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

Özkan, Gülay; Hapoğlu, Hale; Alpbaz, Mustafa

doi:10.1016/s0255-2701(97)00047-0

Cited by 34 publications

(11 citation statements)

References 15 publications

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“…other types of model-based control has demonstrated that process modelling and identification is the most time-consuming task. In this study, ARMAX model for related system has been developed and parameters of these models are found by using identification techniques [21].…”

Section: Parametric Difference Equation Model and Identificationmentioning

confidence: 99%

Control of dissolved oxygen concentration using neural network in a batch bioreactor

Mete

Özkan

Hapoğlu

et al. 2012

Comp Applic In Engineering

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ABSTRACT:Artificial neural networks (ANN) have been utilized for many chemical applications because of their ability to learn system features. This paper presents the use of feedforward neural networks for dynamic modelling and dissolved oxygen (DO) control of a batch yeast fermentation. The ARMAX model of this nonlinear process is also presented. Model verification is tested by using experimental data. Different ANNs are trained using the backpropagation learning algorithm. The resulting ANNs are introduced in a Model Predictive Control (MPC) scheme to test the control performance of the structure. At system, output variable that is DO concentration and adjusting variable that is air flow rate are chosen. The robustness of this control structure is studied in the case of setpoint changes. Results obtained with NN-MPC is also presented and compared with those obtained with Nonlinear Auto Regressive Moving Average (NARMA-L2) control strategy.

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Section: Parametric Difference Equation Model and Identificationmentioning

confidence: 99%

Control of dissolved oxygen concentration using neural network in a batch bioreactor

Mete

Özkan

Hapoğlu

et al. 2012

Comp Applic In Engineering

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“…Inglis et al548 successfully applied a long‐range predictive controller (generalized predictive controller) to control conversion in the polymerization of MMA in a continuous stirred‐tank reactor (CSTR) reactor. Another study that also employed generalized predictive control was developed by Ozkan et al549 The algorithm was implemented to track temperature in a polySTY polymerization reactor. The results obtained were compared to those using a proportional‐integral‐derivative (PID) controller and under certain experimental conditions, it turned out that the generalized predictive controller performed better than the PID controller.…”

Section: Other Topics Related To Sensorsmentioning

confidence: 99%

A Critical Overview of Sensors for Monitoring Polymerizations

Fonseca

Dubé

Penlidis

2009

Macro Reaction Engineering

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The available on‐line and in‐line sensor technologies developed for polymerization reactors from 1990 until today are discussed and critically reviewed. About 600 references are included, which evidence the growth in sensor technology in the last two decades. Sensors for operational parameters in polymer reactors (i.e. temperature, pressure, level and flow) as well as sensors for polymer property monitoring (i.e. calorimetry, chromatography and spectroscopy, among others) are included. Complementary topics such as state estimation, multivariate statistical methods, fault diagnosis techniques and optimal sensor selection and location are briefly covered.

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

Cited by 34 publications

References 15 publications

Control of dissolved oxygen concentration using neural network in a batch bioreactor

Control of dissolved oxygen concentration using neural network in a batch bioreactor

A Critical Overview of Sensors for Monitoring Polymerizations

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

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