An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters

Rahman, Moksadur; Avelin, Anders; Kyprianidis, Konstantinos

doi:10.3390/pr7090602

Cited by 14 publications

(14 citation statements)

References 33 publications

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“…Araneda et al [33] adapted the Purdue model to simulate an industrial Lo-Solids pulp digester and thus showed 9.1% savings of white liquor can be achieved by adapting operating conditions. Recently, Rahman et al [6] developed a dynamic continuous digester model for real-time simulation by following reaction kinetics similar to Bhartiya et al [29] and simplified compaction similar to Fernandes and Castro [15]. The authors developed an object-oriented modeling library in Modelica language [34] for modeling various commercial digesters.…”

Section: Purdue Modelmentioning

confidence: 99%

“…The residence time between these points can be between four to six hours. This literally means that the instantaneous Kappa number measurement is a result of past process input parameters that existed four to six hours earlier [6]. Moreover, blow-line Kappa number is affected by a huge number of process variables.…”

Section: Kappa Number Controlmentioning

confidence: 99%

“…The reduced order controller model was built using the simulation data to design a Luenberger observer for state estimation. Recently, Rahman et al [6] examined a feedforward MPC approach by combining a state-space model identified from complex first-principles model and a near infrared (NIR) soft-sensor for online lignin content measurement. The authors demonstrated that the feedforward MPC performed superiorly to both PID and MPC without feedforwarding lignin content.…”

Section: Kappa Number Controlmentioning

confidence: 99%

See 2 more Smart Citations

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: Purdue Modelmentioning

confidence: 99%

Section: Kappa Number Controlmentioning

confidence: 99%

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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“…Some authors presented an approach for predicting the kappa number using chemical reaction kinetics (Sixta and Rutkowska, 2007;Germgård, 2017), physical phenomena (Rantanen, 2006;Laakso, 2008), Near Infrared regression models (Monrroy et al, 2008;Santos et al, 2014, Moral et al 2015 or advanced process predictive model control tools (Badwe, 2016;Rahman et al, 2017).…”

Section: Kraft Pulping Modelingmentioning

confidence: 99%

Predicting Kappa Number in a Kraft Pulp Continuous Digester: A Comparison of Forecasting Methods

Correia¹,

Dangelo

Almeida

et al. 2018

Braz. J. Chem. Eng.

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This paper discusses kappa number prediction models using Single Exponential Smoothing, Multiple Linear Regression Analysis, the Time Series Method of Box-Jenkins (ARIMA) and Artificial Neural Networks. Applying a database of an industrial eucalyptus Kraft pulp continuous digester, these four different methods were evaluated according to different statistical decision criteria. After fitting the parameters of the models, validations were performed using a new dataset. Results, advantages and limitations of the four methods were compared. Some statistical forecasting indexes indicate that the ARIMA model showed more accurate estimation results, achieving a MAPE lower than 3 % and over 90% of the prediction data deviations lower than one kappa unit.

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“…Andersson et al 11 and Sixta et al 12 performed comparative analysis on the accuracy of these kinetic models, and concluded that the Purdue model has the best structure in both incorporating the process complexity and optimizing the computational efficiency. Motivated by the modeling efforts and tight regulations on product quality, the model predictive control (MPC) has been widely used for different types of pulp digesters, 13‐18 and these works have been extended to design plantwide control frameworks 19,20 …”

Section: Introductionmentioning

confidence: 99%

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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An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters

Cited by 14 publications

References 33 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

Predicting Kappa Number in a Kraft Pulp Continuous Digester: A Comparison of Forecasting Methods

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

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