gPROMS ModelBuilder

The world's leading Advanced Process Modelling environment

gPROMS ModelBuilder® is the custom modelling and flowsheeting environment at the heart of the gPROMS platform products.

gPROMS ModelBuilder is an environment for expert modellers to build, validate and execute steady-state and dynamic process models and deploy these across the organisation.

It provides all the facilities of the gPROMS advanced process modelling platform for creating and managing custom models, from single units such as novel reactors to entire corporate process simulation and optimisation environments.

Build, validate, execute, deploy

The gPROMS platform's powerful process modelling language allows expert modellers to create custom process models of virtually any level of complexity, and validate these against experimental or plant data using built-in advanced parameter estimation techniques.

Models can be combined with other custom or library models within a process flowsheet. You can then apply the gPROMS platform's steady-state and dynamic simulation and industry-leading optimisation capabilities to generate high-accuracy predictive information for decision support in product and process innovation, design and operation.

Library management capabilities allow easy supply of models to users across the organisation. Models can be protected (encrypted) for security and quality assurance purposes.

Build

1. Build

Powerful custom modelling capabilities allow you to create first-principles models of virtually any type of process:

  • Easy custom modelling with no need to program numerical solution methods
  • Lumped and distributed parameter (spatial, size distribution, etc.) modelling
  • Powerful discontinuity handling
  • Steady-state and dynamic models within the same environment
  • TASK language for operating policy
  • Multiple physical property options
Validate

2. Validate

You can then fit empirical model parameters to experimental (laboratory, pilot or operating) data to provide highly predictive models:

  • Advanced parameter estimation capabilities
  • Estimate multiple parameters from multiple steady-state and dynamic experiments
  • Full statistical analysis of parameter uncertainty to support risk analysis and R&D investment decisions
  • Enhance predictive capability of first principles model

At this point you have captured corporate knowledge in a first-principles validated model with accurate predictive capability. Now it is possible to apply this to generate value and create competitive advantage in a number of ways:

Execute

3. Execute

You can perform steady-state and dynamic simulation and optimisation to explore the decision space rapidly and effectively in order to:

  • Optimise unit design to minimise CAPEX, maximise operability, etc.
  • Optimise operation to minimise energy, feedstock use, etc.
  • Optimise operating policy to determine optimal start-up or grade change procedures
  • Perform whole plant optimisation to improve plant economics
  • Perform scale-up of complex units, linking to CFD for mixing effects where necessary.
Deploy

4. Deploy

You can gain further return on investment by deploying models across the organisation:

  • As on-demand operations or purchasing decision support tools executing within a web browser
  • In online execution as part of the plant automation system, for monitoring, optimisation and look-ahead simulation
  • For design support, executing within MS Excel, web browsers, VBA or other custom interfaces
  • Within other engineering software such as Aspen Technology's Aspen Plus®, Schneider Electric SimSci's PRO/II®, ANSYS FLUENT® or The Mathworks' MATLAB®.

Features & advantages

Because of its modelling and solution power and its process industry focus, the gPROMS platform brings many advantages. These are just a few:

  • Unlike more general mathematical modelling environments, ModelBuilder is aimed specifically at process modelling with its units-and-streams approach, flowsheet editor and multiple physical property options.
  • World-leading custom modelling capabilities that allow you to easily construct your own custom models to virtually any level of complexity, capturing corporate knowledge in a way that can be used to create value and competitive advantage.
  • Powerful optimisation capabilities that allow you to determine optimal answers directly rather than via trial-and-error simulation, saving time and reducing time-to-market for new processes or enhancements.
  • Library management facilities that allow you to create easily-maintainable libraries of models and provide them to users across the organisation.
  • Steady-state and dynamic simulation capabilities within the same framework, allowing you to streamline workflows, investigate important transient effects easily and optimise transient process performance.
  • World-leading numerical solution techniques that provide answers rapidly and robustly and are fast enough to be used in large-scale optimisation.
  • Model export facilities to package models for execution in other engineering software environments via the gPROMS Objects.
  • A sophisticated flowsheeting environment that allows you to utilise your custom models within a process flowsheet along with gPROMS gML and AML library models.

Long-term benefits

Systematic investment in advanced process modelling can bring significant long-term benefits. You can:

  • accelerate innovation: optimisation technologies coupled with accurate predictive models can help to significantly shorten design cycles, with faster time-to-market for new processes and process enhancements
  • optimise process design and operation reliably, based on the accurate predictions provided by first-principles validated models
  • capture corporate knowledge – for example, reaction characterisation – in a form in which it can be used to create value by groups across the organisation
  • leverage existing investment and academic research to create competitive advantage by embodying the knowledge gained within corporate models
  • reduce experimentation time and cost by combining R&D experimentation and engineering design
  • manage risk by identifying parameter uncertainty and determining where to invest in R&D, and through quantified decision support generally.

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