gCRYSTAL

Challenges & solutions

We reduced crystallisation batch time by 44% by optimising the cooling curve.

Gerrit Westhoff,
Friesland Campina

5 key challenges

Specific challenges and some typical questions facing designers and operators of crystallisation processes include:

How to process experimental data to estimate model parameters.
Obtaining useful process design information from experimental data requires significant analysis. Model-based techniques using first-principles models of the experiment provide accurate parameter values from multiple experiments.

Key issues that are often encountered include:
How can I process data from multiple experiments simultaneously?
How can I determine scale-invariant kinetic parameters from experimental data?
How can I estimate the accuracy of the experimental data?

How to design experiments that provide maximum information at minimum cost.
Experimentation is a resource and time-intensive activity, and significant savings can be obtained through more targeted experimentation.

Key questions that gCRYSTAL can address include:
What is the most effective 'next experiment' that I can perform?
How can I distinguish between different candidate models – for example, representing diferent growth kinetic models – of the same process?
Is there a way to minimise the number of experiments to be performed to reach an acceptable parameter accuracy?

Particle size distribution (PSD) on start-up

How to optimise operating conditions.
It is possible to achieve significant cost savings by using model-based techniques to optimise operating conditions, at little or no capital cost.
Key questions that gCRYSTAL can address include

What is the optimal cooling profile that will minimise the batch time while keeping product quality the same?
How can the yield of a continuous crystalliser train be improved with the least amount of capital investment?

How to determine the impact of scale-up and geometry changes on crystalliser design and performance.
There is a significant potential to improve process efficiency through careful crystalliser design. However, CFD simulations and experimentation alone are insufficient to determine how certain changes will affect crystalliser performance.

Key questions that gCRYSTAL can address include:
What is the best impeller design for my process?
Is there a different configuration of feed and product points that will improve productivity?
Can I quantify the risk associated with the scale-up my process from the lab-scale to the plant scale?

How to analyse risk analysis. There is considerable risk inherent in activities such as scale-up and change in operation of crystallisation processes. gCRYSTAL allows businesses to quantify and manage the risks associated with such engineering decisions, by helping answer questions such as:

How should I scale up my process to plant scale to ensure correct operation?
Can I quantify the risk associated with the scale-up ?
Which data values present the most risk?
Where do I need to invest R&D resources in order to minimise risk?

How gCRYSTAL addresses these challenges

gCRYSTAL provides a comprehensive set of powerful and easy-to-use capabilities that can address all of the challenges listed above. These include:

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 accurately. It is possible to use.separate population balances for each solid phase – for example, 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. This makes it easy for non-expert modellers – for example, process engineers – to make use of the IP captured in the detailed unit operation models

Parameter estimation

Dynamic simulation capabilities. This allows realistic representation where processes cannot be assumed to be at steady-state (for example, during cooling operations), and are essential for modelling batch and semi-continuous processes.
Dynamic and mixed-integer optimisation capabilities. These enable the optimisation of flowsheet and equipment design taking into account many decision variables simultaneously. Optimisation makes it possible to determine optimal values or trajectories (for example, optimal cooling profiles) directly rather than by lengthy trial-and-error analysis.
The ability to link with CFD models to enable the coupling of hydrodynamic effects and crystallisation phenomena, for example, for reliable scale-up or when studying the effect of changes to crystalliser configuration

links to CFD

Parameter estimation facilities for fitting models to experimental or operating data in order to provide an accurate predictive capability during model validation
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 through the design of optimal experiments that maximise information at minimum cost
Design and process optimisation for batch and continuous processes
Downstream integration with gSOLIDS and liquid and gas models. This means that design and optimisation of solids and downstream processes can be performed simultaneously rather than sequentially, allowing true process optimisation.
A stream structure that includes crystal size distributions and chemical compositions, making it possible to predict the evolution of the crystal size distribution resulting from nucleation, growth, agglomeration and attrition
The ability to add custom models of proprietary equipment or methods.