Modeling and control of cell wall thickness in batch delignification

Choi, Hyun‐Kyu; Kwon, Joseph

doi:10.1016/j.compchemeng.2019.06.025

Cited by 38 publications

(18 citation statements)

References 36 publications

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“…In this work, a multiscale model for a batch pulp digester is established by improving the previously developed multiscale model 29,30 . Figure 1 clearly shows the pulping process is indeed a multiscale problem.…”

Section: Multiscale Modeling Of Pulp Digestermentioning

confidence: 99%

“…One novel aspect of this work is that important fiber morphological properties such as cell wall thickness and fiber length are microscopically described by integrating the macroscopic model with a microscopic model (i.e., kMC algorithm). Even though Choi and Kwon 29,30 successfully modeled and controlled the cell wall thickness evolution in a pulp cooking process, the fiber length evolution was not studied. It is important to note that the modeling of fiber length evolution is indeed a multiscale problem as the dimension of fiber length (mm) is much greater than that of cell wall thickness (μm).…”

Section: Multiscale Modeling Of Pulp Digestermentioning

confidence: 99%

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Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester

Choi

Kwon

2020

AIChE Journal

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Even though it is widely known that mechanical properties of papers are dependent upon fiber morphology such as fiber length and cell wall thickness, existing macroscopic models are limited in describing the microscopic traits of pulp. Thus, we proposed a multiscale model by integrating a macroscopic model (i.e., Purdue model) and a microscopic model (i.e., kinetic Monte Carlo algorithm) to capture the dynamic evolution of the fiber morphology as well as conventional pulp quality index such as Kappa number. Then, a reduced‐order model is identified to handle the computational requirement of the multiscale model, and implemented to a model‐based controller to regulate both the fiber length and the Kappa number which are expressed in the forms of conflicting objective functions. The epsilon‐constraint method is employed to find the Pareto optimal sets to provide decision makers with the degree of freedom to choose one according to their preferred end‐use paper properties.

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Section: Multiscale Modeling Of Pulp Digestermentioning

confidence: 99%

Section: Multiscale Modeling Of Pulp Digestermentioning

confidence: 99%

Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester

Choi

Kwon

2020

AIChE Journal

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“…To handle this limitation, Choi and Kwon 20 developed a multiscale modeling framework for pulp digesters that combines a kinetic Monte Carlo (kMC) model 21–24 with the extended Purdue model, 25 which is the most commonly used macroscopic model for pulp digesters, to describe the evolution of microscopic attributes of fibers as well as that of macroscopic phenomena during pulping. Based on this multiscale modeling framework, Choi and Kwon 26 developed a multiscale model that tracks the CWT value of fibers and considers the fiber collapse phenomenon. Then, Choi and Kwon 27 further improved the multiscale model to capture the fiber breakage phenomenon during pulping.…”

Section: Introductionmentioning

confidence: 99%

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

2021

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This work presents the application of a Koopman operator approach to a batch pulp digester. To manufacture paper products with desired properties, it is essential to consider both macroscopic and microscopic attributes of pulp. However, the complexity of multiscale dynamics of pulping processes hinders proper control system design. Therefore, we utilize extended dynamic mode decomposition (EDMD), which is based on Koopman operator theory, to derive a global linear representation of a pulp digester. Then, we design an offset‐free Koopman‐based model predictive control (KMPC) system to regulate the Kappa number and cell wall thickness (CWT) of fibers at a batch pulp digester while compensating for the influence of plant‐model mismatch and disturbance during operation. The numerical experiments demonstrate that the linear state‐space model, obtained via EDMD, properly predicts the behavior of a batch pulp digester, and the designed offset‐free KMPC system successfully drives the Kappa number and CWT to set‐point values.

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“…4,5,33 Specifically, the proposed kMC model tracks individual glycans on a cell membrane as well as their interactions with lectins over time, which allows one to simulate the temporal evolution of the surface configuration and consider its impacts on the binding dynamics. 17,[36][37][38][39][40][41] Moreover, as the kMC is a stochastic modeling approach, it simulates the binding kinetics in a probabilistic manner to take into account the stochasticity of lectinglycan binding processes due to the low copy numbers of lectins and glycans. 36 One remaining challenge associated with the kMC model proposed for studying lectin-glycan binding processes is the computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

HybridPDE‐kMCmodeling approach to simulate multivalent lectin‐glycan binding process

Lee

Green

et al. 2021

AIChE Journal

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Glycans are the major components of the cellular membranes and mediate many cellular processes via their interactions with lectins. A kinetic Monte Carlo (kMC) model was proposed previously to incorporate the key features of glycan-lectin interactions such as multivalency and glycan diffusion, and its accuracy has been validated by experiments. However, computational cost of the kMC model is its major bottleneck.In this study, a hybrid model combining a partial differential equation (PDE) with the kMC model is proposed to greatly reduce the computational cost while preserving the accuracy. Specifically, glycan diffusion is simulated by the PDE for improving computational efficiency since the glycan diffusion execution through the kMC is computationally expensive. The hybrid PDE-kMC model is employed to simulate the binding dynamics between cholera toxin subunit B and gangliosides on cellular membranes. The accuracy and efficiency of the proposed model was demonstrated by comparing with the sole kMC model.

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Modeling and control of cell wall thickness in batch delignification

Cited by 38 publications

References 36 publications

Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester

Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester

HybridPDE‐kMCmodeling approach to simulate multivalent lectin‐glycan binding process

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