Nonlinear predictive control of a drying process using genetic algorithms Ugur Yuzgec, a, * Yasar Becerikli, b,† Mustafa Turker c,‡ a Department of Electronics and Telecommunications Engineering, Kocaeli University, 41040, Kocaeli, Turkey b Department of Computer Engineering, Kocaeli University, 41040, Kocaeli, Turkey c Pakmaya, P.O. Box 149, 41001, Kocaeli, Turkey Received 30 June 2005; accepted 10 January 2006 Abstract A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker’s yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a genetic algorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker’s yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control. © 2006 ISA—The Instrumentation, Systems, and Automation Society. Keywords: Genetic algorithm; Predictive controller; Optimization; Drying process 1. Introduction Predictive control is a member of advanced discrete-time process control algorithms. This con- trol algorithm is based on the use of an explicit process model to predict the manipulated variables and thus the future control actions are optimized throughout a finite horizon. To obtain a good per- formance, a process model describing the effects of all the different inputs on all the outputs must be developed. Besides objective function all the control goals must be included 1,2. Linear model predictive control is suitable for processes that are not highly nonlinear. But many industrial pro- cesses have strong nonlinearities and predictive control is applied in order to provide satisfactory control results 3. Two problems have appeared because of the introduction of nonlinearities in the predictive control. The first of the problems is that the modeling of processes is much more difficult and complex than the linear case. The second im- portant problem in nonlinear predictive control is the solving of the optimization problem. The con- ventional iterative optimization methods are very sensitive to the initialization of the algorithm and usually lead to unacceptable solutions due to the convergence to local optima 4. In recent years, several numerical search methods for optimization have been presented due to the complexity and the *E-mail address: uyuzgec@kou.edu.tr † Corresponding author. E-mail address: becer@kou.edu.tr; ybecer@ieee.org ‡ E-mail address: mustafat@pakmaya.com.tr ISA Transactions ® Volume 45, Number 4, October 2006, pages 589–602 0019-0578/2006/$ - see front matter © 2006 ISA—The Instrumentation, Systems, and Automation Society.