A Composite Model Predictive Control Strategy for Furnaces

doi:10.1016/j.cjche.2014.05.014

Abstract

Abstract: Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

Key words: Furnace, Tracking nonlinear model predictive control, Economic nonlinear model predictive control, Distributed model predictive control

摘要： Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

关键词: Furnace, Tracking nonlinear model predictive control, Economic nonlinear model predictive control, Distributed model predictive control

Hao Zang, Hongguang Li, Jingwen Huang, Jia Wang. A Composite Model Predictive Control Strategy for Furnaces[J]. , 2014, 22(7): 788-794.

References

[1] Z. Jegla, P. Stehlík, J. Kohoutek, Plant energy saving through efficient retrofit of furnaces, Appl. Therm. Eng. 20 (15-16) (2000) 1545-1560.
[2] S.A. Kalogirou, Artificial intelligence for the modeling and control of combustion processes: a review, Prog. Energy Combust. Sci. 29 (6) (2003) 515-566.
[3] M. Ramírez, Fuzzy control of a multiple hearth furnace, Comput. Ind. 54 (1) (2004) 105-113.
[4] C. Lee, C.G. Jou, Saving fuel consumption and reducing pollution emissions for industrial furnace, Fuel Process. Technol. 92 (12) (2011) 2335-2340.
[5] C. Lee, C.G. Jou, Improving furnace and boiler cost-effectiveness and CO2 emission by adjusting excess air, Environ. Prog. Sustain. Energy 31 (1) (2012) 157-162.
[6] Wenxiang Lu, Xiaoyong Gao, Dexian Huang, Research on intelligent optimal control of thermal efficiency of furnace, Chin. J. Sci. Instrum. 35 (8) (2009) 2335-2340.
[7] A.Willersrud, Short-termproduction optimization of offshore oil and gas production using nonlinear model predictive control, J. Process Control 23 (2) (2013) 215-223.
[8] D.Q. Mayne, Constrained model predictive control: stability and optimality, Automatica 36 (6) (2000) 789-814.
[9] S.J. Qin, T. Badgwell, in: F. Allgöwer, A. Zheng, F. Allgöwer, A. Zheng (Eds.), An Overview of Nonlinear Model Predictive Control Applications, Birkhäuser, Basel, 2000, pp. 369-392.
[10] Y.Wang, D. Huang, Y. Jin, A hybrid model predictive control for handling infeasibility and constraint prioritization, Chin. J. Chem. Eng. (02) (2005) 65-71.
[11] R. Scattolini, Architectures for distributed and hierarchicalmodel predictive control — a review, J. Process Control 19 (5) (2009) 723-731.
[12] P.D. Christofides, Distributed model predictive control: a tutorial review and future research directions, Comput. Chem. Eng. 51 (2013) 21-41.
[13] X. Chen, Distributed economic MPC: application to a nonlinear chemical process network, J. Process Control 22 (4) (2012) 689-699.
[14] L. Würth, R. Hannemann, W. Marquardt, A two-layer architecture for economically optimal process control and operation, J. Process Control 21 (3) (2011) 311-321.
[15] V. Adetola,M. Guay, Integration of real-time optimization and model predictive control, J. Process Control 20 (2) (2010) 125-133.
[16] M. Heidarinejad, J. Liu, P.D. Christofides, Economic model predictive control of nonlinear process systems using Lyapunov techniques, AIChE J. 58 (3) (2012) 855-870.
[17] M. Ellisa, P.D. Christofides, Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems, Control. Eng. Pract. 22 (10) (2013).
[18] Wenxiang Lu, Ying Zhu, Dexian Huang, Yihui Jin, A new strategy of integrated control and on-line optimization on high-purity distillation process, Chin. J. Chem. Eng. 18 (1) (2010) 66-79.
[19] A. Gopalakrishnan, L.T. Biegler, Economic nonlinear model predictive control for periodic optimal operation of gas pipeline networks, Comput. Chem. Eng. 52 (2013) 90-99.
[20] M.A. Müller, D. Angeli, F. Allgöwer, Economic model predictive control with selftuning terminal cost, Eur. J. Control. 19 (5) (2013).
[21] R. Huang, E. Harinath, L.T. Biegler, Lyapunov stability of economically oriented NMPC for cyclic processes, J. Process Control 21 (4) (2011) 501-509.
[22] Bo Li, Xuefang Lin-Shi, B. Allard, J.-M. Retif, A digital dual-state-variable predictive controller for high switching frequency buck converter with improved Σ-Δ DPWM, IEEE Trans. Ind. Inform. 8 (3) (Aug. 2012) 472-481.
[23] R. Huang, V.M. Zavala, L.T. Biegler, Advanced step nonlinear model predictive control for air separation units, J. Process Control 19 (4) (2009) 678-685