Nonlinear control of polymerization reactor

Özkan, Gülay; Özen, Şükrü; Erdoğan, Sebahat; Hapoğlu, Hale; Alpbaz, Mustafa

doi:10.1016/s0098-1354(01)00651-2

Cited by 29 publications

(17 citation statements)

References 10 publications

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“…The mathematical formulation of the minimum time-optimal temperature policy was originally solved by Sacks and coworkers [19]. Consequently, this approach prompted several researchers to apply them in optimal control studies due to their simplicity [20][21][22][23][24]. By using Hamiltonian and the model equations, an equation for the optimal temperature was obtained as:…”

Section: Optimal Temperature Policymentioning

confidence: 99%

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

2007

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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.

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Section: Optimal Temperature Policymentioning

confidence: 99%

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

2007

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“…In our case we do not use relaxation as a tuning parameter, instead, we propose to use a first-order time constant, which will be more acceptable from an engineering point of view. As shown in Appendix B, update of the model parameters K 3 and K 4 proceeds according to the following equation: (15) in which k is the time step, j = 3 when the process variable pv is the conversion and j = 4 when the process variable is the melt index; e is the error between the measured process output and the estimated process output using the simplified model. The coefficient α depends on the process conditions.…”

Section: Parameterization Of the Simplified Modelmentioning

confidence: 99%

“…There are numerous examples in the literature of linear control approaches applied to polymerization reactor control, such as, PI cascade control [6], dynamic matrix control [7,8], generalized predictive control [9] and adaptive internal model control [10]. Examples of the application of non-linear control approaches are, amongst others, globally linearizing control [11][12][13] and non-linear model predictive control [14,15]. There are also some approaches in which linear control is used, combined with non-linear models for setpoint updating [16].…”

Section: Introductionmentioning

confidence: 99%

Non-linear model based control of a propylene polymerization reactor

Ali

Betlem

Weickert

et al. 2007

Chemical Engineering and Processing: Process Intensification

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Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract A modified generic model controller is developed and tested through a simulation study. The application involves model-based control of a propylene polymerization reactor in which the monomer conversion and melt index of the produced polymer are controlled by manipulating the reactor cooling water flow and the inlet hydrogen concentration.Non-linear control is designed using a simplified non-linear model, in order to demonstrate the robustness of the control approach for modeling errors. Two model parameters are updated online in order to ensure that the controlled process outputs and their predicted values track closely. The controller is the static inverse of the process model with setpoints of the measured process outputs converted to setpoints for some of the state variables.The simulation study shows that the proposed controller has good setpoint tracking and disturbance rejection properties and is superior to the conventional generic model control and Smith predictor control approaches.

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“…The models used for these predictions have been linear approximations of the process which are obtained experimentally from step response data. Unfortunately, strong nonlinear behavior is often found in real chemical processes such as highly exothermic batch polymerization reactors [2][3][4]. There are also some approaches in which linear control is used, combined with non-linear models for setpoint updating [5].…”

Section: Introductionmentioning

confidence: 99%

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

Özkan

Tekin

Hapoğlu

2009

Korean J. Chem. Eng.

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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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Nonlinear control of polymerization reactor

Cited by 29 publications

References 10 publications

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

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

Non-linear model based control of a propylene polymerization reactor

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

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