Profile control in distributed parameter systems using lexicographic optimization based MPC

Padhiyar, Nitin; Bhartiya, Sharad

doi:10.1016/j.jprocont.2008.01.005

Cited by 23 publications

(5 citation statements)

References 37 publications

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“…Both linearization of the physics-based models and subspace identification from the data were adopted. On the other hand, nonlinear MPC based on complex physics-based methods, and data-driven methods such as ANN, MLR and PLS, have also been studied [94,97,116,117]. Interestingly enough, the focus shifted along the way from end-point Kappa number control to Kappa number profile control.…”

Section: Discussion and Future Research Directions Of Pulp Digesters mentioning

confidence: 99%

“…The authors used a self-organizing map (SOM) to monitor the process and the information was used to switch on the controller or switch it off. Padhiyar and Bhartiya [97] proposed a lexicographic optimization-based MPC that enforces priorities to achieve the blow-line Kappa number target when the target Kappa number profile is unachievable due to model-plant mismatch, unmeasured disturbances and input limitations. Galicia et al [98] showed that the closed-loop performance of PID can be significantly improved by feedbacking Kappa number prediction from a recursive reduced order dynamic PLS model.…”

Section: Kappa Number Controlmentioning

confidence: 99%

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A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

2020

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Being at the heart of modern pulp mills, continuous pulp digesters have attracted much attention from the research community. In this article, a comprehensive review in the area of modeling, control and diagnostics of continuous pulp digesters is conducted. The evolution of research focus within these areas is followed and discussed. Particular effort has been devoted to identifying the state-of-the-art and the research gap in a summarized way. Finally, the current and future research directions in the areas have been analyzed and discussed. To date, digester modeling following the Purdue approach, Kappa number control using model predictive controllers and health index-based diagnostic approaches by utilizing different statistical methods have dominated the field. While the rising research interest within the field is evident, we anticipate further developments in advanced sensors and integration of these sensors for improving model prediction and controller performance; and the exploration of different AI-based approaches will be at the core of future research.

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Section: Discussion and Future Research Directions Of Pulp Digesters mentioning

confidence: 99%

Section: Kappa Number Controlmentioning

confidence: 99%

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

2020

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“…The authors concluded that the target Kappa profile was achievable only in 'normal' conditions and profile may not be achievable in the presence of model-plant mismatch, unmeasured disturbances and input limitations. In a later publication, Padhiyar and Bhartiya [33] proposed a lexicographic optimization-based MPC that enforces priorities to achieve the blow-line Kappa target when the target Kappa profile is unachievable due to the aforementioned reasons. Wisnewski and Doyle [34] analyzed performance of three different MPCs-each employing a different plant model, with varying degrees of complexity-based on their ability to reject stochastic, measured and unmeasured disturbances.…”

Section: Predictive Control Of the Pulp Digestermentioning

confidence: 99%

An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters

2019

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Kappa number variability at the continuous digester outlet is a major concern for pulp and paper mills. It is evident that the aforementioned variability is strongly linked to the feedstock wood properties, particularly lignin content. Online measurement of lignin content utilizing near-infrared spectroscopy at the inlet of the digester is paving the way for tighter control of the blow-line Kappa number. In this paper, an innovative approach of feedforwarding the lignin content to a model predictive controller was investigated with the help of modeling and simulation studies. For this purpose, a physics-based modeling library for continuous pulp digesters was developed and validated. Finally, model predictive control approaches with and without feedforwarding the lignin measurement were evaluated against current industrial control and proportional-integral-derivative (PID) schemes.

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“…In MPC, the process dynamics (linear or nonlinear) can be treated as constraints of the optimization, while the multiple control objectives, which are often encountered in industrial processes, are enforced by a set of properly defined objective functions. In Padhiyar and Bhartiya, 29 lexicographic optimization-based MPC was proposed for profile control of plug flow reactors. The controller utilizes lexicographic optimization to prioritize different sections of the profile in case the target profile is unachievable.…”

Section: Introductionmentioning

confidence: 99%

Estimation and predictive control of nonlinear diffusion processes with application to drying of coatings

Cao

Ayalew

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article deals with the estimation and control of a class of distributed parameter processes dominated by nonlinear diffusion. The major challenges in control of such systems lie in the nonlinear infinite-dimensional nature of the systems and the lack of direct sensing for relevant system states. In the proposed scheme, the proper orthogonal decomposition-Galerkin method is adopted to derive a reduced-order model in terms of temporal coefficients from the original system described by nonlinear partial differential equation(s). To overcome the sensing problem, the unscented Kalman filter is implemented to estimate the temporal coefficients of the reduced-order model online and subsequently reconstruct the distributed system states. A set of improved sufficient conditions are also established to ensure stability of the estimation scheme. Then, once these difficulties are addressed, a nonlinear model predictive control scheme is formulated to achieve desired control objectives such as trajectory tracking for distributed states, energy optimization and quality control. The proposed estimation and control scheme is demonstrated via an application to infrared drying of coatings in the automotive industry.

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Profile control in distributed parameter systems using lexicographic optimization based MPC

Cited by 23 publications

References 37 publications

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters

Estimation and predictive control of nonlinear diffusion processes with application to drying of coatings

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