Batch top-spray fluid bed coating: Scale-up insight using dynamic heat- and mass-transfer modelling

Abstract

A mathematical model was developed for batch top-spray fluid bed coating processes based on Ronsse et al. [2007a, b. Combined population balance and thermodynamic modelling of the batch top-spray fluidised bed coating process. Part I—model development and validation. Journal of Food Engineering 78, 296–307; Combined population balance and thermodynamic modelling of the batch top-spray fluidised bed coating process. Part II—model and process analysis. Journal of Food Engineering 78, 308–322]. The model is based on one-dimensional discretisation of the fluid bed into a number of well-mixed control volumes. In each control volume, dynamic heat and mass balances were set up allowing the simulation of the contents of water vapour, water on core particles and deposited coating mass as well as fluidisation gas, particle and chamber wall temperature. The model was used to test different scale-up principles by comparing simulation results with experimental temperature and humidity data obtained from inorganic salt coating of placebo cores in three pilot fluid bed scales being a 0.5 kg small-scale (GEA Aeromatic-Fielder Strea-1), 4 kg medium-scale (GEA Niro MP-1) and 24 kg large-scale (GEA MP-2/3). Results show good agreement between simulated and experimental outlet fluidisation air temperature and humidity as well as bed temperature. Simulations reveal that vertical temperature and humidity gradients increase significantly with increasing scale and that in fluid beds as the simulated 900 kg (RICA-TEC Anhydro) production-scale, the gradients become too large to use the simple combined drying force/relative droplet size scale-up approach without also increasing the inlet fluidisation air temperature significantly. Instead, scale-up in terms of combinations of the viscous Stokes theory with simulated particle liquid layer profiles (obtained with the model) is suggested. In this way, the given fluid bed scale may be optimised in terms of low agglomeration tendency for a given process intensity across scale.