Development of a State Estimation Environment for the Optimal Control of a Mini-plant for the Hydroformylation in Microemulsions

Abstract

A state estimation framework for a surfactant containing multiphase process for the hydroformylation of long-chained alkenes is presented. Firstly, available state estimation methods, such as the extended Kalman filter, the unscented Kalman filter and the particle filter are compared regarding their usability in processes with high model and measurement uncertainty. Subsequently, an MHE-based state estimation algorithm is introduced. This includes an approach, which handles the occurring multi-rate measurements by dividing the state estimation into two separate steps. Finally, the implementation is discussed regarding necessary requirements and the state estimation framework is applied within long-term real process operation in a mini-plant.