Innovation and optimisation challenges in the food industry are typically addressed case by case using money and time consuming trial-and-error methods. A more efficient way to speed up innovations, optimise production processes and improve product quality is to develop and deploy predictive models that capture essential scientific process-product interaction knowledge.
One of the examples that will be shown in the presentation is model based optimisation of spray drying. The technology combines a predictive model for spray dryers developed by NIZO and in-line absolute moisture sensors developed by Hobré. Variations in ambient air humidity often have a significant effect on drying capacity. Because the model incorporates both drying behaviour and stickiness properties it can be used for real time optimisation of spray drying capacity, while maintaining or improving product quality. In combination with the in-line absolute moisture sensors typically 10 to 20% capacity increase can be achieved.