Sinapic acid is a potential valuable compound to be recovered from rapeseed meal extracts as it processes antioxidant and antimicrobial properties. However, the concentration of this compound might be low and the presence of other low value compounds could complicate its downstream processing. Adsorption is an alternative technique that might allow selective recovery of this compound. This work was focused on establishing the foundation of an industrial process design to recover sinapic acid by adsorption. The obtained results from multicomponent experiments indicate that, resin Amberlite^TM FPX66 is the best performing one showing a maximum adsorption capacity of 102.6 + 11.7 mg/g_resin, easy sinapic acid recovery by desorbing it with 70% ethanol and high selectivity to sinapic acid over glucose, phytic acid and glucosinolates. The obtained equilibrium information was applied as input in a dynamic column model and compared with experimental results, showing a good agreement (r² = 0.98). The model can be further applied for a large-scale chromatography process design to recover sinapic acid from rapeseed/canola meal extracts.

Expanded bed adsorption (EBA) emerged in the early 1990s in an attempt to integrate the clarification, capture and initial product concentration/purification process. Several mathematical models have been put forward to describe its operation. However, none of the models developed specifically for EBA allows simultaneous prediction of bed hydrodynamics, mass transfer/adsorption and (unwanted) interactions and fouling. This currently limits the development and early optimization of EBA-based separation processes. In multiphase reactor engineering, the use of multiphase computational fluid dynamics has been shown to improve fundamental understanding of fluidized beds. To advance EBA technology, a combination of particle, equipment and process scale models should be used. By employing a cascade of multiscale simulations, the various challenges EBA currently faces can be addressed. This allows for optimal design and selection of equipment, materials and process conditions, and reduces risks and development times of downstream processes involving EBA.