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Multipurpose stirring and blending vessels equipped with various impeller systems are indispensable in the pharmaceutical industry because of the high flexibility necessary during multiproduct manufacturing. On the other hand, process scale-up and scale-down during process development and transfer from bench or pilot to manufacturing scale, or the design of so-called scale-down models (SDMs), is a difficult task due to the geometrical differences of used vessels. The present work comprises a hybrid approach to predict mixing times from pilot to manufacturing scale for geometrical nonsimilar vessels equipped with single top, bottom or multiple eccentrically located impellers. The developed hybrid approach is based on the experimental characterization of mixing time in the dedicated equipment and evaluation of the vessel-averaged energy dissipation rate employing computational fluid dynamics (CFD) using single-phase steady-state simulations. Obtained data are consequently used to develop a correlation of mixing time as a function of vessel filling volume and vessel-averaged energy dissipation rate, which enables the prediction of mixing times in specific vessels based on the process parameters. Predicted mixing times are in good agreement with those simulated using time-dependent CFD simulations for tested operating conditions.

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Hybridization is one of the key technologies to reduce the fuel consumption of a vehicle with internal combustion engine (ICE) significantly. Using at least one electric motor (EM), the kinetic energy of the vehicle can be recuperated and the ICE can operate more efficiently. The control strategy (CS) coordinates the torque of the ICE and the EM. If the driving cycle is known, the coordination of the drive units can be adjusted for every point in time, therefore the fuel consumption based on the entire cycle is minimal. Dynamic programming, for example, can be used, but computation time is long and it offers only a few degrees of freedom to evaluate the potential of hybrid drives. For this reason, a new method to identify the global energy optimum in a particularly systematic and transparent way was developed at the Institute of Automotive Engineering. It is therefore a globally optimal control strategy. At the same time, the approach is efficient in terms of computation time and inherently SOC neutral, therefore allowing a very good comparability of results.