Abstract Summary (Max 250 words)
The optimal operation of mixer-settler systems requires the conflicting requirements of the two unit operations to be balanced. This project aims to address this challenge by establishing a closed-loop dynamic process control based on a data-driven model and inline and online drop size distribution (DSD) and conductivity measurements. The control methodology was demonstrated in a batch STR using a toluene/NaOH system, with benzoyl chloride as the transfer component to study the influence of mass transfer from the dispersed to the continuous phase. Stirrer speed was the only dynamic control variable employed to influence DSD. The dynamic behaviour of DSD in response to variations of stirrer speed was characterised in terms of coalescence and breakage time constants. An online particle detector was integrated based on an endoscopic, inline optical measurement technique enhanced with automated image analysis via a convolutional neural network. This enabled the real-time acquisition of a DSD every 2–7 s based on 4,000 detected drops. A higher-order dynamic model was identified and validated to describe the key DSD characteristics. In combination with the implementation of the particle detector and a standard PI controller, the stirrer speed could be successfully adapted in a closed-loop system. The STR setup is now operated continuously and has been extended with a settler to validate the potential of the developed methods to improve the overall robustness and efficiency of the complete mixer-settler system. This talk will provide an overview of the experimental results and modelling approaches.
