Optimisation of mixing performance of helical ribbon mixers for high throughput applications using computational fluid dynamics

The work presented focuses on the optimisation in a 1 L vessel using an anchor with a helical ribbon design using CFD and a learning algorithm, optimised by minimisation of the torque output of the mixer and the homogeneity of the mixture in the vessel after a defined mixing time. The results were successfully validated experimentally using Electrical Resistance Tomography (ERT) and direct torque measurements.

The study determined that the height of the mixer is a key factor in the performance of the mixer, with other significant factors present, but with a lower impact. For the case of torque, all design features of the mixer which increase the size, i.e., surface area acting against motion, were found to be significant in increasing the modelled torque response. The Auger screw was found to have no significant impact on either mixing and torque response.

The results illustrate the capability of optimisation algorithms to achieve results comparable to those achieved experimentally, while assessing a significantly larger number of design options and optimising for several performance indicators simultaneously.