gPROMS Training Course - Optimisation and Model Validation with gPROMS®
Audience
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.
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".
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