gPROMS training courses

gPROMS®: Optimisation and model validation (1 day)

The ability to optimise the dynamic behaviour of processes is one of the major technological advances in recent years. 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.

Audience

A one-day training course aims to enable process modellers to effectively apply powerful optimisation facilities provided by gPROMS, including steady-state, dynamic and mixed-integer optimisation, to real problems.

In addition, 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. Participants also learn how to use the optimal experimental design capability, which fully utilises achievable experimental data for subsequent parameter estimation.

It is advisable to have completed the Introduction to gPROMS course prior to attending the advanced course or to have previously had extensive use and/or experience with gPROMS.

Content

A combination of tutorials and hands-on problem solving sessions, allows participants to cover the following topics:

Optimisation with gPROMS

  • Dynamic optimisation in batch and continuous processes by characterising the optimisation problem, i.e.
    • objectives,
    • constraints and
    • decision variables.
  • Formulating steady-state and dynamic optimisation in gPROMS.
  • Process modelling for dynamic optimisation, i.e.
    • model accuracy requirements,
    • dealing with model-plant mismatch,
    • ensuring model robustness and
    • optimising equipment parameters using distributed process models.
  • Optimisation of continuous and discrete variables for steady-state and dynamic processes.
  • Interpretation and use of results.

Model Validation with gPROMS

  • The concept of an ‘experiment’, i.e. translating available experimental data for model validation.
  • Formulating parameter estimation from steady-state and dynamic experiments in gPROMS.
  • Formulating model-based experiment design for steady-state and dynamic processes in gPROMS;
  • Interpretation and use of results.
Email to register

Please drop us an email for the dates and the course you would like to register for and we would get in touch.

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