Model validation techniques for incorporating experimental, pilot and plant data within models to provide the ultimate predictive accuracy.

Experimental procedures for generating data to maximise the overall predictive capability of models.

Model-based experiment design procedures to generate the maximum information from the minimum number of experiments, thus reducing experimentation time and cost while increasing its effectiveness.

Experimental procedures to generate scale-invariant model parameters, to ensure validity over a range of scales.

Specialist techniques for scaling up from a small amount of experimental data – even single experiments

Hybrid modelling techniques for accurate scale-up from experimental to industrial-size equipment taking mixing effects into account.

Optimisation techniques to determine optimal design parameters or operating trajectories without the need for trial-and-error simulation.