Why use gPROMS FormulatedProducts' crystallization capabilities?

gPROMS FormulatedProducts's crystallization libraries are designed to enable more efficient and effective design and operation of crystallizers and crystallization processes by increasing the knowledge and understanding about them. It caters for a wide range of industry applications and has a proven track record of adding sustainable value to the businesses that use it.

Applications

In addition to the basic, steady-state models available in conventional process modelling tools, gCRYSTAL modules of gPROMS FormulatedProducts provides dynamic, high-fidelity models and advanced model validation, process optimisation and custom modelling capabilities providing better accuracy and robustness for steady-state and dynamic modelling, single- and multi-stage crystallizers, batch, semi-batch and continuous operation.  Additionally, the gCRYSTAL modules also include process models for plug flow crystallizers, wet milling and filtration (pressure filter & centrifugal filter) which can be employed to assist scale-up and technical transfer activities. This enables corporations to make better and safer decisions about the development and operation of their processes.

The crystallizer libraries of PSE's the gPROMS FormulatedProducts gCRYSTAL software are designed to enable more efficient and effective design and operation of crystallizers and crystallization processes by increasing the knowledge and understanding about them. It caters for a wide range of industry applications and has a proven track record of adding sustainable value to the businesses that use it.  

Some typical applications for gPROMS FormulatedProducts' crystallization capabilities are: 

Advanced parameter estimation and model validation

Unlike most modelling tools on the market, gPROMS FormulatedProducts gCRYSTAL modules offer advanced model validation (incorporating internal model validation or parameter estimation and external model validation) parameter estimation capabilities with full statistical analysis of equipment or process empirical parameters from experimental; laboratory, pilot or operational; data, with estimates of data uncertainty for risk analysis.

Advanced optimisation

The crystallizer modules in gCRYSTAL are capable of steady-state, dynamic and Mixed Integer Optimisation, ideal for crystallization and whole-process optimisation. The crystallizer modules in gCRYSTAL are not limited by application area or operation mode, the same tool and the same models are used for steady-state and dynamic simulation or for batch, semi-batch and continuous operation, improving workflows and reducing risk of error. 

Global System Analysis

The gCRYSTAL modules can be utilised with the Global System Analysis (GSA) platform to efficiently perform the following activities:

  • Parametric studies
  • Uncertainty analysis
  • Sensitivity analysis

These activities can be performed to conduct analyses to gauge the impact of estimated kinetic parameter uncertainty on the model predictions of the Critical Quality Attributes (CQA); identify the Critical Process Parameters (CPP) which impact key CQA’s of the process.

Design and scale-up

The risk of a scaled up process not yielding the desired product quality, process and economic performance can be significantly reduced by combining hydrodynamic models (CFD) with the crystallization models using PSEs Hybrid Multizonal capability. 

Extract better kinetic information from fewer experiments

Extracting kinetic data from experiments is key to reliable models predictions, but the process is often laborious and time consuming. gPROMS FormulatedProducts has features that enable better kinetic information from fewer experiments, saving time and cost on experimentation. The integrated model validation capabilities can estimate kinetic parameters and assess their accuracy (confidence intervals) from steady-state and/or dynamic experiments. If the uncertainty and associated business risk is considered too high, the experiment design feature will deliver the minimum number of experiments required to obtain sufficient parameter accuracy. 

Robust and efficient batch processes

Robust recipes are difficult to develop and batch to batch variability incur high costs and reduced asset utilisation when operating crystallization processes. gPROMS FormulatedProducts is ideal for the design of robust recipes that ensure on-spec product quality, both PSD and purity. It can simultaneously consider decisions such as seed amount, seed PSD, initial loading, temperature profile, anti-solvent addition profile and optimise the recipe to reduce variability in operation. 

Batch to continuous

Continuous processing may have many advantages related to operation flexibility and costs compared to batch processes, although many corporations are resistant to the transfer procedure and related costs. gPROMS FormulatedProducts's advanced model validation and optimisation features allow knowledge to be captured from lab-scale batch experiments and to apply that knowledge to the optimal design and operation of a continuous, manufacturing scale process. It enables the transfer of a manufacturing process from batch to continuous operation without changing the R&D set-up and techniques, reducing cost and time spent on the procedure. 

Flexible and reliable continuous processes

Use gPROMS FormulatedProducts's steady-state and dynamic optimisation capabilities to determine the optimal number of crystallization stages, recycle structure, operating conditions as well as start-up and shutdown procedures. This approach results in an economically optimal process subject to product quality (PSD and purity), operability and safety constraints. 

Quantify the risk associated with imperfect knowledge of your process

In practice you will never have full knowledge of a process as neither models nor measurements are perfect. gPROMS FormulatedProducts's advanced model validation features allow you to understand how imperfect process knowledge, captured by the parameters’ confidence intervals, translates to uncertainty in model predictions for process optimisation and scale-up.

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