Mustafa Alpbaz

Generalized predictive control of optimal temperature profiles in a polystyrene polymerization reactor

The performance of generalized predictive control (GPC) was examined when it was applied to the temperature of a free radical solution polymerization of styrene in a jacketed batch reactor. The optimal temperature policies were obtained at different initiator concentrations by applying the optimal control theory to the relevant polymerization reactor. The use of polynomial ARIMAX model related with reactor temperature and heat input was emphasised. Model parameters were determined by using Kalman, Bierman and Levenberg Marquardt algorithms. A pseudo random binary sequence (PRBS) signal was employed to operate the system. The GPC control method was based on ARIMAX model. The performance of the GPC method was compared with that of the PID controller.

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.

Adaptive heuristic temperature control of a batch polymerisation reactor

The purpose of this research is to improve and apply the temperature control of a free radical solution polymerisation of styrene and to examine its performance on the basis of adaptive heuristic criticism (AHC) control. This algorithm consists of a three-layer feed forward artificial neural network (ANN) which uses supervised learning with reinforcement in a unique topology. This algorithm has two neurone-like adaptive elements and a difficult learning control problem which can be solved by means of a learning system with a single associative search element (ASE) and a single adaptive critic element (ACE). AHC uses a type of control system whose output value is either maximum or minimum. The controller will take in process data on-line and update the weights to proper ones in the control of the process. The performance results of the AHC controller are compared with the results obtained by using conventional Deadbeat algorithm. AHC control system shows satisfactory behaviour to track the reactor temperature.

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.

Optimal temperature control in a batch polymerization reactor using fuzzy-relational models-dynamics matrix control

In this work, fuzzy-relational models-dynamics matrix control (Fuzzy-DMC) 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, molecular weight and chain length in a minimum time. The reactor temperature was controlled by manipulating the heat-input to the reactor. The performance of the Fuzzy-DMC controller was compared with that of the nonlinear generalized predictive control (NLGPC).

Generalized minimum variance control of optimal temperature profiles in a polystyrene polymerization reactor

In this paper, the dynamic, optimization and on-line temperature control of the free radical polymerization of styrene in a cooling jacketed batch polymerization reactor were investigated theoretically and experimentally. Time varying optimal temperature trajectories to obtain a product with desired quality in a minimum time are calculated using optimal control theories. The polynomial CARMA model for reactor temperature and heat input to the reactor was used to design the relevant control system. The CARMA model parameters were calculated by applying the Bierman algorithm. A Pseudo Random Binary Sequence (PRBS) signal was given to disturb the system. Generalized minimum variance (GMV) control based on the CARMA model was applied to the polymer reactor. Theoretical and experimental results were in agreement and it was shown that the GMV method controlled the system very well at optimal trajectory.

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.

Application of optimal adaptive generalized predictive control to a packed distillation column

Abstract In this work, optimal operating conditions for a packed distillation column and optimal adaptive generalized predictive control (OA-GPC) were investigated. Thus, the dynamic and steady-state properties of the packed distillation column distilling methanol–water mixture were observed experimentally and theoretically. Mathematical models for the packed distillation column were solved with orthogonal collocation on finite elements. Optimal operating conditions of the system were found by using Box–Wilson optimization method and “Experimental Design” technique. Two types of control algorithm were utilized for controlling the packed distillation column, viz. conventional proportional integral derivative (PID) and generalized predictive control (GPC) at optimal operating conditions. Overhead temperature control was examined experimentally and theoretically. Pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of the polynomial ARIMAX model. Generally theoretical and experimental control results were in accord with each other and it was observed that OA-GPC produced better performance than PID for the packed distillation column.

Generalized Predictive Control to a packed distillation column for regulatory problems

Abstract In this work, adaptive Generalized Predictive Control (GPC) was investigated at the optimal operating conditions for a pilot plant binary packed distillation column. The studieswere made experimentally and theoretically. The dynamic behavior of the distillation column has been simulated using backmixing model and solved by utilizing Hermite Polynomials within the finite element procedure. The control of the overhead product temperature was examined for both experimental and theoretical works. Perturbations in feed composition were utilized as the disturbance and the reboiler heat duty was selected as the manipulated variable. Pseudo Random Binary Sequence (PRBS) signal and Bierman algorithm were used to estimate the relevant parameters of the system model for GPC. Generally theoretical and experimental control results were in good agreement with each other.