The development and application of sticky-point models to spray drying processes for the manufacturing of nutritional powder products and infant formulas
This paper describes an approach to developing a predictive model for the spray drying of powder nutritional products, both infant and adult formulas, in order to optimize drying efficiency and avoiding the sticking of the powder on the dryer walls. The model is based on the glass transition tempera...
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Veröffentlicht in: | Journal of food engineering 2020-08, Vol.279, p.109947, Article 109947 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This paper describes an approach to developing a predictive model for the spray drying of powder nutritional products, both infant and adult formulas, in order to optimize drying efficiency and avoiding the sticking of the powder on the dryer walls. The model is based on the glass transition temperature measurement by rheology method, and subsequent sticky point characterization of the products to set constrains for process variables which affect the drying efficiency and powder stickiness. Three different approaches are presented to characterize the products: 1) using theoretical data, 2) using experimental results based on laboratory tests, and 3) data from direct measurement of the dryer temperature and moisture sensors. Equations for variable manipulations to reach the target powder moisture content have also been developed. The developed models have been successfully applied in a real manufacturing environment, demonstrating how the sticky point model fits an industrial spray drying process and are a useful tool to improving efficiency and reduce quality issues related to powder properties.
•Three different methods have been used to determine the glass transition temperature of powder products.•Four different powder formulas have been characterized.•Equations to predict powder moisture according to process conditions have been developed.•Sicky point models and prediction equations have been applied at industrial scale. |
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ISSN: | 0260-8774 1873-5770 |
DOI: | 10.1016/j.jfoodeng.2020.109947 |