Process Systems Enterprise Limited
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gPROMS training courses

Optimisation and Model Validation with gPROMS®

gPROMS support

If you have a support query, you should submit it via e-mail to

support.gPROMS@psenterprise.com

together with the following files, where applicable:

  • complete Case file (.gCS), as it includes the Execution Output showing the specific problem
  • Foreign Object code
  • physical properties used

Alternatively, you may Submit your support query online (PSE access password required) or phone or fax your closest office.

 

The ability to optimise the dynamic behaviour of processes is one of the major technological advances in recent years. This two-day training course aims to enable process modellers to understand the potential of this new technology and to apply it effectively to real problems using the powerful optimisation facilities provided by gPROMS, including steady-state, dynamic and mixed-integer optimisation.

The Introduction to gPROMS course is a pre-requisite and should have been completed at least one month prior to this course, preferably with extensive use of gPROMS in between. Please note that we do not take back-to-back bookings for the Introductory and Optimisation & Model Validation courses.

Validating the model is an integral part of most process modelling activities. This is usually done as an iterative procedure, designing one or more experiments, performing the experiments in the laboratory or a pilot plant, estimating model parameters and, based on a statistical analysis, designing one or more additional experiments etc. The course provides process modellers with a detailed understanding of the use of the parameter estimation facilities in gPROMS and the statistical analysis required for model discrimination and the accuracy of the model parameters. The participants also learn how to use the optimal experimental design capability in order to generate maximum information in the experimental data to be used for subsequent parameter estimation.

Content

The course is organised as a combination of small group tutoring and hands-on exercises to cover the following topics:

  • Applications and benefits of dynamic optimisation in batch and continuous processes. The dynamic optimisation problem: objectives, constraints, decision variables
  • Formulating steady-state and dynamic optimisation in gPROMS; interpretation and use of results
    Process modelling for dynamic optimisation: model accuracy requirements, dealing with model-plant mismatch, ensuring model robustness, optimising equipment parameters using distributed process models
    Mixed-integer optimisation - allowing the optimisation of continuous and discrete variables for steady-state and dynamic processes
    The parameter estimation problem; the concept of an 'experiment'
  • Parameter estimation from steady-state and dynamic experiments in gPROMS; interpretation and use of results
    Model-based experiment design for steady-state and dynamic processes in gPROMS; interpretation and use of results.

The course assumes a basic knowledge of gPROMS to the level covered by the course "An introduction to gPROMS".