Control structure selection and model predictive control of the Weyerhaeuser digester problem

Wisnewski, Philip A.; Doyle, Francis J.

doi:10.1016/s0959-1524(98)00014-6

Cited by 31 publications

(23 citation statements)

References 12 publications

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“…In summary, selecting a small value of lignin content change at the top of the digester, e.g., Δρ sL (z top ) ∈ [1,3], is a reasonable choice for u 3 .…”

Section: A the Influence Of U 3 On The Identification Processmentioning

confidence: 99%

“…Due to the page limit, we only illustrate a model error model in Fig. 5, that describe the model error concerning the output y 3 . The model error model is a high-order black-box model identified using a black-box identification toolbox [18], and it provides helpful information for the controller design with robustness considerations.…”

Section: Model Validationmentioning

confidence: 99%

“…Funkquist [1], [2] used a grey-box identification approach for model parameter estimation of a continuous digester. Wisnewski et al [3] applied an identification procedure based on normalized moments of an impulse response to identify a set of linear models used for the model predictive control of a continuous digester. Alexandridis [4] applied the partial least squares method in model identification based on a Purdue digester model proposed by Wisnewski et al [5].…”

Section: Introductionmentioning

confidence: 99%

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Physical Model Parameter Estimation of a Nonlinear Process

Ding

Gustafsson

Johansson

2006

2006 IEEE International Conference on Control Applications

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Abstract-A physical model of a nonlinear subprocess in a continuous paper pulp digester is discussed and simplified. Model approximation is carried out in order to produce a simple linear model to be used for unknown parameter estimation of the physical model. The Taylor series expansion and the orthogonal collocation method are applied for the model linearization and model lumping, respectively. The reduced model is expressed as a standard state space form. The model parameters are estimated in the least squares sense, and the parameters retain their own physical meanings. The results of the parameter estimation are discussed and the model is verified using validation data. I. INTRODUCTIONProcess models play a more and more important role in the development of control technology. Modelling of a continuous paper pulp digester is a troublesome task due to a complex mixture of chemical reactions and transport phenomena within the process. System identification is a good alternative to model a complex process, for example, a digester. Comparing with black-box models, the parameters in a physical model bear certain physical meanings, which can be very useful in the diagnostic procedure or the controller design. The current study aims to deliver a simple linear model for a distributed parameter nonlinear process. For this process a complex nonlinear physical model is already available, and there exist unknown parameters in the physical model. The unknown parameters can be identified based on the simple linear model that is an approximation of the complex physical model, and the parameter estimates retain their own physical meanings. A subprocess in a continuous paper pulp digester is selected as an application example. In the next section, a physical digester model is simplified for the selected subprocess. Then the model is linearized and lumped. In Section 3, a software tool developed for the parameter estimation is applied and the parameter estimation results are analyzed. Model validation is discussed in Section 4. Section 5 gives conclusions.

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“…In summary, selecting a small value of lignin content change at the top of the digester, e.g., Δρ sL (z top ) ∈ [1,3], is a reasonable choice for u 3 .…”

Section: A the Influence Of U 3 On The Identification Processmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Physical Model Parameter Estimation of a Nonlinear Process

Ding

Gustafsson

Johansson

2006

2006 IEEE International Conference on Control Applications

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“…Despite these advantages of profile control, only the endpoint property is commonly controlled in large-scale distributed parameter systems. For example, control of bottom and top product compositions in a distillation column [1,2] as well as endpoint Kappa number control in a continuous pulp digester [3,4] have been reported in the literature. An obvious advantage of the endpoint property control is that we typically have an online or laboratory measurement of the property of interest at the exit and this facilitates its implementation.…”

Section: Introductionmentioning

confidence: 99%

Profile control in distributed parameter systems using lexicographic optimization based MPC

Padhiyar

Bhartiya

2009

Journal of Process Control

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“…Funkquist [1,2] used a grey-box identification approach for model parameter estimation of a continuous digester. Wisnewski and Doyle [3] applied an identification procedure based on normalized moments of an impulse response to identify a set of linear models used for model predictive control of a continuous digester. Alexandridis [4] applied the partial least squares method in model identification based on a Purdue digester model proposed by Wisnewski et al [5].…”

Section: Introductionmentioning

confidence: 99%

Model parameter estimation of simplified linear models for a continuous paper pulp digester

Ding

Gustafsson

Johansson

2007

Journal of Process Control

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A physical model of a continuous paper pulp digester is simplified and two subprocesses selected from the digester are modelled by coupled linear partial differential equations. This study focuses on the parameter identification of the simplified linear models. Finitedimensional approximation of the model is made and a software package developed for identification of distributed parameter processes is applied. This identification system is developed for flexibility to allow identification for different choices of subprocesses and process variables. Unknown parameters of the subprocess models are estimated and the results are illustrated by process simulation and model validation.

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Control structure selection and model predictive control of the Weyerhaeuser digester problem

Cited by 31 publications

References 12 publications

Physical Model Parameter Estimation of a Nonlinear Process

Physical Model Parameter Estimation of a Nonlinear Process

Profile control in distributed parameter systems using lexicographic optimization based MPC

Model parameter estimation of simplified linear models for a continuous paper pulp digester

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