Process Systems Enterprise Limited
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Model-Based Engineering for the Chemicals sector

Maximizing ROI through process optimization and plant improvement

Chemical Engineering November 2008 modeling article

How does MBE work? See our article in Chemical Engineering

 

 

 

We made rapid progress in bridging experiments and process design for lactide production

PURAC Biochem,
PSE AM 2008 

 

 

More on MBE …

We are happy to help you explore the following Model-Based techniques further if you are interested:

  • Model-Based Data Analysis
  • Model-Based Experiment Design
  • Model-Based Design
  • Model-Based Safety Engineering
  • Model-Based Innovation

 

 

 

 

PSE has extensive experience in the modelling of many reaction and separation processes.

This is embodied in our Model-Based Engineering (MBE) solutions, a set of models and advanced engineering methodologies for the detailed modelling and optimisation of chemical processes.

Model-Based Engineering: key components

PSE's MBE approach is founded on:

  • 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 and polymerisation.
  • PSE's hybrid modelling technologies which combine modelling in gPROMS with computational fluid dynamics (CFD) tools for ultimate scale-up accuracy.
  • PSE's Consulting expertise.
  • The advanced methodologies described below, devised over many years of application to industrial problems.

Collectively these bring a step change to the operation and design of full-scale commercial plants.

Model-Based Engineering: methodologies

The methodologies at the heart of Model-Based Engineering 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.
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.