PSE enhances process modelling software

Process Systems Enterprise (PSE) has released version 3.2 of the Gproms advanced process modelling (APM) software.

Gproms is used by large process industry companies in the oil and gas, chemicals and petrochemicals, power generation, clean energy, food and beverage, consumer products, pharmaceutical and other process sectors.

The latest version contains enhancements for handling discrete logic within the equation-oriented modelling environment, as well as productivity, speed and robustness enhancements.

Building on Gproms capabilities for modelling and optimising both complex physics and discrete operating procedures, version 3.2 allows each physical model to incorporate discrete logic that is automatically triggered for every instance of the model included in a flowsheet.

This allows, for example, Gproms library models to include sophisticated supervisory controllers, whose execution is completely transparent to end-users.

The software contains enhanced diagnostics that make information more easily accessible both for model developers and users.

These include facilities for diagnosing over-and-under specification and making intelligent suggestions for the choice of specification variables.

There is improved analysis and assistance for numerical troubleshooting during execution, as well as improved information at the flowsheet level during model construction.

There are a number of usability enhancements aimed at simplifying the creation and maintenance of complex flowsheets.

These range from minor improvements such as the ability to align flowsheet elements to the ability to use text array indices (for example, component names) consistently throughout Gproms, including dialogue specifications.

For results presentation, a data-export facility provides semi-automated selective export of variables to postprocessing facilities such as MS Excel for pivot table analysis, for example.

In addition to providing formatted output and addition calculations, this helps reduce model size and execution time.

Numerical enhancements and exact model reduction techniques mean that many classes of problem now run up to 20 per cent faster in less memory.

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