Dynamic optimization oriented modeling and nonlinear model predictive control of the wet limestone FGD system

doi:10.1016/j.cjche.2019.07.017

Abstract

Abstract: Nonlinear model predictive control (NMPC) scheme is an effective method of multi-objective optimization control in complex industrial systems. In this paper, a NMPC scheme for the wet limestone flue gas desulphurization (WFGD) system is proposed which provides a more flexible framework of optimal control and decision-making compared with PID scheme. At first, a mathematical model of the FGD process is deduced which is suitable for NMPC structure. To equipoise the model's accuracy and conciseness, the wet limestone FGD system is separated into several modules. Based on the conservation laws, a model with reasonable simplification is developed to describe dynamics of different modules for the purpose of controller design. Then, by addressing economic objectives directly into the NMPC scheme, the NMPC controller can minimize economic cost and track the set-point simultaneously. The accuracy of model is validated by the field data of a 1000 MW thermal power plant in Henan Province, China. The simulation results show that the NMPC strategy improves the economic performance and ensures the emission requirement at the same time. In the meantime, the control scheme satisfies the multiobjective control requirements under complex operation conditions (e.g., boiler load fluctuation and set point variation). The mathematical model and NMPC structure provides the basic work for the future development of advanced optimized control algorithms in the wet limestone FGD systems.

Key words: Wet limestone flue gas desulphurization (WFGD) system, Modeling, Nonlinear model predictive control (NMPC), Multi-objective optimization

Lukuan Yang, Wenqi Zhong, Li Sun, Xi Chen, Yingjuan Shao. Dynamic optimization oriented modeling and nonlinear model predictive control of the wet limestone FGD system[J]. Chinese Journal of Chemical Engineering, 2020, 28(3): 832-845.

References

[1] D. Wu, X.Z. Ma, S.Q. Zhang, Integrating synergistic effects of air pollution control technologies:More cost-effective approach in the coal-fired sector in China, J. Clean. Production 199(20) (2018) 1035-1042.
[2] C.L. Gage, Limestone dissolution in modelling of slurry scrubbing for flue gas desulfurization, Ph.D, Thesis, University of Texas, Texas 1989.
[3] R.S. Argarwal, G.T. Rochelle, Chemistry of Limestone Slurry Scrubbing, SO₂ Control Symposium 3, 2015.
[4] C. Brogren, H.T. Karlson, A model for prediction of limestone dissolution in wet flue gas desulfurization applications, Ind. Eng. Chem. Res. 36(9) (1997) 3889-3897.
[5] D. Eden, M. Luckas, A heat and mass transfer model for the simulation of the wet limestone flue gas scrubbing process, Chem. Eng. Technol. 21(1) (1998) 56-60.
[6] H. Kikkawa, H. Kaku, S. Nozawa, K. Kohata, New wet FGD process using granular limestone, Proceedings of power gen Europe (1997) 585-601.
[7] J. Warych, M. Szymanowski, Model of the wet limestone flue gas desulfurization process for cost optimization, Ind. Eng. Chem. Res. 40(12) (2001) 2597-2605.
[8] S. Kiil, M.L. Michelsen, K.D. Johansen, Experimental investigation and modelling of a wet flue gas desulphurisation pilot plant, Ind. Eng. Chem. Res. 37(1998) 2792-2806.
[9] M.X. Guo, Studies of slurry jet flue gas desulfurization, Ph. D. Thesis, Tsinghua Univ., Beijing, (2002).
[10] L. Wurth, R. Hannemann, W. Marquardt, Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization, J. Process Control 19(8) (2009) 1277-1288.
[11] H.G. Bock, M.M. Diehl, D.B. Leineweber, J.P. Schloder, A direct multiple shooting method for real-time optimization of nonlinear DAE processes, J. Process Control 26(2009) 245-267.
[12] F. Allgöwer, A. Zheng, Nonlinear model predictive control, progress in systems and control theory, Birkhäuser Verlag 26(2000) 245-267.
[13] M. Diehl, H. Bock, J. Schloder, R. Findeisen, Z. Nagy, F. Allgower, Real-time optimization and nonlinear model predictive control of processes governed by differentialalgebraic equations, J. Process Control 12(4) (2002) 577-585.
[14] L. Grüne, J. Pannek, Analysis of unconstrained NMPC schemes with incomplete optimization, IFAC Proceedings 43(14) (2010) 238-243.
[15] V.M. Zavala, M. Anitescu, Real-time nonlinear optimization as a generalized equation, SIAM J. Control. Optim. 48(2010) 5444-5467.
[16] I.J. Wolf, L. Würth, W. Marquardt, Rigorous solution vs. fast update:acceptable computational delay in NMPC, 50th IEEE CDC-ECC 2011, pp. 5230-5235.
[17] V.M. Zavala, L.T. Biegler, The advanced-step NMPC controller:optimality, stability and robustness, Automatica 45(1) (2009) 86-93.
[18] J. Jäschke, X. Yang, L.T. Biegler, Fast economic model predictive control based on NLP-sensitivities, J. Process Control 24(8) (2014) 1260-1272.
[19] M.V. Sotnikova, Control system design for visual positioning of a ship based on NMPC and multi-objective structure, IFAC-PapersOnLine 51(32) (2018) 445-450.
[20] O. Makoto, T. Gaku, Real-time autonomous car motion planning using NMPC with approximated problem considering traffic environment, IFAC-PapersOnLine 51(20) (2018) 279-286.
[21] W.Y. Zhang, D.X. Huang, Y.D. Wang, J.C. Wang, Adaptive state feedback predictive control and expert control for a delayed coking furnace, Chin. J. Chem. Eng. 16(4) (2008) 590-598.
[22] H. Flemming, M. Alexander, T.B. Lorenz, Multistage NMPC with on-line generated scenario trees:Application to a semi-batch polymerization process, J. Process Control 80(2019) 167-179.
[23] M.Z. Yu, T.B. Lorenz, Economic NMPC strategies for solid sorbent-based CO₂ capture, IFAC-PapersOnLine 51(18) (2018) 103-108.
[24] D. Telen, B. Houska, M. Vallerio, F. Logist, J. Vanimpe, A study of integrated experiment design for NMPC applied to the Droop model, Chem. Eng. Sci. 160(2017) 370-383.
[25] A.V. Fiacco, G.P. McCormick, Nonlinear Programming:Sequential Unconstrained Minimization Techniques, John Wiley & Sons Inc., 1968
[26] S.H. Chen, H.M. He, X.F. Li, G. Chen, Application of fuzzy PID controller with selfadaptive algorithm and non-uniform grid scheduling to WFGD, IFAC Proceedings Volumes 42(9) (2009) 20-25.
[27] A. Gautam, Y.C. Chu, Y.C. Soh, Optimized dynamic policy for receding horizon control of linear time-varying systems with bounded disturbances, IEEE Trans Automat Control 57(4) (2012) 973-988.
[28] Alexandra Grancharova, Tor A. Johansen, Computation, approximation and stability of explicit feedback min-max nonlinear model predictive control, Automatica 45(5) (2009) 1134-1143.
[29] D.F. Hen, H. Huang, Q.X. Chen, Quasi-min-max MPC for constrained nonlinear systems with guaranteed input-to-state stability, Journal of the Franklin Institute 351(6) (2014) 3405-3423.
[30] Y. Zhong, X. Gao, W. Huo, Z.Y. Luo, M.J. Ni, K.F. Cen, A model for performance optimization of wet flue gas desulfurization systems of power plants, Fuel Process. Technol. 89(11) (2008) 1025-1032.
[31] G. Helle, K. Søren, E.J. Jan, N.J. Jørgen, H. Jørn, F. Folmer, D.J. Kim, Full-scale measurements of SO₂ gas phase concentrations and slurry compositions in a wet flue gas desulphurisation spray absorber, Fuel 83(9) (2004) 1151-1164.
[32] Q. Zhang, S.J. Wang, P. Zhu, Z.Y. Wang, G. Zhang, Full-scale simulation of flow field in ammonia-based wet flue gas desulfurizationdouble tower, J. Energy Inst. 91(4) (2018) 619-629.
[33] C. Patricia, Status of flue gas desulphurisation (FGD) systems from coal-fired power plants:Overview of the physic-chemical control processes of wet limestone FGDs, Fuel 144(2015) 274-286.
[34] X.H. Li, Y.D. He, Simulation of flue gas flow field in the spraying tower of wet flue gas desulfurization system Therm, Power Generation Technologies 39(2010) 41-45.
[35] L.Q. Li, C. Zhang, G.J. Huang, Z. Liu, W.W. Ma, A multicomponent droplet model in simulating mass transfer of the ammonia-based spray process, Proc. CSEE. 34(2014) 5741-5749.
[36] S.L. Zhao, Y.F. Duan, J.C. Lu, R. Gupt, D. Pudasainee, S. Liu, M. Liu, J.H. Lua, Thermal stability, chemical speciation and leaching characteristics of hazardous trace elements in FGD gypsum from coal-fired power plants, Fuel 231(2018) 94-100.
[37] P. Córdoba, R. Ochoa-Gonzalez, O. Font, M. Izquierdo, X. Querol, C. Leiva, Partitioning of trace inorganic elements in a coal-fired power plant equipped with a wet flue gas desulphurisation system, Fuel 92(2012) 145-157.
[38] R. Meij, T.H. Winkel, The emissions and environmental impact of PM10 and trace elements from a modern coal-fired power plant equipped with ESP and wet FGD, Fuel Process. Technol. 85(2004) 641-656.
[39] C. Claudio, D.B. Cataldo, M. Marianna, P. Raffaele, W. Tapio, Ultrasonic enhanced limestone dissolution:Experimental and mathematical modeling, Chemical Engineering and Processing:Process Intensification 118(2017) 26-36.
[40] J. Rawlings, D. Mayne, Model Predictive Control:Theory and Design, Nob Hill Publishing, San Francisco, 2009.
[41] G. Lars, NMPC without terminal constraints, IFAC Proceedings 45(17) (2012) 1-13.
[42] A.W. Hermansson, S. Syafiie, An offset-free MPC formulation for nonlinear systems using adaptive integral controller, ISA Trans. 91(2019) 66-77.
[43] B.W. Frandsen, K. Søren, E.J. Jan, Optimisation of a wet FGD pilot plant using fine limestone and organic acids, Chem. Eng. Sci. 56(10) (2001) 3275-3287.
[44] Z. Mario, G. Sébastien, D. Moritz, A tracking MPC formulation that is locally equivalent to economic MPC, J. Process Control 45(2016) 30-42.
[45] M. Ellis, P.D. Christofides, Economic model predictive control with timevarying objective function for nonlinear process systems, AIChE J. 60(2014) 507-519.
[46] M. Zanon, S. Gros, M. Diehl, Indefinite linear MPC and approximated economic MPC for nonlinear systems, J. Process Control 24(2014) 1273-1281.