Sebahat Erdoğan

Nonlinear control of polymerization reactor

Abstract In this work, nonlinear model based control was applied to the free radical solution polymerization of styrene in a jacketted batch reactor and its performance was examined to reach the required monomer conversion and molecular weight. Optimal temperature profiles for the properties of polymer quality were evaluated using the Hamiltonian optimization method. Total simulation program having mass and energy balances of the jacketed polymerization reactor was used to calculate the optimal trajectories. For control purposes, several experimental and theoretical dynamic studies have been made to observe the validity of simulation program. Experimental and theoretical nonlinear model based control have been investigated to track the temperature at the optimal trajectory Two types of parametric and nonparametric models were evaluated to achieve the temperature control. For this purpose, reaction curve was obtained to calculate the system dynamic matrix as a nonparametric model. In all control work, heat input to the reactor was chosen as a manipulated variable. Nonlinear auto regressive moving average exogenous (NARMAX) giving a relation between heat input and reactor temperature was chosen to represent the system dynamic and this model was used to describe the related control system as a parametric model. NARMAX model parameters were determined by using Levenberg Marquard algorithm. A pseudo random binary sequence (P.R.B.S.) signal was employed to disturb the system. Total simulation program was used to calculate the system and control parameters. Several types and orders were used to construct the NARMAX models. The efficiency and the performance of the nonlinear model based control with the NARMAX model and dynamic matrix were tested to calculate the best model. Nonlinear model based control system was used to control the reactor temperature at desired temperature trajectory experimentally and theoretically. Theoretical simulation results were compared with experimental control data. It was concluded that the control simulation program represents the behavior of the controlled reactor temperature well. In addition, nonlinear model based control keeps the reactor temperature of optimal trajectory satisfactorily.

Control of a polymerization reactor by fuzzy control method with genetic algorithm

The optimal temperature control of a batch jacketed free radical polymerization reactor by fuzzy control method with genetic algorithm (GA) is considered. The manipulated variable was chosen as the heat given by the immersed heater. A key issue in this study is to generate sets of fuzzy control membership function and relation matrix using GA, which can be easily implemented and an efficient method for optimization problems. The fitness function for GA is chosen as the integral of the absolute value of error (IAE). By using the fuzzy parameters obtained for three different optimal temperature profiles, the efficiency of the fuzzy controller with GA was examined by simulation and experimentally. It was seen that GA is able to tune the fuzzy controller efficiently for different situations and therefore to control the temperature of the polymerization reactor.

APPLICATION OF ADAPTIVE PID CONTROL WITH GENETIC ALGORITHM TO A POLYMERIZATION REACTOR

This article describes the application of adaptive PID control with genetic algorithm (GA) to a jacketed batch polymerization reactor. This method was used to keep the polymerization reactor temperature at the desired optimal path, which was determined by the Hamiltonian maximum principle method. The reactor was simulated and the model equations of this jacketed polymerization reactor were solved by means of Runge-Kutta-Felthberg methods. A genetic algorithm can be a good solution for finding the optimum PID parameters because unlike other techniques it does not impose many limitations and it is simple. In this research, suitability of these parameters was checked by the integral absolute error (IAE) criterion. The control parameters in the PID algorithm were changed with time during the control of a polymerization reactor. It was seen that the genetic algorithm was able to tune the PID controller used in this system in terms of higher robustness and reliability by changing the parameters continuously.

The effect of operational conditions on the performance of batch polymerization reactor control

In this work, the effect of operational conditions on the performance of a controlled batch polymerization reactor was investigated experimentally. The effect of agitation speed on conversion and heat transfer coefficient in free radical chain growth polymerization in this controlled, stirred, jacketed batch reactor was also investigated. The transient response of the polymerization reactor following sequential step changes in agitation speed has been obtained experimentally. The experiments were conducted under optimal loading conditions calculated by using Lagrange’s multiplier method. The reactor temperature was controlled by manipulating the heat input to the reactor. Some correlations are also provided relating the viscosity and the overall heat transfer coefficient to the monomer conversion.

TEMPERATURE CONTROL OF A BATCH POLYMERIZATION REACTOR

In this work, DMC (Dynamic Matrix Control) of a batch solution polymerization reactor has been investigated experimentally and by simulation to achieve a specific constant number average chain length and conversion in a minimum time. The process variables are reaction temperature and initiator initiation concentration. The performance of the DMC controller was compared with that of the IMC (Internal Model Control) controller. It was seen that DMC and IMC controllers yielded a good performance in maintaining the reactor temperature at its set point at the isothermal conditions.

Fuzzy control with genetic algorithm in a batch bioreactor.

In this study, the growth medium temperature in a batch bioreactor was controlled at the set point by using fuzzy model-based control method. Fuzzy control parameters which are membership functions and relation matrix were found using genetic algorithm. Heat input given from the immersed heater and the cooling water flow rate were selected as the manipulated variables in order to control the growth medium temperature in the bioreactor. Controller performance was tested in the face of different types of input variables. To eliminate the noise on the temperature measurements, first-order filter was used in the control algorithm. The achievement of the temperature control was analyzed in terms of both microorganism concentration which was reached at the end of the stationary phase and the performance criteria of Integral of the Absolute Error. It was concluded that the cooling flow rate was suitable as manipulated variable with regard to microorganism concentration. On the other hand, performance of the controller was satisfactory when the heat input given from the immersed heater was manipulated variable.

Nonlinear model based control of optimal temperature profiles in polystyrene polymerization reactor

In this work, nonlinear model based control was applied to the free radical solution polymerization of styrene in a jacketted batch reactor and its performance was examined to reach the required monomer conversion and molecular weight. Optimal temperature profiles for the properties of polymer quality were evaluated using the Hamiltonian optimization method. Total simulation program having mass and energy balances of the jacketed polymerization reactor was used to calculate the optimal trajectories. For control purposes, several experimental and theoretical dynamic studies have been made to observe the validity of simulation program. Experimental and theoretical nonlinear model based control have been investigated to track the temperature at the optimal trajectory Two types of parametric and nonparametric models were evaluated to achieve the temperature control. For this purpose, reaction curve was obtained to calculate the system dynamic matrix as a nonparametric model. In all control work, heat input to the reactor was chosen as a manipulated variable. NARMAX (Nonlinear Auto Regressive Moving Average eXogenous) giving a relation between heat input and reactor temperature was chosen to represent the system dynamic and this model was used to desing the related control system as a parametric model. NARMAX model parameters were determined by using Levenberg Marquard algorithm. A Pseudo Random Binary Sequence (P.R.B.S.) signal was employed to disturb the system. Total simulation program was used to calculate the system and control parameters. Several types and orders were used to construct the NARMAX models. The efficiency and the performance of the nonlinear model based control with the NARMAX model and dynamix matrix were tested to calculate the best model. Nonlinear model based control system was used to control the reactor temperature at the desired temperature trajectory experimentally and theoretically. Theoretical simulation results were compared with experimental control data. It was concluded that the control simulation program represents the behavior of the controlled reactor temperature well. In addition, nonlinear model based control keeps the reactor temperature of optimal trajectory satisfactorily.