Dynamic optimization with simulated annealing
In this paper we present an approach for the dynamic optimization of energy and chemical engineering processes with the simulated annealing (SA) algorithm. The aim of this work is to develop an optimization methodology which finds optimal control strategies requiring a minimum of user input. The methodology we propose uses rigorous dynamic Simulink models based on first principles in a black-box approach. If any, only slight modifications have to be done for existing Simulink models to be able to be optimized. The presented approach based on the SA algorithm has the potential to find the global optimum and it does not need any additional information than the dynamic model itself. Both control profiles and time-invariant parameters, such as PI control parameters can be optimized simultaneously. The simulated annealing algorithm is implemented in a set of MATLAB script files together with a graphical user interface (GUI) as an independent module of the OptControlCentre (OCC). All parameters of the simulated annealing algorithm and of the model can be specified in the GUI as well as the course of the optimization itself is made visible at runtime. The proposed methodology is tested on a set of example problems and the influence of several simulated annealing parameters on the optimization is shown. (C) 2004 Elsevier Ltd. All rights reserved.
simulink black-box approach dynamic models stochastic methods start-up batch reactor