Petersen, Lars Norbert2; Poulsen, Niels Kjølstad1; Niemann, Hans Henrik4; Utzen, Christer7; Jørgensen, John Bagterp8
1 Department of Applied Mathematics and Computer Science, Technical University of Denmark2 Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark3 Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark4 Department of Electrical Engineering, Technical University of Denmark5 Automation and Control, Department of Electrical Engineering, Technical University of Denmark6 Center for Energy Resources Engineering, Center, Technical University of Denmark7 GEA Process Engineering A/S8 Copenhagen Center for Health Technology, Center, Technical University of Denmark
In 2015 the milk quota system in the European Union will be completely liberalized. As a result, analysts expect production of skimmed and whole milk powder to increase by 5-6% while its price will decline by about 6-7%. Multi-stage spray drying is the prime process for the production of food powders. The process is highly energy consuming and capacity depends among other factors on correct control of the dryer. Consequently efficient control and optimization of the spray drying process has become increasingly important to accommodate the future market challenges. The goal of the presentation is to present our results regarding modeling of the process and how the efficiency and protability can be lifted by introducing an economic optimizing MPC scheme. Firstly, we develop a first-principle engineering model that can be used to simulate spray drying processes with high accuracy. The model can be adjusted to describe drying of various products and describes the complete drying process of a multi-stage spray dryer. The dryer is divided into three stages, the spray stage and two uid bed stages. Each stage is assumed ideally mixed and described by mass- and energy balances. The model is able to predict outlet temperatures, the residual moisture and particle size of the product. We also give a novel approach to predict deposits due to stickiness of the powder. The model predictions are compared to datasets gathered at GEA Process Engineering's test facility. The identified model parameters are identified from data and the resulting model ts the data well. Secondly, the effect of disturbances, ambient air humidity and solids content in the feed, is studied by simulation. We show that conventional control is insufficient at controlling the product quality as well as driving the plant to the most economic conditions. Furthermore, we show that the efficiency can be increased by correct adjustment of heat and inlet air ow at each stage. The recent focus in research has shifted from reference tracking MPC to optimization of economic objective functions.We will discuss how this optimization can be performed by advanced process control techniques, such as Economic Model Predictive Control (E-MPC). We suggest adding an E-MPC based supervisory control layer on top of the contemporary PI-controllers. The strong interconnection between drying stages and process onstraints are well suited for MPC.
Spray drying; Multi-Stage dryer; Grey-box model; First-principles model