Reducing batch time to build an agile, profitable and sustainable business with gCRYSTAL
Friesland Campina DOMO successfully used gCRYSTAL® for an innovative, model-based approach to achieve their goal of becoming a partner of choice for pharmaceutical customers requiring lactose with specific functional properties.
In addition, DOMO reduced the batch time of their crystallization process by an average of 44% while still complying with the high quality standards of their customers in Pharma, thus improving asset utilisation significantly.
Business implications and objectives
Providing pharma-grade lactose to a range of customers with specific needs, demands great agility from the manufacturer in order to consistently deliver the product on-spec and on time. This was a challenge for DOMO since new recipes need to be designed and may carry a high risk with regard to consistently achieving the desired product quality. Furthermore, the need for optimisation becomes crucial for improving asset utilisation. DOMO used gCRYSTAL to achieve the following for their batch cooling crystallization process:
- design of new recipes to meet product particle size distribution and purity
- process optimisation to reduce batch time
- reduction in batch-to-batch variability
- increase capture and maintenance of lactose crystallization knowledge
Model configuration in gCRYSTAL
The aim of the crystallization process model was to be able to accurately predict the following key model output; product particle size distribution (PSD), process yield and the α- and β-lactose concentrations. gCRYSTAL was used to include the configuration of the crystallization equipment, operating recipes and also to importantly account for a range of key crystallization phenomena in the lactose process, such as:
- primary nucleation
- growth and growth rate dispersion
As part of the recommended workflow for model-based decision support, DOMO carried out a model validation exercise to ensure the use of a reliable model for their manufacturing process. Consequently, parameter estimation was carried out and the model was compared against plant data from seven batches. The same parameter set was used for the prediction of the key model output for all seven batches.
The validated gCRYSTAL process model was successfully able to:
- describe the observed product PSD and lactose composition in solution for the seven experiments used for estimation
- predict behaviour at conditions not included in estimation (including extreme behaviour reported by operators)
- describe other aspects of the process (e.g. mutarotation) of importance for future product development
The validated gCRYSTAL model has been used successfully to design optimised recipes for producing product with specific PSDs and further to reduce batch time of existing recipes by an average of 44% while maintaining the same crystal median size and purity.
Due to its capabilities for accurately predict the batch crystallization plant operation, the gCRYSTAL model is used in the control room for operator advice and training via PSE's gO:Run Excel
Due to its capabilities for accurately predict the batch crystallization plant operation, the gCRYSTAL model is used in the control room for operator advice and training via PSE's gO:Run Excel interface.