Process Systems Enterprise Limited
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Although crystallisation is one of the older unit operations in the process industries, the design and operation of crystallisation processes still pose many problems related to product quality and process reliability.

gCRYSTAL is a powerful and user-friendly tool for scientists and engineers that uses high-fidelity predictive models validated with experimental or operating data to provide accurate information for support of design and operational decisions.

gCRYSTAL's model-based engineering approach can save businesses millions of dollars of capital or annual operating cost through optimal design and operation and reliable scale-up.

Seminar details

This free seminar describes the concepts behind a model-based engineering approach to crystallisation process design, and provides an opportunity for attendees to use gCRYSTAL to solve typical practical problems. Attendees of the hands-on session will be offered evaluation licences.

You can see the detailed agenda here

Target audience Technical management and process engineers from companies with crystallisation processes: bulk & fine chemicals, food, pharmaceuticals, mining, FMCG and other sectors
Outline A half-day seminar describing the application of model-based engineering approaches to the design and operation of crystallisation processes, using gCRYSTAL [including hands-on sessions].
Date Wednesday 6 October 2010
Time Registration 13:00-13:30, Seminar 13:30-16:30
Location The PC Microlab (Engineering Room C233)
Chemical and Biochemical Engineering Department
Rutgers University
Cost FREE
Presenters Dr Sean Bermingham, VP Strategic Business Development

Further information

The key differentiators of gCRYSTAL are its capability to address true dynamics and go beyond just simulation to model validation, process optimisation and scale-up.

Challenges

Specific challenges and some typical questions facing designers and operators of crystallisation processes include (see more detail):

  • Processing of experimental data to estimate model parameters
  • Designing targeted experiments that provide maximum information at minimum cost
  • Determine the impact of scale-up and geometry changes on crystalliser design and performance
  • Optimising of operating conditions, such as cooling profiles
  • Quantifying and managing risk associated with activities such as scale-up.

Model-based engineering can be applied in all these areas to bring significant revenue enhancement and cost savings.

How gCRYSTAL addresses these challenges

gCRYSTAL provides a comprehensive set of powerful and easy-to-use capabilities that include (see more detail):

  • a library of common crystalliser configurations:
      – batch, semi-batch and continuous operations
      – supersaturation generation through cooling, evaporation, anti-solvent addition and reaction
      – seeded and unseeded
      – single and multi-stage
  • population balance modelling to represent the crystal size distribution; separate population balances for each solid phase, e.g. to handle polymorphism
  • representation of key phenomena such as primary and secondary nucleation, growth, attrition, agglomeration and breakage with detailed first-principles models
  • drag-and-drop flowsheeting for creating process flowsheets
  • dynamic simulation and optimisation (including mixed-integer optimisation) capabilities
  • the ability to link with CFD models to capture hydrodynamic effects, e.g. for reliable scale-up
  • parameter estimation facilities for fitting models to experimental or operating data
  • model-based data analysis capabilities including the ability to process multiple, dynamic experiments simultaneously and reporting of statistical significance of parameter estimates
  • experiment design capabilities to assist in targeted experimentation
  • design and process optimisation for batch and continuous processes
  • downstream integration with gSOLIDS and upstream integration with liquid and gas models to allow wider process optimisation
  • a stream structure that includes crystal size distributions and chemical compositions
  • the ability to add custom models of proprietary equipment or methods.

PSE. Advanced Process Systems Engineering tools