Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

›› 2008, Vol. 16 ›› Issue (4): 590-598.

• SYSTEM ENGINEERING • Previous Articles Next Articles

Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

ZHANG Weiyong^1,2, HUANG Dexian^2,3, WANG Yudong^2,3, WANG Jingchun^2,3

1. School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2. Department of Automation, Tsinghua University, Beijing 100084, China;
3. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China

Received:2007-03-30 Revised:2008-03-05 Online:2008-08-28 Published:2008-08-28
Supported by:
the State Key Development Program for Basic Research of China(2002CB312200);the National High Technology Research and Development Program of China(2007AA04Z193)

Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

张伟勇^1,2, 黄德先^2,3, 王毓栋^2,3, 王京春^2,3

1. School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2. Department of Automation, Tsinghua University, Beijing 100084, China;
3. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China

通讯作者: HUANG Dexian, E-mail: huangdx@mail.tsinghua.edu.cn
基金资助:
the State Key Development Program for Basic Research of China(2002CB312200);the National High Technology Research and Development Program of China(2007AA04Z193)

Abstract

Abstract: An adaptive state feedback predictive control (SFPC) scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a^-1 delayed coking furnace, which is the key equipment for the delayed coking process. Adaptive SFPC is used to improve the performance of temperature control in normal operation. A simplified nonlinear model on the basis of first principles of the furnace is developed to obtain a state space model by linearization. Taking advantage of the nonlinear model, an online model adapting method is presented to accommodate the dynamic change of process characteristics because of tube coking and load changes. To compensate the large inverse response of outlet temperature resulting from the sudden increase of injected steam of a particular velocity to tubes, a monitoring method and an expert control scheme based on heat balance calculation are proposed. Industrial implementation shows the effectiveness and feasibility of the proposed control strategy.

Key words: delayed coking furnace, adaptive control, state feedback, predictive control, expert control

摘要： An adaptive state feedback predictive control (SFPC) scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a^-1 delayed coking furnace, which is the key equipment for the delayed coking process. Adaptive SFPC is used to improve the performance of temperature control in normal operation. A simplified nonlinear model on the basis of first principles of the furnace is developed to obtain a state space model by linearization. Taking advantage of the nonlinear model, an online model adapting method is presented to accommodate the dynamic change of process characteristics because of tube coking and load changes. To compensate the large inverse response of outlet temperature resulting from the sudden increase of injected steam of a particular velocity to tubes, a monitoring method and an expert control scheme based on heat balance calculation are proposed. Industrial implementation shows the effectiveness and feasibility of the proposed control strategy.

关键词: delayed coking furnace, adaptive control, state feedback, predictive control, expert control

ZHANG Weiyong, HUANG Dexian, WANG Yudong, WANG Jingchun. Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace[J]. , 2008, 16(4): 590-598.

张伟勇, 黄德先, 王毓栋, 王京春. Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace[J]. , 2008, 16(4): 590-598.

References

1 Diwoky, R.J., “Continuous residuum coking by delayed coking process”, Oil & Gas Journal, 100 (35), 130-132 (2002).
2 Elliott, J.D., Stewart, M.D., “Residue upgrading with delayed coking”, Hydrocarbon Processing, 83 (11), 17-20 (2004).
3 Sawarkar, A.N., Pandit, A.B., Samant, S.D., Joshi, J.B., “Petroleum residue upgrading via delayed coking: A review”, Can. J. Chem. Eng., 85 (2), 1-24 (2007).
4 Qu, G.H., Huang, D.Z., Liang, W.J., “Role and prospects of delayed coking in CHINA’s petroleum processing”, Acta Petrolei Sinica (Petroleum Processing Section), 21 (3), 47-53 (2005). (in Chinese)
5 Chao, K.S., “Design consideration for long period running of delayed coker furnace”, Petroleum Refinery Engineering, 29 (11),29-33 (1999). (in Chinese)
6 Xiao, J.Z., Zhang, Y., Wang, L., Ni, H., Zhang, T., “Study on correlative methods for describing coking rate in furnace tubes”, Petroleum Science and Technology, 18 (3), 305-318 (2000).
7 Cheng, G., He, M., Li, D.L., “Model-free coking furnace adaptive control”, Hydrocarbon Processing, 78 (12), 73-76 (1999).
8 Abilov, A.G., Zeybek, Z., Tuzunalp, O., Telatar, Z., “Fuzzy temperature control of industrial refineries furnaces through combined feedforward/feedback multivariable cascade systems”, Chemical Engineering and Processing, 41 (1), 87-98 (2002).
9 Qiu, X.B., Yuan, J.Q., Wang, Z.F., “Feedforward variable structural proportional-integral-derivative for temperature control of polymerase chain reaction”, Chin. J. Chem. Eng., 14 (2), 200-206 (2006).
10 Qin, S.J., Badgwell, T.A., “A survey of industrial model predictive control technology”, Control Engineering Practice, 11 (7), 733-764 (2003).
11 Liu, Y.F., Wu, G., Wang, Y., Xue, M.S., Sun, D.M., “Application of stair-like modular multivariable DMC in atmospheric pyrochemical furnace”, Information and control, 31 (6), 508-512 (2002). (in Chinese)
12 Zhang, R.D., Wang, S.Q., “Predictive functional controller with a similar proportional integral optimal regulator structure: Comparison with traditional predictive functional controller and application to heavy oil coking equipment”, Chin. J. Chem. Eng., 15 (2), 247-253 (2007).
13 Yuan, P., Dynamic Model for the Process Control and Its Applications, China Petrochemical Press, Beijing (1998). (in Chinese)
14 Yuan, P., Zuo, X., Zheng, H.T., “State feedback predictive control”, Acta Automatica Sinica, 19 (5), 569-577 (1993). (in Chinese)
15 Xi, Y.G., Wang, F., “Multi-model method for predictive control of nonlinear system”, Acta Automatica Sinica, 22 (4), 456-461 (1996). (in Chinese)
16 Dougherty, D., Cooper, D., “A practical multiple model adaptive strategy for single-loop MPC”, Control Engineering Practice, 11 (2),141-159 (2003).
17 Aufderheide, B., Bequette, B.W., “Extension of dynamic matrix control to multiple models”, Comp. Chem. Eng., 27 (6/7), 1079-1096 (2003).
18 Porfírio, C.R., Neto, E.A., Odloak, D., “Multi-model predictive control of an industrial C3/C4 splitter”, Control Engineering Practice,11 (7), 765-779 (2003).
19 Xiao, J., Wang, L., Wei, X., Li, X., Zhang, T., “Process simulation for a tubular coking heater”, Petroleum Science and Technology, 18 (3), 319-333 (2000).
20 Abdelghani-Idrissi, M.A., Arbaoui, M.A., Estel, L., Richalet, J., “Predictive functional control of a counter current heat exchanger using convexity property”, Chemical Engineering and Processing,40 (5), 449-457 (2001).
21 Bouhenchir, H., Cabassud, M., Le Lann, M.V., “Predictive functional control for the temperature control of a chemical batch reactor”, Comp. Chem. Eng., 30 (6/7), 1141-1154 (2006).
22 Lundstrom, P., Lee, J.H., Morari, M.S., Skogestad, S., “Limitations of dynamic matrix control”, Comp. Chem. Eng., 19 (4), 409-421 (1995).

[1]	Shan Liu, Wenqi Zhong, Xi Chen, Li Sun, Lukuan Yang. Multiobjective economic model predictive control using utopia-tracking for the wet flue gas desulphurization system [J]. Chinese Journal of Chemical Engineering, 2023, 54(2): 343-352.
[2]	Jia Ren, Zengqiang Chen, Mingwei Sun, Qinglin Sun, Zenghui Wang. Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning [J]. Chinese Journal of Chemical Engineering, 2022, 49(9): 234-244.
[3]	Zhen Wang, Aimin An, Qianrong Li. Distributed model predictive control based on adaptive sampling mechanism [J]. Chinese Journal of Chemical Engineering, 2021, 39(11): 193-204.
[4]	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.
[5]	Li Jia, Wendan Tan. Just-in-time learning based integrated MPC-ILC control for batch processes [J]. Chin.J.Chem.Eng., 2018, 26(8): 1713-1720.
[6]	Jianxin Wang, Na Yu, Mengqi Chen, Lin Cong, Lanyi Sun. Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies [J]. Chin.J.Chem.Eng., 2018, 26(5): 1087-1101.
[7]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor [J]. , 2017, 25(9): 1230-1237.
[8]	Haokun Wang, Zuhua Xu, Jun Zhao, Aipeng Jiang. An optimal filter based MPC for systems with arbitrary disturbances [J]. , 2017, 25(5): 632-640.
[9]	Mahmoud M. Saafan, Mohamed M. Abdelsalam, Mohamed S. Elksas, Sabry F. Saraya, Fayez F. G. Areed. An adaptive neuro-fuzzy sliding mode controller for MIMO systems with disturbance [J]. , 2017, 25(4): 463-476.
[10]	Ping Yuan, Bi Zhang, Zhizhong Mao. A self-tuning control method for Wiener nonlinear systems and its application to process control problems [J]. , 2017, 25(2): 193-201.
[11]	Cuimei Bo, Jun Li, Lei Yang, Hui Yi, Jihai Tang, Xu Qiao. MPC of distillation column with side reactors for methyl acetate [J]. Chin.J.Chem.Eng., 2017, 25(12): 1798-1804.
[12]	Hongguang Pan, Weimin Zhong, Zaiying Wang. An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC [J]. Chin.J.Chem.Eng., 2017, 25(12): 1805-1811.
[13]	Baocang Ding, Hongguang Pan. Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance [J]. , 2016, 24(10): 1431-1441.
[14]	Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm [J]. Chin.J.Chem.Eng., 2015, 23(12): 2048-2052.
[15]	Zhe Dong. Adaptive output-feedback power-level control for modular high temperature gas-cooled reactors [J]. Chin.J.Chem.Eng., 2015, 23(12): 2092-2097.

Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments