On-line modelling and predictive control of an industrial terpolymerization reactor

Ogunnaike, Babatunde A.

doi:10.1080/00207179408923101

Cited by 97 publications

(48 citation statements)

References 10 publications

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“…A dynamic extension is possible if the calculations are performed using a dynamically weighted sensitivity matrix (18) where and are defined as before and is a dynamic weighting factor that is used to obtain an appropriate relative weighting of dynamic and steady-state data. The proposed method possibly can be extended to nonlinear steady-state analysis by averaging the sensitivity matrices generated at multiple steady states (19) where is the number of steady-state points and is the dimensionless sensitivity matrix at steady-state point . Both of these extensions currently are being studied for the EPDM kinetic parameter-estimation problem.…”

Section: Discussionmentioning

confidence: 99%

“…Several EPDM kinetic models have been presented in the open literature [17]- [19]. In this paper, we utilize the kinetic model of Cozewith [17] shown in Table I.…”

Section: A Epdm Reactor Dynamic Modelmentioning

confidence: 99%

“…Detailed model equations are not provided here for the sake of brevity. The interested reader is referred to [19] for further details on the general model structure.…”

Section: A Epdm Reactor Dynamic Modelmentioning

confidence: 99%

“…We assume that the polymer-production rate , the polymer contents of ethylene and diene , and the weight-average molecular weight are measured every six minutes without delay. Although not pursued in this study, the Mooney viscosity (a primary indicator of product quality) can be inferred from [19]. The off-line parameter-estimation problem is formulated as the constrained nonlinear optimization problem where (5) is the vector of measurements and is the th plant measurement.…”

Section: B Parameter Estimationmentioning

confidence: 99%

“…is the th state variable which represents a reactant molar concentration or MWD moment, is the concentration of the th state variable in the feed stream which is nonzero only for the reactants, is the feed volumetric flow rate, is the constant reactor volume, and is a reaction rate function that depends nonlinearly on the state variables and linearly on the parameters [19]. The complete model has 29-state variables representing seven reactant concentrations, four zeroth-order moments, 12 first-order moments and six-bulk second-order mo- ments.…”

Section: A Epdm Reactor Dynamic Modelmentioning

confidence: 99%

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Selection of Model Parameters for Off-Line Parameter Estimation

Henson

Kurtz

2004

IEEE Trans. Contr. Syst. Technol.

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Abstract-Mechanistic dynamic models often contain unknown parameters whose values are difficult to determine even with highly specialized laboratory experiments. A practical approach is to estimate such parameters from available process data. Typically only a subset of the parameters can be estimated due to restrictions imposed by the model structure, lack of measurements, and limited data. We present a simple parameter selection method which accounts for the first two factors independent of the data available for parameter estimation. The magnitude of each parameter effect on the measured variables is quantified by applying principal-component analysis to the steady-state parameter-output sensitivity matrix. The uniqueness of each parameter effect is determined by computing the minimum distance between the sensitivity vector of the particular parameter and the vector spaces spanned by sensitivity vectors of the parameters already selected for estimation. A recursive algorithm that provides a tradeoff between the magnitude and linear independence of parameter effects yields a ranking of the parameters according to their inherent ease of estimation. The parameter-selection procedure is applied to the problem of kinetic parameter estimation for an industrial model of a polymerization reactor. For this specific example, the proposed method yields superior estimation results than those obtained with a parameter-selection technique based on the Fisher information matrix (FIM).

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Section: Discussionmentioning

confidence: 99%

“…Several EPDM kinetic models have been presented in the open literature [17]- [19]. In this paper, we utilize the kinetic model of Cozewith [17] shown in Table I.…”

Section: A Epdm Reactor Dynamic Modelmentioning

confidence: 99%

“…Detailed model equations are not provided here for the sake of brevity. The interested reader is referred to [19] for further details on the general model structure.…”

Section: A Epdm Reactor Dynamic Modelmentioning

confidence: 99%

Section: B Parameter Estimationmentioning

confidence: 99%

Section: A Epdm Reactor Dynamic Modelmentioning

confidence: 99%

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