Temperature control of industrial gas phase polyethylene reactors

Dadebo, S. A.; Bell, M.L.; McLellan, P. James; McAuley, Kimberley B.

doi:10.1016/s0959-1524(96)00016-9

Cited by 53 publications

(63 citation statements)

References 21 publications

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“…4) corresponding to X 3 = 1.307, q c = 0.35 kg/h, Y o = 1.15, Y F = 1.2, U o /U mf = 6. This point is just below the softening point of the polymer, which is approximately 400 K (the type of polymer considered by Dedebo et al [1], McAuley et al [3], Choi and Ray [4] and Ghasem [5]). …”

Section: Closed Loop (Controlled) Bifurcation Analysismentioning

confidence: 87%

“…Mass balance on hydrogen and co-monomers has not been considered in the system model, because hydrogen and comonomer have only mild effects on the reactor dynamics (McAuley et al [3]). The temperature in the reaction zone can be adjusted instantaneously by manipulating the flow rate of the cooling water in the external heat exchanger, or by adjusting the cooling rates in the heat exchanger by instantaneous blending of cold and warm water streams while maintaining a constant total cooling water flow rate through the heat exchanger (Dadebo et al [1]). The temperature in the reaction zone can also be adjusted by manipulating the coolant temperature of the reactor walls (the case considered in the present work).…”

Section: System Model Descriptionmentioning

confidence: 99%

“…In large reactors (e.g., volume of the reactor =150 m 3 ), the reactor walls are usually insulated and the reactor is operated in an adiabatic mode, while the temperature inside the reactor is adjusted by the heat exchanger used to cool the recycle gas to maintain the reaction zone temperature below the polymer melting point. Putting the equations of the two-phase model (Choi and Ray [4] and Ghasem [5]) in dimensionless form gives 1) : Material balance l Bubble phase (ethylene)…”

Section: Model Developmentmentioning

confidence: 99%

“…High purity ethylene and catalyst are continuously fed into the fluidized-bed reactor, where ethylene and co-monomers such as 1-butene or 1-hexene are polymerized together at a pressure of 20±30 atm and a narrow temperature range. In gas phase ethylene polymerization reactors, tight temperature control is of utmost importance to ensure that the temperature in the reaction zone is kept above the dew point of the reactants, yet below the melting point of the polymer to prevent melting and consequent agglomeration of the product particles (Dadebo et al [1]). Therefore, most commercial gas phase fluidized bed polyethylene reactors are operated in the relatively narrow temperature range of 248± 400 K (Xie et al [2]).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Behavior of Industrial Gas Phase Fluidized Bed Polyethylene Reactors under PI Control

Ghasem¹

2000

Chem Eng & Technol

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A mathematical model describing the UNIPOL process for the production of polyethylene in the gas phase using a Ziegler-Natta catalyst in a bubbling fluidized bed is used to analyze the major processes determining the behavior and performance of these industrially important units. The investigation shows that both static bifurcation (multiplicity of the steady states) as well as dynamic bifurcation (stable/unstable periodic attractors) behavior cover wide regions of the design and operating parameter domain. A conventional proportional-integral (PI) control policy is suggested to stabilize the behavior of the system. The control philosophy covers both aspects of stabilizing unstable steady states as well as compensating for external disturbances. It is shown that for some controller configurations and set points the controlled process can go through a period doubling sequence leading to chaotic strange attractors. The industrial implications of the phenomena discovered for both the open loop (uncontrolled) as well the closed-loop (controlled) systems are analyzed.

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Section: Closed Loop (Controlled) Bifurcation Analysismentioning

confidence: 87%

Section: System Model Descriptionmentioning

confidence: 99%

Section: Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Behavior of Industrial Gas Phase Fluidized Bed Polyethylene Reactors under PI Control

Ghasem¹

2000

Chem Eng & Technol

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“…To perform this covariance estimation step, two nonlinear process examples are considered: (1) a continuous stirred-tank reactor (CSTR) example from 45 and (2) a published model of a gas-phase ethylene copolymerization process. [46][47][48] These estimated noise covariances are used to specify the noise statistics of MHE and EKF state estimators. Using the illustrative CSTR example, these estimators are implemented and compared to show that high-quality state estimates can be obtained after the determination of their noise statistics by ALS.…”

Section: Introduction and Prior Workmentioning

confidence: 99%

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

Lima

Rawlings

2011

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com).State estimation from plant measurements plays an important role in advanced monitoring and control technologies, especially for chemical processes with nonlinear dynamics and significant levels of process and sensor noise. Several types of state estimators have been shown to provide high-quality estimates that are robust to significant process disturbances and model errors. These estimators require a dynamic model of the process, including the statistics of the stochastic disturbances affecting the states and measurements. The goal of this article is to introduce a design method for nonlinear state estimation including the following steps: (i) nonlinear process model selection, (ii) stochastic disturbance model selection, (iii) covariance identification from operating data, and (iv) estimator selection and implementation. Results on the implementation of this design method in nonlinear examples (CSTR and large dimensional polymerization process) show that the linear time-varying autocovariance least-squares technique accurately estimates the noise covariances for the examples analyzed, providing a good set of such covariances for the state estimators implemented. On the estimation implementation, a case study of a chemical reactor demonstrates the better capabilities of MHE when compared with the extended Kalman filter.

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Fault‐tolerant control of nonlinear process systems subject to sensor faults

et al. 2007

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in Wiley InterScience (www.interscience.wiley.com).The problem of control of nonlinear process systems subject to input constraints and sensor faults (complete failure or intermittent unavailability of measurements) is considered. A fault-tolerant controller is designed that utilizes reconfiguration (switching to an alternate control configuration) in a way that accounts for the process nonlinearity, the presence of constraints and the occurrence of sensor faults. To clearly illustrate the importance of accounting for the presence of input constraints, first the problem of sensor faults that necessitate sensor recovery to maintain closed-loop stability is considered. We address the problem of determining, based on stability region characterizations for the candidate control configurations, which control configuration should be activated (reactivating the primary control configuration may not preserve stability) after the sensor is rectified. We then consider the problem of asynchronous measurements, that is of intermittent unavailability of measurements. To address this problem, the stability region (that is, the set of initial conditions starting from where closed-loop stabilization under continuous availability of measurements is guaranteed), as well as the maximum allowable data loss rate which preserves closed-loop stability for the primary and the candidate backup configurations are computed. This characterization is utilized in identifying the occurrence of a destabilizing sensor fault, and in activating a suitable backup configuration that preserves closed-loop stability. The proposed method is illustrated using a chemical process example and demonstrated via application to a polyethylene reactor.

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Temperature control of industrial gas phase polyethylene reactors

Cited by 53 publications

References 21 publications

Dynamic Behavior of Industrial Gas Phase Fluidized Bed Polyethylene Reactors under PI Control

Dynamic Behavior of Industrial Gas Phase Fluidized Bed Polyethylene Reactors under PI Control

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

Fault‐tolerant control of nonlinear process systems subject to sensor faults

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