Computational fluid dynamics simulation of a jet crystallizer for continuous crystallization of lovastatin.

Continuous crystallization of lovastatin from a lovastatin-methanol solution and water as the anti-solvent in an impinging jet crystallizer is investigated using a computational fluid dynamics model. To capture the important phenomena, the model is coupled with micro-mixing, population balance, and related energy balance equations. It is implemented in OpenFOAM and validated against experimental data, where a fairly good agreement is found. The effects of key process parameters on the crystallization performance are also studied using the validated model. The results show that increasing the inlet jet velocity from 1 to 4 m/s yields a much narrower size distribution and 70% reduction in the mean crystal size. The four-fold increase in the inlet jet velocity also reduces the crystal production rate by one order of magnitude. Also, it is found that increasing the inlet supersaturation ratio from 6.8 to 8.8 nearly doubles the mean crystal size. Moreover, it results in a wider size distribution and a six-fold increase in the crystal production rate. The simulations also confirm that lower solution to anti-solvent mass flow ratios yield a wider size distribution, a larger mean crystal size and a higher crystal production rate. Increasing this ratio from 0.5 to 2 reduces the production rate by two orders of magnitude.

Optimally Safe Tank Changeover Operation Using a Smooth Optimization Formulation.

Tank changeover is a routine process in industry for placing fuel tanks into or out of service. The operation must use inert gas to avoid the flammability zone. However, inert gas consumption should be minimized for economic reasons. This requires dynamic modeling and optimization of the process, as addressed in the present work. A new dynamic optimization problem for minimizing the inert gas consumption, while ensuring fire safety is proposed. As part of the problem constraints, the flammability zone is characterized by disjunctive constraints, which are then converted to a new simple, nonsmooth formula, removing the need for data regression. This together with the multi-mode flow equations in the model leads to a nonsmooth dynamic optimization problem. To enable reliable solution by gradient-based solvers, the problem is reformulated to a smooth one using sigmoid functions. Case studies of methane tank purging and filling operations demonstrate that the proposed approach is able to minimize the inert consumption by providing optimal trajectories of the tank inlet and outlet flow rates, while ensuring the operation remains outside the flammability zone. It is shown that the proposed dynamic optimization can yield significant economic benefits as it reduces the nitrogen consumption by about two-third in one of the examples solved.