gPROMS ProcessBuilder

Advanced applications to create competitive advantage

Because of its equation-oriented architecture, ProcessBuilder has unprecedented capabilities for high-value, advanced applications that go well beyond the standard heat-and-mass balance calculations of traditional process simulators.

These allow process designers and operators to explore the process decision space rapidly and effectively with high-fidelity models to create unique value and competitive advantage.

Advanced applications

The applications listed here are made possible through a combination of ProcessBuilder's high-fidelity model libraries and its equation-oriented solution power. Most cannot be done using coventional simulation tools.

Sensitivity analyses

ProcessBuilder's equation-oriented solution speed allows you to run many similar simulations rapidly. This is ideal for performing sensitivity analyses, to determine the effects of changing combinations of design or operating variables on a process's performance.

Whole-plant economic optimisation

ProcessBuilder's powerful optimisation capabilities, coupled with rigorous reactor and separation models, mean that you can perform plant-wide optimisation with many decision variables – for truly optimal process design and operation.

Batch process optimisation

The combination of dynamics, rigorous reaction kinetics and optimisation capabilities means that you can optimise batch processes to determine the optimal trajectories of key variables – for example, the addition profile of a reactant – subject to performance – e.g. product quality – constraints, to minimise batch time.

Combined reaction and separation

ProcessBuilder's unique reaction modelling capabilities mean that you can create reactor models that can be used from laboratory analysis to detailed design at industrial scale. These can be combined with state-of-the-art separation models to allow rigorous full-plant steady-state and dynamic analysis.

Complex integrated flowsheets

ProcessBuilder's powerful equation-oriented engine deals easily with processes involving multiple material and energy recycles. Air separation flowsheets, for example, can be solved in seconds, making possible advanced applications such as sensitivity analysis or whole-plant optimisation.

Complex dynamic simulation

Designing optimal cyclic separation processes can be a real challenge because of the complex transients in these inherently dynamic processes. ProcessBuilder brings a new level of high-fidelity modelling to this type of application.

Equipment configuration decisions

On which column tray should the feed go? How many stages do you need? How many CSTRs do you need for a given conversion? For the first time in a process simulator, ProcessBuilder's mixed-integer optimisation allows you to find the optimal equipment configuration based on your objective function and constraints.

Process synthesis decisions

Which combination of reaction and separation equipment will give you the best overall economics? How do you choose between a set of proposed process options? Again, for the first time in a process simulator, ProcessBuilder's mixed-integer optimisation allows you to find the optimal process flowsheet configuration for your requirements.

Advanced applications add value

The real value of the advanced applications outlined above is that they enable creation of additional value well beyond that from the heat and material balance information from traditional simulators.

A ProcessBuilder model enables you to:

  • accelerate innovation, reducing time-to-market. It provides the ability to explore the decision space rapidly using accurate predictive models, and reduces the need for costly pilot plants and time-consuming experimentation.
  • come up with optimal design and operations. High-fidelity reaction and separation models coupled with whole-plant optimisation allows true process optimisation.
  • determine quantitative measures for managing risk. Advanced process modelling makes it possible to quantify uncertainty in model parameters (for example, the critical reaction kinetic parameters that determine the production of impurities) and make decisions about risk, further investment in R&D, risk/reward trade-offs, etc.
  • integrate experimentation and engineering design. By integrating experimental data with the models used for engineering design, it quickly becomes apparent where more data is required, improving the efficiency and effectiveness of both the R&D and engineering design functions.

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