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        Application of experimental non-linear control based on generic algorithm to a polymerization reactor

        Gülay Özkan,Öznur Tekin,Hale Hapoglu 한국화학공학회 2009 Korean Journal of Chemical Engineering Vol.26 No.5

        The dynamics of free radical polymerization of styrene and on-line control of temperature in a cooling jacketed batch polymerization reactor is investigated. The benzoyl peroxide initiator is introduced into the reactor once at the beginning of the reaction to obtain the desired monomer conversion and the desired average chain length in a minimum reaction time. The optimal constant set temperature, which is generally realized in industrial applications, and the set profile are used as two different optimal operating conditions. The temperature control of the polymerization reactor is achieved experimentally and theoretically. The control of nonlinear systems has progressed considerably, and various nonlinear process model based control techniques have appeared in the literature. The problem is how to tune the controller in order to obtain comparable closed loop responses. Generic model control (GMC) is applied and the performance of the control results are compared with the previously published control results.

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        Discrete system identification and self-tuning control of dissolved oxygen concentration in a stirred reactor

        Bulent Akay,Suna Ertunc,Havva Boyacioglu,Hale Hapoglu,Mustafa Alpbaz 한국화학공학회 2011 Korean Journal of Chemical Engineering Vol.28 No.3

        This work presents the applications of discrete-time system identification and generalized minimum variance (GMV) control of dissolved oxygen (DO) level in a batch bioreactor in which Saccharomyces cerevisiae is produced at aerobic condition. Air flow rate and mixing rate were varied to determine the maximum local liquid phase volumetric mass transfer coefficient (K_La). Maximum K_La value was determined at a mixing rate of 600 rpm and air flow rate of 3.4 Lmin^(−1). For control purpose, manipulated variable was selected as air flow rate due to its effectiveness on the K_La. To examine the dynamic behavior of the bioreactor, various input signals were utilized as a forcing function and three different model orders were tested. A second0order controlled auto regressive moving average (CARMA) model was used as the process model in the control algorithm and in the system identification step. It is concluded that the ternary input is more suitable than the other input types used in this work for system identification. Recursive least squares method (RLS) was used to determine the model parameters. GMV control results were compared with the traditional PID control results by using performance criteria of IAE and ITAE for different types of DO set point trajectories. DO concentration in the batch bioreactor was controlled more successfully with an adaptive controller structure of GMV than the PID controller with fixed parameters.

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