Spherical crystallization is a promising process intensification technique, where surfactant is an important ingredient in formulation but needs to be used carefully due to toxicological reasons. This work proposes to substitute the conventionally used surfactants by adopting colloidal particles stabilized Pickering emulsions for spherical crystallization. A representative system is selected for study, where silica nanoparticles are prepared to stabilize emulsions and evaporative crystallization of ibuprofen is carried out. Depletion attraction is exploited to improve the Pickering emulsion stability for better confining on crystallization with two depletants PEG and PVA tested. Crystal products from the emulsions prepared with silica nanoparticles and the non-ionic surfactant Tween 20 are compared. The results show that depletion attraction is helpful for producing stable Pickering emulsions with high dispersed phase fraction and mono-dispersed ibuprofen spherical agglomerates. Silica nanoparticles contribute to reduced induction time by boosting heterogeneous nucleation and mitigate secondary agglomeration possibly by steric effects.

A novel adaptive refined grid search strategy is developed for representative characterization of process feasible region boundaries and accurate estimation of its hypervolume. In particular, a linked list data structure adopted from the field of computer science is used to maintain the grid connectivity information. A uniform perturbation scheme is used to refine the search only near boundaries. The volumetric flexibility index FI_V is calculated directly from a summation of feasible hypercubes in the grid, without the need to apply shape reconstruction techniques. The proposed adaptive grid search strategy can capture complex region shapes with reduced sampling costs and without randomness for better reproducibility. Operational flexibility is optimized traditionally at a process scale. A case study on refrigerant selection is presented to demonstrated that the developed strategy could be combined within a computer-aided molecular design framework for operational flexibility optimization in molecular scale.