Dynamic Optimization of An Evaporator with a Nonlinear Model Predictive Controller for Application at Modular Micro Rectification
Ralf Knauss, Department of Chemical Engineering and Environmental Technology, Department, Inffeldgasse 25 C/II, Graz, Austria, R. Marr, Department of Chemical Engineering and Environmental Technology, Graz University of Technology, Inffeldgasse 25/C/II, Graz, A-8010, Austria, and M. Siebenhofer, University of Technology Graz, Department of Chemical Engineering and Environmental Technology, Inffeldgasse 25/C/II, Graz, 8010, Austria.
From micro-structured equipment maximum efficiency is expected for variety of production processes. Unit operations are realized as individual functional modules in different micro devices. In a multi unit operation plant a correspondingly large number of manipulable variables have to be coordinated. While proper design of micro-scaled plants offer sophisticated systems, fully optimized plant and process control still suffers from a lack of satisfying solutions. A system for modular batch phase contacting micro rectification devices includes the unit operations heating, cooling, mixing and separating. Heat exchangers, cyclones for phase separation and mixers can be arranged to a counter-current rectification system with maximum mass-transfer efficiency in every unit. When operating an electrical heated evaporator, designed by the Forschungszentrum Karlsruhe for modular rectification devices, a strong interaction of mass flow with the steam fraction and the outlet temperature can be observed. For constant flow rate the temperature and vapour fraction can hardly be kept on specified set points with common methods of linear control technology. For dynamic optimization of the above mentioned multivariable micro-structured evaporator a Nonlinear Model Predictive Control (NMPC) was generically formulated in C++ and implemented into the control software LABVIEW 7.0. At any discrete time step an objective function, which is generated from nonlinear process NARX-polynomial models, evolves optimal sequences of control actions for plant operation. The resulting constrained cost function is non-convex making detection of relative local optimum a difficult task. This obstacle can be overcome using heuristic optimization algorithm in combination with traditional techniques. Based on experimental results it was demonstrated that NMPC keeps the coupled variables flow-rate and temperature at set point in the entire two-phase region with minimal control activity. As a consequence the mechanical stress of actuators and energy consumption is kept low. References: R. Brandstätter, Development of strategies for the realisation of a modular micro-distillation, diploma thesis, Graz University of Technology, Graz, 2007.