Application of offset-free Koopman-based model predictive control to a batch pulp digester
Sang Hwan Son
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing
Search for more papers by this authorHyun-Kyu Choi
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
Contribution: Conceptualization, Data curation, Investigation, Validation, Writing - review & editing
Search for more papers by this authorCorresponding Author
Joseph Sang-Il Kwon
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
Correspondence
Joseph Sang-Il Kwon, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College station, TX 77840.
Email: [email protected]
Contribution: Conceptualization, Formal analysis, Funding acquisition, Resources, Supervision, Writing - review & editing
Search for more papers by this authorSang Hwan Son
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing
Search for more papers by this authorHyun-Kyu Choi
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
Contribution: Conceptualization, Data curation, Investigation, Validation, Writing - review & editing
Search for more papers by this authorCorresponding Author
Joseph Sang-Il Kwon
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
Correspondence
Joseph Sang-Il Kwon, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College station, TX 77840.
Email: [email protected]
Contribution: Conceptualization, Formal analysis, Funding acquisition, Resources, Supervision, Writing - review & editing
Search for more papers by this authorFunding information: Artie McFerrin Department of Chemical Engineering and the Energy Institute, Texas A and M University
Abstract
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.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
REFERENCES
- 1Garside M. Production of Paper and Cardboard Worldwide 2007–2017. Statista, Hamburg, Germany: Technical Report; 2020. https://www.statista.com/statistics/270314/production-of-paper-and-cardboard-in-selected-countries/. Accessed 12 May 2021.
- 2Garside M. Global Paper Production Volume from 2008 to 2018 by Type. Statista, Hamburg, Germany: Technical Report; 2020. https://www.statista.com/statistics/270317/production-volume-of-paper-by-type/. Accessed 12 May 2021.
- 3Cheremisinoff NP, Rosenfeld P. Handbook of Pollution Prevention and Cleaner Production Vol. 3: Best Practices in the Agrochemical Industry. Vol 3. Norwich: William Andrew; 2010.
- 4Wisnewski PA, Doyle FJ III, Kayihan F. Fundamental continuous-pulp-digester model for simulation and control. AIChE J. 1997; 43(12): 3175-3192.
- 5Gustafson RR, Sleicher CA, McKean WT, Finlayson BA. Theoretical model of the Kraft pulping process. Ind Eng Chem Proc Des Dev. 1983; 22(1): 87-96.
- 6Masura V. A mathematical model of Kraft pulping related to the alkali concentration in the cooking liquor. Wood Sci Technol. 1999; 33(5): 381-389.
- 7Smith CC, Williams TJ. Mathematical Modelling, Simulation and Control of the Operation of a Kamyr Continuous Digester for the Kraft Process. West Lafayette, IN, USA: Purdue Laboratory for Applied Industrial Control, Schools of Engineering, Purdue University; 1974.
- 8Bhartiya S, Dufour P, Doyle FJ III. Fundamental thermal–hydraulic pulp digester model with grade transition. AIChE J. 2003; 49(2): 411-425.
- 9Lee JH, Datta A. Nonlinear inferential control of pulp digesters. AIChE J. 1994; 40(1): 50-64.
- 10Paulonis MA, Krishnagopalan A. Kappa number and overall yield calculation based on digester liquor analysis. Tappi J (USA). 1988; 71(11): 185-187.
- 11Wisnewski PA, Doyle FJ III. A reduced model approach to estimation and control of a Kamyr digester. Comput Chem Eng. 1996; 20: S1053-S1058.
- 12Wisnewski PA, Doyle FJ III. Control structure selection and model predictive control of the Weyerhaeuser digester problem. J Process Control. 1998; 8: 487-495.
- 13Amirthalingam R, Lee JH. Subspace identification based inferential control of a continuous pulp digester. Comput Chem Eng. 1997; 21: S1143-S1148.
- 14Kesavan P, Lee JH, Saucedo V, Krishnagopalan GA. Partial least squares (PLS) based monitoring and control of batch digesters. J Process Control. 2000; 10: 229-236.
- 15Chari NC. Integrated control-system approach for batch digester control. Tappi. 1973; 56(7): 65-68.
- 16Padhiyar N, Gupta A, Gautam A, et al. Nonlinear inferential multi-rate control of kappa number at multiple locations in a continuous pulp digester. J Process Control. 2006; 16(10): 1037-1053.
- 17Liang Z, Ioannidis M, Chatzis I. Geometric and topological analysis of three-dimensional porous media: pore space partitioning based on morphological skeletonization. J Colloid Interface Sci. 2000; 221(1): 13-24.
- 18Facada MJ. Influence of Kraft paper quality on the performance of an industrial paper impregnation process [PhD thesis]. Lisboa, Portugal: Universidade Tecnica de Lisboa; 2015.
- 19Johansson A. Correlations between Fibre Properties and Paper Properties [Master's thesis]. Stockholm, Sweden:KTH Royal Institute of Technology; 2011.
- 20Choi HK, Kwon JSI. Multiscale modeling and control of kappa number and porosity in a batch-type pulp digester. AIChE J. 2019; 65(6):e16589.
- 21Sitapure N, Epps R, Abolhasani M, Kwon JSI. Multiscale modeling and optimal operation of millifluidic synthesis of perovskite quantum dots: towards size-controlled continuous manufacturing. Chem Eng J. 2020; 413:127905.
- 22Kwon JSI, Nayhouse M, Christofides PD. Multiscale, multidomain modeling and parallel computation: application to crystal shape evolution in crystallization. Ind Eng Chem Res. 2015; 54(47): 11903-11914.
- 23Kwon JSI, Nayhouse M, Christofides PD, Orkoulas G. Protein crystal shape and size control in batch crystallization: comparing model predictive control with conventional operating policies. Ind Eng Chem Res. 2014; 53(13): 5002-5014.
- 24Kwon JSI, Nayhouse M, Orkoulas G, Christofides PD. Enhancing the crystal production rate and reducing polydispersity in continuous protein crystallization. Ind Eng Chem Res. 2014; 53(40): 15538-15548.
- 25Andersson N, Wilson DI, Germgård U. An improved kinetic model structure for softwood Kraft cooking. Nord Pulp Pap Res J. 2003; 18(2): 200-209.
- 26Choi HK, Kwon JSI. Modeling and control of cell wall thickness in batch delignification. Comput Chem Eng. 2019; 128: 512-523.
- 27Choi HK, Kwon JSI. Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester. AIChE J. 2020; 66(7):e16972.
- 28Son SH, Choi HK, Kwon JSI. Multiscale modeling and control of pulp digester under fiber-to-fiber heterogeneity. Comput Chem Eng. 2020; 143:107117.
- 29Wisnewski PA, Doyle F. Model-based predictive control studies for a continuous pulp digester. IEEE Trans Control Syst Technol. 2001; 9(3): 435-444.
- 30Koopman BO. Hamiltonian systems and transformation in Hilbert space. Proc Natl Acad Sci U S A. 1931; 17(5): 315.
- 31Koopman B, Neumann JV. Dynamical systems of continuous spectra. Proc Natl Acad Sci U S A. 1932; 18(3): 255.
- 32Rowley CW, Mezić I, Bagheri S, Schlatter P, Henningson DS. Spectral analysis of nonlinear flows. J Fluid Mech. 2009; 641(1): 115-127.
- 33Mauroy A, Mezić I. On the use of Fourier averages to compute the global isochrons of (quasi) periodic dynamics. Chaos. 2012; 22(3):033112.
- 34Peitz S, Klus S. Koopman operator-based model reduction for switched-system control of PDEs. Automatica. 2019; 106: 184-191.
- 35Schmid PJ. Dynamic mode decomposition of numerical and experimental data. J Fluid Mech. 2010; 656: 5-28.
- 36Tu JH, Rowley CW, Luchtenburg DM, Brunton SL, Kutz JN. On dynamic mode decomposition: theory and applications. arXiv Preprint. 2013; arXiv:1312.0041: 1-30.
- 37Williams MO, Kevrekidis IG, Rowley CW. A data–driven approximation of the Koopman operator: extending dynamic mode decomposition. J Nonlinear Sci. 2015; 25(6): 1307-1346.
- 38Williams MO, Hemati MS, Dawson ST, Kevrekidis IG, Rowley CW. Extending data-driven Koopman analysis to actuated systems. IFAC-PapersOnLine. 2016; 49(18): 704-709.
- 39Son SH, Park BJ, Oh TH, Kim JW, Lee JM. Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences. Int J Control. 2020; 1-13.
- 40Son SH, Oh TH, Kim JW, Lee JM. Move blocked model predictive control with improved optimality using semi-explicit approach for applying time-varying blocking structure. J Process Control. 2020; 92: 50-61.
- 41Arbabi H, Korda M, Mezic I. A data-driven Koopman model predictive control framework for nonlinear flows. arXiv Preprint. 2018; arXiv:1804.05291: 1-25.
- 42Korda M, Mezić I. Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control. Automatica. 2018; 93: 149-160.
- 43Narasingam A, Kwon JSI. Koopman Lyapunov-based model predictive control of nonlinear chemical process systems. AIChE J. 2019; 65(11):e16743.
- 44Narasingam A, Kwon JSI. Data-driven feedback stabilization of nonlinear systems: Koopman-based model predictive control. arXiv Preprint. 2020; arXiv:2005.09741: 1-11.
- 45Son SH, Narasingam A, Kwon JSI. Handling plant-model mismatch in Koopman Lyapunov-based model predictive control via offset-free control framework. arXiv Preprint. 2020; arXiv:2010.07239: 1-12.
- 46Korda M, Susuki Y, Mezić I. Power grid transient stabilization using Koopman model predictive control. IFAC-PapersOnLine. 2018; 51(28): 297-302.
- 47Hanke S, Peitz S, Wallscheid O, Klus S, Böcker J, Dellnitz M. Koopman operator-based finite-control-set model predictive control for electrical drives. arXiv Preprint. 2018; arXiv:1804.00854: 1-10.
- 48Sootla A, Mauroy A, Ernst D. Optimal control formulation of pulse-based control using Koopman operator. Automatica. 2018; 91: 217-224.
- 49Narasingam A, Kwon JSI. Development of local dynamic mode decomposition with control: application to model predictive control of hydraulic fracturing. Comput Chem Eng. 2017; 106: 501-511.
- 50Narasingam A, Siddhamshetty P, Kwon JSI. Handling spatial heterogeneity in reservoir parameters using proper orthogonal decomposition based ensemble Kalman filter for model-based feedback control of hydraulic fracturing. Ind Eng Chem Res. 2018; 57(11): 3977-3989.
- 51Bangi MSF, Narasingam A, Siddhamshetty P, Kwon JSI. Enlarging the domain of attraction of the local dynamic mode decomposition with control technique: application to hydraulic fracturing. Ind Eng Chem Res. 2019; 58(14): 5588-5601.
- 52Narasingam A, Kwon JSI. Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation. J Process Control. 2020; 91: 25-36.
- 53Son SH, Kim JW, Oh TH, Lee JM. Model-plant mismatch learning offset-free model predictive control. arXiv Preprint. 2020; arXiv:2012.02753: 1-17.
- 54Pannocchia G, Rawlings JB. Disturbance models for offset-free model-predictive control. AIChE J. 2003; 49(2): 426-437.
- 55Pannocchia G, Bemporad A. Combined design of disturbance model and observer for offset-free model predictive control. IEEE Trans Autom Control. 2007; 52(6): 1048-1053.
- 56Budišić M, Mohr R, Mezić I. Applied Koopmanism. Chaos. 2012; 22(4):047510.
- 57Proctor JL, Brunton SL, Kutz JN. Generalizing Koopman theory to allow for inputs and control. SIAM J Appl Dyn Syst. 2018; 17(1): 909-930.
- 58Grüne L, Pannek J. Nonlinear Model Predictive Control. Cham, Switzerland: Springer; 2017: 45-69.
- 59Alanqar A, Ellis M, Christofides PD. Economic model predictive control of nonlinear process systems using empirical models. AIChE J. 2015; 61(3): 816-830.
- 60Mhaskar P, El-Farra NH, Christofides PD. Stabilization of nonlinear systems with state and control constraints using Lyapunov-based predictive control. Syst Control Lett. 2006; 55(8): 650-659.
- 61de la Peña DM, Christofides PD. Lyapunov-based model predictive control of nonlinear systems subject to data losses. IEEE Trans Autom Control. 2008; 53(9): 2076-2089.
- 62Christensen T, Albright LF, Williams TJ. A mathematical model of the Kraft pulping process [PhD thesis]. West Lafayette, Indiana: Purdue University; 1982.
- 63Son SH, Oh SK, Park BJ, Song MJ, Lee JM. Idle speed control with low-complexity offset-free explicit model predictive control in presence of system delay. arXiv Preprint. 2020; arXiv:2012.02859: 1-14.
- 64Jo YP, Bangi MSF, Son SH, Kwon JSI, Hwang SW. Dynamic modeling and offset-free predictive control of LNG tank. Fuel. 2020; 285:119074.
- 65Korda M, Mezić I. On convergence of extended dynamic mode decomposition to the Koopman operator. J Nonlinear Sci. 2018; 28(2): 687-710.
- 66Boyd JP. Chebyshev and Fourier Spectral Methods. Ann Arbor, MI: Courier Corporation; 2001.
- 67Wendland H. Meshless Galerkin methods using radial basis functions. Math Comput. 1999; 68(228): 1521-1531.
- 68Karniadakis G, Sherwin S. Spectral/hp Element Methods for Computational Fluid Dynamics. Oxford, England: Oxford University Press; 2013.
- 69Maeder U, Borrelli F, Morari M. Linear offset-free model predictive control. Automatica. 2009; 45(10): 2214-2222.
- 70Kautsky J, Nichols NK, Van Dooren P. Robust pole assignment in linear state feedback. Int J Control. 1985; 41(5): 1129-1155.
- 71Chen CT, Chen CT. Linear System Theory and Design. Vol 301. New York: Holt, Rinehart and Winston; 1984.
- 72Savitha S, Sadhasivam S, Swaminathan K. Modification of paper properties by the pretreatment of wastepaper pulp with Graphium putredinis, Trichoderma harzianum and fusant xylanases. Bioresour Technol. 2009; 100(2): 883-889.
- 73Bao A, Gildin E, Narasingam A, Kwon JS. Data-driven model reduction for coupled flow and geomechanics based on DMD methods. Fluids. 2019; 4(3): 138.