Deriving fast distillation models: diploma thesis proposal

Distillation is the most important separation technology today. A trend in controlling distillation columns economically efficient is going toward using model predictive control (MPC) algorithms, which calculate an optimal input trajectory to the plant based on repeated simulations of a model of the process to predict the future behaviour of the plant with respect to disturbances and control inputs. Since the controllers operate in real-time, very fast process simulations are needed. In case of distillation, full models are usually too complex to be simulated sufficiently fast. Therefore, methods to derive reduced models are of high interest to both industry and research community. Our approach to derive a reduced model is based on tray aggregation, which means that the slow dynamics of a number of consecutive stages in the distillation column are approximated by a large ”aggregation tray”, and the fast dynamics are approximated by employing a quasi-steady-state assumption. These reduced models provide a good gain in computational performance while being sufficiently accurate to be used in an MPC. While the procedure to derive these models is straightforward, there are many structural and implementation degrees of freedom which can be used to further improve the performance of the method.