Non-linear model based control of a propylene polymerization reactor

Ali, Majid; Betlem, Bernardus H.L.; Weickert, G.; Roffel, Brian

doi:10.1016/j.cep.2006.07.012

Cited by 18 publications

(7 citation statements)

References 29 publications

(34 reference statements)

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“…The developed control scheme is capable of dealing with the shortcomings of GMC. The detailed development of this scheme can be found in [17][18][19]; in this investigation, a brief description of this derivation is given below.…”

Section: Control Algorithmmentioning

confidence: 99%

Optimal grade transition control for liquid‐propylene polymerization reactor

Ali

2010

Asian Journal of Control

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A nonlinear control system integrating an off-line optimizer and a nonlinear model-based controller is developed to perform optimal grade transition operations in a continuous pilot plant reactor. A simple black-box model is developed and used to determine optimal trajectories of inputs and outputs for a series of three polypropylene grades. The simplified model is also used to develop a nonlinear controller. This controller is similar to generic model control; however, the integral action is omitted and an on-line updating scheme is incorporated to update pre-specified model parameters using delayed process measurements. The time optimal inputs, which are calculated by the off-line optimizer, are introduced to the plant in a feedforward manner. At the same time, the deviations from the optimal output are corrected using the feedback nonlinear controller. The simulations on a complex mechanistic model of the process reveal that the nonlinear control scheme performs well for both set point tracking and disturbance rejection. This paper integrates well-known methodologies, such as the generic-model control algorithm, parameter update schemes, and off-line optimization, together to develop an applicable and robust control technique for continuous polymerization reactors.

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Section: Control Algorithmmentioning

confidence: 99%

Optimal grade transition control for liquid‐propylene polymerization reactor

Ali

2010

Asian Journal of Control

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“…The difficulty also found in the on-line monitoring of the product (polymer) quality because of the main quality variables such as composition and molecular weight distribution cannot be measured on-line. If it is at all possible, there are still other problems associated with the measurement such as sampling problems, large dead times and/or high noise levels [2].…”

Section: Introductionmentioning

confidence: 99%

“…Although the studies on the modeling and control of polymerization process in fluidized bed reactor are available in the literature [2,[4][5][6], but minimal work has been done on the polymerization of propylene, specifically copolymerization of propylene and ethylene, for both modeling and control. shows the simplified schematic of fluidized bed propylene and ethylene copolymerization reactor.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-GMC Control of Gas-Phase Propylene Copolymerization in Fluidized Bed Reactor

et al. 2018

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A modified two-phase model for gas phase propylene and ethylene copolymerization was chosen to represent the process in a fluidized bed reactor. This model considered the entrainment of solid particles in the reactor, as a modification to the original two-phase model assumptions. The non-linearity of this process makes it difficult to control just by using conventional controller such as PID. A hybrid control strategy (a simple designed fuzzy logic controller (FLC) integrated with generic model control (GMC)) is designed to control the temperature of the reactor. This advanced control system was compared with the GMC and conventional PID controller. The simulation results showed that the hybrid controller (Fuzzy-GMC) performed better than both GMC and PID in terms of both servo and regulatory control.

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“…So far, several works have been carried out to predict MI with various types of modeling methods by scholars and practitioners [1][2][3][4][5][6][7][8][9]. Among them, taking the multiscale process characteristics into account, Shi [10,11] combined multiscale analysis and independent principal component with Radial Basis Function (RBF) neural network (NN) for MI modeling.…”

Section: Introductionmentioning

confidence: 99%

Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction

Wang

Zhang

Liu

2015

Journal of Chemometrics

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This paper presents the development of wavelet neural network (WNN) with an improved fruit fly optimization algorithm (IFOA) for the melt index prediction in the industrial propylene polymerization process. The structure, calculation, and prediction process of WNN are proposed, and the improved details of IFOA are introduced, which can enhance the searching efficiency and improve the searching quality over the traditional fruit fly optimization algorithm. Finally, the WNN-IFOA model can obtain the least predicting errors compared with other existing models and shows better generality for the online melt index prediction from the experimental results.

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Non-linear model based control of a propylene polymerization reactor

Cited by 18 publications

References 29 publications

Optimal grade transition control for liquid‐propylene polymerization reactor

Optimal grade transition control for liquid‐propylene polymerization reactor

Fuzzy-GMC Control of Gas-Phase Propylene Copolymerization in Fluidized Bed Reactor

Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction

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