Adaptive fuzzy control of MMA batch polymerization reactor based on fuzzy trajectory definition (original) (raw)

Takagi-Sugeno fuzzy control of batch polymerization reactors

Soft Computing in Engineering Design and Manufacturing, pp. 420-429, 1998

It is a well-known fact that batch processes are gaining wider ground in chemical industries. Compared with continuous processes the control of batch processes is more difficult because of the physical and chemical properties of the contents, such as heat capacity, heat transfer coefficient and reaction rate, which vary from run to run and within runs. The control problem focuses on the temperature control of a polystyrene batch reactor using the rule based Takagi-Sugeno fuzzy controller based on the controller output error method. The proposed learning fuzzy logic controllers are shown to be capable of providing a good overall system performance.

Fuzzy control of a nylon polymerization semi-batch reactor

Fuzzy Sets and Systems, 2009

Batch and semi-batch polymerization reactors with specified trajectories for certain process variables present challenging control problems. This work reports, results and procedures related to the application of PI (proportional and integral) fuzzy control in a semi-batch reactor for the production of nylon 6. Closed loop simulation results were based on a phenomenological model adjusted for a commercial reactor and they attest to the potential benefits and versatility of the use of PI fuzzy control in polymerization systems.

Temperature Control of a Bench-Scale Batch Polymerization Reactor for Polystyrene Production

Chemical Engineering & Technology, 2007

Batch polymerization reactors commonly use optimal temperature control as the strategic operation parameter. This strategy allows for better operability and a more economic process. The main objective of the batch polymerization reactor control is to obtain acceptable product quality. Direct measurement of polymer quality is rarely achievable, which makes the online control of the reactor difficult. Temperature is the most controllable operational variable in the polymer reactor, which is seen to have a direct effect on the polymer properties. Temperature is chosen as the set point by using either the isothermal temperature or optimal temperature trajectory. Online control of the optimal temperature profile of a bench-scale batch polymerization reactor was experimentally investigated in this study. The temperature trajectory was used as the target for controllers to follow. The time-profile temperature was obtained with the objective of obtaining the desired conversion and number-average chain length within the minimum time. Two advanced controls of fuzzy logic control and generic model control were applied to the polymer reactor. A comparison of the controllers reveals that both performed better than conventional controllers.

Neural-fuzzy modelling of polymer quality in batch polymerization reactors

The estimation of parameters and obtaining an accurate and comprehensive mathematical model of the polymerization process is of strategic importance to the control engineering purposes in the polymerization industry. It is characteristic for these processes a grate non-linearity and many difficulties applying traditional estimation techniques. This paper describes an approach based upon neural-fuzzy representation of the model. A concrete model is constructed with the Sugeno fuzzy inference technique and a fuzzy-neural network is used to model the dynamic behavior of the polymer process. Such neural-fuzzy models of polymer quality could be used successfully for optimization and control of polymerization processes. Short example for such implementation is included with additional results for modeling of Mn and Mw.

Nonlinear temperature control of a batch suspension polymerization reactor

Polymer Engineering & Science, 2002

T his paper concerns molecular weight control of a batch polymerization reactor where suspension polymerization of methyl methacrylate(MMA) takes place. For this purpose, a cascade control structure with two control loops has been selected. The slave loop is used for temperature control using on-line temperature measurements, and the master loop controls the average molecular weights based on its estimated values. Two different control algorithms namely proportional-integral(PI) controller and globally linearizing controller (GLC) have been used for temperature control. An estimator, which has the structure of an extended Kalman filter (EKF), is used for estimating monomer conversion and average molecular weights of polymer using reactor temperature measurements. The performance of proposed control algorithm is evaluated through simulation and experimental studies. The results indicate that a constant average molecular weight cannot be achieved in case of strong gel effect. However, the polydispersity of product will be lower in comparison to isothermal operation. It is also shown that in case of model mismatch, the performance of cascade control is superior compared to the case where only reactor temperature is controlled based on desired temperature trajectory obtained through cascade strategy.

Self-Tuning Control of Batch Polymerization Reactor

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, 1998

The optimal temperature control of a batch jacketed free radical polymerization reactor with self-tuning PID and conventional PID control methods are considered. The controller is implemented on a digital computer to track precalculated optimal temperature trajectories obtained for different initiator initiation concentration in a batch styrene polymerization reactor. The performance of the self-tuning controller is compared against that of a PID controller. Self-tuning controller provides good, robust control in despite of the nonlinear dynamics of the polymerization reactor system.

Nonlinear Adaptive Control of a Continuous Polymerization Reactor

Elsevier eBooks, 1994

NonIinear control strategies are suitable for processes presenting important nonlinearitics. In the present polymerizatioo process, a wide domain of monomer cooversion is reachabIe and the gel effect is considered, imposing large variations in the model. A precise dynamic model is thus required. In this study, an adaptive control for a multi-input multi-output linearizable system, together with an extended Kalman filter, is applied to a simulated continuous polymerization reactor to follow two output set-points (mooomer cooversion and reactor temperature) in the presence of model parameter uncertainties. Simulation results show that this technique is robust and the output performance can be ensured even in the presence of large model parameter errors or variations and disturbances.

Nonlinear control of polymerization reactor

Computers & Chemical Engineering, 2001

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.