GP-based Modeling for PSD Control of Emulsification Processes*

Emulsification processes show a plethora of use cases in different industries. The complexity and intransparency of many emulsion systems make it hard to apply classic control approaches. The operation of these systems therefore often diverts towards a manual open-loop control. While population balance models (PBM) have been explored for multiple decades, they are rarely used in practice for closed-loop control due to the high computational effort. For this purpose a data-driven modeling approach specifically tailored to the control of complex emulsification devices is introduced. A new simplified description scheme of particle size distributions in combination with Gaussian process regression on a reasonably sized dataset can predict the system change. It additionally gives a useful measure of uncertainty for the predicted change, which is propagated onto the discrete distribution description. The concept is proven with leave-one-out cross-validation, before showing its potential in a model predictive control (MPC) simulation.