Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

doi:10.1016/j.cjche.2024.09.011

Chinese Journal of Chemical Engineering ›› 2024, Vol. 76 ›› Issue (12): 318-326.DOI: 10.1016/j.cjche.2024.09.011

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

Jiahui Liu^1,2,3, Nan Guo^1,2,3, Yixin Peng^1,2,3, Wenlu Li^1,2,3, Junfei Qiao^1,2,3, Xiaolong Gao⁴, Wei Xiong⁵

1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China;
3. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing 100124, China;
4. Baotou Reclaimed Water Resources and Sewage Treatment Co., Ltd., Inner Mongolia 014000, China;
5. Baotou Water Group, Inner Mongolia 014000, China

Received:2023-11-01 Revised:2024-09-04 Accepted:2024-09-05 Online:2024-10-17 Published:2024-12-28
Contact: Junfei Qiao,E-mail:adqiao@bjut.edu.cn
Supported by:
The authors gratefully acknowledge the financial support from the Scientific and Technological Innovation 2030-“New Generation Artificial Intelligence” Major Project (2021ZD0112301), National Natural Science Foundation of China (62273011, 62076013, 62303027).

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

Jiahui Liu^1,2,3, Nan Guo^1,2,3, Yixin Peng^1,2,3, Wenlu Li^1,2,3, Junfei Qiao^1,2,3, Xiaolong Gao⁴, Wei Xiong⁵

1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China;
3. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing 100124, China;
4. Baotou Reclaimed Water Resources and Sewage Treatment Co., Ltd., Inner Mongolia 014000, China;
5. Baotou Water Group, Inner Mongolia 014000, China

通讯作者: Junfei Qiao,E-mail:adqiao@bjut.edu.cn
基金资助:
The authors gratefully acknowledge the financial support from the Scientific and Technological Innovation 2030-“New Generation Artificial Intelligence” Major Project (2021ZD0112301), National Natural Science Foundation of China (62273011, 62076013, 62303027).

Abstract

Abstract: Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares. Due to the complex and variable composition of the waste gas they handle, the combustion environment is severely affected by meteorological conditions. Key process parameters such as intake composition, flow rate, and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability. As a result, the control methods for these flares are limited, leading to poor control effectiveness. To address this issue, this paper proposes an adaptive sliding mode control method based on the radial basis function (RBF) network. Firstly, the operational characteristics of the petrochemical flare combustion process are analyzed, and a control model for the combustion process is established based on carbon dioxide detection. Secondly, an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system. Finally, by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law, fast and stable control of the flare combustion state is achieved. Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state, and the adaptive law also accomplishes system identification.

Key words: Petrochemical torch combustion process, Sliding mode control, RBF neural network, System identification, Fast response

摘要： Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares. Due to the complex and variable composition of the waste gas they handle, the combustion environment is severely affected by meteorological conditions. Key process parameters such as intake composition, flow rate, and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability. As a result, the control methods for these flares are limited, leading to poor control effectiveness. To address this issue, this paper proposes an adaptive sliding mode control method based on the radial basis function (RBF) network. Firstly, the operational characteristics of the petrochemical flare combustion process are analyzed, and a control model for the combustion process is established based on carbon dioxide detection. Secondly, an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system. Finally, by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law, fast and stable control of the flare combustion state is achieved. Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state, and the adaptive law also accomplishes system identification.

关键词: Petrochemical torch combustion process, Sliding mode control, RBF neural network, System identification, Fast response

Jiahui Liu, Nan Guo, Yixin Peng, Wenlu Li, Junfei Qiao, Xiaolong Gao, Wei Xiong. Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network[J]. Chinese Journal of Chemical Engineering, 2024, 76(12): 318-326.

References

[1] K. Gu, H.Y. Liu, J. Liu, X.F. Yu, T. Shi, J.F. Qiao, Air pollution prediction in mass rallies with a new temporally-weighted sample-based multitask learner, IEEE Trans. Instrum. Meas. 71 (2022) 2505915.
[2] H.L. Gai, A.N. Wang, J. Fang, H.H. Lou, D. Chen, X.C. Li, C. Martin, Clean combustion and flare minimization to reduce emissions from process industry, Curr. Opin. Green Sustain. Chem. 23 (2020) 38-45.
[3] Y.J. Wang, H. Zhang, H.H. Zhang, X.Y. Kang, X.Y. Xu, R.H. Wang, H.H. Zou, W.W. Chen, D. Pan, F. Lu, P.J. He, Flare exhaust: An underestimated pollution source in municipal solid waste landfills, Chemosphere 325 (2023) 138327.
[4] K. Gu, Y.H. Zhang, J.F. Qiao, Ensemble meta-learning for few-shot soot density recognition, IEEE Trans. Ind. Inform. 17 (3) (2021) 2261-2270.
[5] N. Guo, K. Gu, J.F. Qiao, Flare soot monitoring based on thermal infrared image processing and attention-based meta-learning, 2021 China Automation Congress (CAC). October 22-24, 2021, Beijing, China. IEEE, (2021) 7098-7103.
[6] R. Srinivasarao, K.V.S.G.M. Krishna, Automatic control of soot and unburnt hydro carbons from flares in oil and gas industry, 2014 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE). March 19-21, 2014, Pattaya, Thailand. IEEE, (2014) 1-5.
[7] D.H. Chen, A. Alphones, Characterization of the incipient smoke point for steam-/ air-assisted and nonassisted flares, J. Air Waste Manag. Assoc. 69 (1) (2019) 119-130.
[8] S.C. Zhang, Q. Guan, Development of an elevated flare monitor using video image processing technique, IOP Conf. Ser.: Earth Environ. Sci. 199 (2018) 032058.
[9] M.A. Gagnon, P. Tremblay, S. Savary, P. Lagueux, M. Chamberland, Standoff thermal hyperspectral imaging for flare and smokestack characterization in industrial environments, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). June 26-28, 2013, Gainesville, FL, USA. IEEE, (2013) 1-4.
[10] J.H. Pohl, B.A. Tichenor, J. Lee, R. Payne, Combustion efficiency of flares, Combust. Sci. Technol. 50 (4-6) (1986) 217-231.
[11] K. Devesh Singh, P. Gangadharan, T. Dabade, V. Shinde, D. Chen, H.H. Lou, P.C. Richmond, X.C. Li, Parametric study of ethylene flare operations using numerical simulation, Eng. Appl. Comput. Fluid Mech. 8 (2) (2014) 211-228.
[12] H.H. Lou, J. Fang, H.L. Gai, R. Xu, S. Lin, A novel zone-based machine learning approach for the prediction of the performance of industrial flares, Comput. Chem. Eng. 162 (2022) 107795.
[13] K.D. Singh, P. Gangadharan, D.H. Chen, H.H. Lou, X.C. Li, P. Richmond, Computational fluid dynamics modeling of laboratory flames and an industrial flare, J. Air Waste Manag. Assoc. 1995 64 (11) (2014) 1328-1340.
[14] B.P. Maldonado, K. Zaseck, E. Kitagawa, A.G. Stefanopoulou, Closed-loop control of combustion initiation and combustion duration, IEEE Trans. Contr. Syst. Technol. 28 (3) (2020) 936-950.
[15] Y. Feng, M. Wu, L.F. Chen, X. Chen, W.H. Cao, S. Du, W. Pedrycz, Hybrid intelligent control based on condition identification for combustion process in heating furnace of compact strip production, IEEE Trans. Ind. Electron. 69 (3) (2022) 2790-2800.
[16] Q. Lei, M. Wu, J.H. She, Online optimization of fuzzy controller for coke-oven combustion process based on dynamic just-in-time learning, IEEE Trans. Autom. Sci. Eng. 12 (4) (2015) 1535-1540.
[17] Y.Y. Zhou, Q.C. Zhang, H. Wang, P. Zhou, T.Y. Chai, EKF-based enhanced performance controller design for nonlinear stochastic systems, IEEE Trans. Autom. Contr. 63 (4) (2018) 1155-1162.
[18] P. Zhou, D.W. Guo, T.Y. Chai, Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification, Neurocomputing 308 (2018) 101-110.
[19] Q. Xu, X.T. Yang, C.W. Liu, K. Li, H.H. Lou, J.L. Gossage, Chemical plant flare minimization via plantwide dynamic simulation, Ind. Eng. Chem. Res. 48 (7) (2009) 3505-3512.
[20] R.K. Sharma, Y.B. Prasad, V. Harsshbabu, Minimize your refinery flaring, Hydrocarbon Process. 86 (2) (2007) 105-106.
[21] K. Gu, Y.H. Zhang, J.F. Qiao, Vision-based monitoring of flare soot, IEEE Trans. Instrum. Meas. 69 (9) (2020) 7136-7145.
[22] D.T. Allen, D. Smith, V.M. Torres, F.C. Saldana, Carbon dioxide, methane and black carbon emissions from upstream oil and gas flaring in the United States, Curr. Opin. Chem. Eng. 13 (2016) 119-123.
[23] P.J. Prieto, N.R. Cazarez-Castro, L.T. Aguilar, S.L. Cardenas-Maciel, Chattering existence and attenuation in fuzzy-based sliding mode control, Eng. Appl. Artif. Intell. 61 (2017) 152-160.
[24] C. Munoz, H. Young, C. Antileo, C. Bornhardt, Sliding mode control of dissolved oxygen in an integrated nitrogen removal process in a sequencing batch reactor (SBR), Water Sci. Technol. 60 (10) (2009) 2545-2553.
[25] H.G. Han, X.L. Wu, J.F. Qiao, A self-organizing sliding-mode controller for wastewater treatment processes, IEEE Trans. Contr. Syst. Technol. 27 (4) (2019) 1480-1491.
[26] A. Mohammadzadeh, S. Ghaemi, A modified sliding mode approach for synchronization of fractional-order chaotic/hyperchaotic systems by using new self-structuring hierarchical type-2 fuzzy neural network, Neurocomputing 191 (2016) 200-213.
[27] Y.Q. Han, Y.G. Kao, C.C. Gao, Robust sliding mode control for uncertain discrete singular systems with time-varying delays and external disturbances, Automatica 75 (2017) 210-216.
[28] M.X. Yang, X. Zhang, Y.L. Xia, Q.Y. Liu, Q. Zhu, Adaptive neural network-based sliding mode control for a hydraulic rotary drive joint, Comput. Electr. Eng. 102 (2022) 108189.
[29] D.P. Li, D.J. Li, Y.J. Liu, S.C. Tong, C.L.P. Philip Chen, Approximation-based adaptive neural tracking control of nonlinear MIMO unknown time-varying delay systems with full state constraints, IEEE Trans. Cybern. 47 (10) (2017) 3100-3109.
[30] D.P. Li, Y.J. Liu, S.C. Tong, C.L.P. Philip Chen, D.J. Li, Neural networks-based adaptive control for nonlinear state constrained systems with input delay, IEEE Trans. Cybern. 49 (4) (2019) 1249-1258.
[31] D.P. Li, C.L. Philip Chen, Y.J. Liu, S.C. Tong, Neural network controller design for a class of nonlinear delayed systems with time-varying full-state constraints, IEEE Trans. Neural Netw. Learn. Syst. 30 (9) (2019) 2625-2636.
[32] J.L. Kong, W.Q. Liu, J.H. Liu, Z.B. Sun. Design and finite element analysis of PZT for three-dimensional Laser scanner in forest area[J].Mechanical Engineering & Automation, (2)(2016)55-57. (in Chinese).
[33] M. Gilardi, F. Bisotti, A. Tobiesen, H.K. Knuutila, D. Bonalumi, An approach for VLE model development, validation, and implementation in aspen plus for amine blends in CO₂ capture: The HS3 solvent case study, Int. J. Greenh. Gas Contr. 126 (2023) 103911.
[34] B.X. Zhou, J.C. Chang, J. Li, J.L. Hong, T. Wang, Z.L. Zhu, L.Q. Zhang, C.Y. Ma, Two-stage gasification process simulation and optimization of pulverized coal for hydrogen-rich production using Aspen plus, Int. J. Hydrog. Energy 49 (2024) 849-860.
[35] F. Njuguna, H. Ndiritu, B. Gathitu, M. Hawi, J. Munyalo, Kinetic modeling and optimization of process parameters for gasification of macadamia nutshells with air preheating: A combined use of aspen plus and response surface methodology (RSM), Bioresour. Technol. Rep. 22 (2023) 101477.
[36] A.M. Ali, M. Shahbaz, M. Inayat, K. Shahzad, A. Ahmad Al-Zahrani, A.B. Mahpudz, Conversion of municipals waste into syngas and methanol via steam gasification using CaO as sorbent: An aspen plus modelling, Fuel 349 (2023) 128640.
[37] Z.J. Wang, Z.W. Mao, J.J. Zhang, Z.N. Jiang, G.Q. Xiong, A temporal correlation micro-visco-elastohydrodynamic lubrication model and its applications on internal combustion engine, Tribol. Int. 178 (2023) 108101.
[38] W.J. Li, X. Zhang, R.Q. Liu, S.Y. Xu, S. Xu, Y.H. Lan, Y.Z. Fu, Y. Zhang, Y.A. Feng, W.G. Cao, Thermal decomposition, flame propagation, and combustion reactions behaviours of stearic acid by experiments and molecular dynamic simulation, Chem. Eng. J. 461 (2023) 141906.
[39] E. Siswanto, D.B. Darmadi, A.S. Widodo, M. Talice, Enhancement of combustion performances and reduction of combustible species emission using an additional of combustion-reaction of engine, Case Stud. Therm. Eng. 49 (2023) 103328.
[40] Y.M. Liu, H. Babaee, P. Givi, H.K. Chelliah, D. Livescu, A.G. Nouri, Skeletal reaction models for methane combustion, Fuel 357 (2024) 129581.
[41] L. Wang, Y.F. Liu, J.B. Wang, X.Y. Li, J.Y. Ma, Effect of CO₂ and methyl groups reaction kinetics on the ignition and combustion of diesel surrogate fuel: Part Ⅰ. reaction mechanisms, Fuel 351 (2023) 128984.

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 6

Recommended Articles

Metrics

Comments

[1]	Yaoyao Bao, Yuanming Zhu, Weimin Zhong, Feng Qian. A novel chemical composition estimation model for cement raw material blending process [J]. Chinese Journal of Chemical Engineering, 2019, 27(11): 2734-2741.
[2]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics [J]. Chin.J.Chem.Eng., 2018, 26(1): 137-143.
[3]	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.
[4]	SHI Yuanhao, WANG Jingcheng, ZHANG Yunfeng. Sliding Mode Predictive Control of Main Steam Pressure in Coal-fired Power Plant Boiler^* [J]. Chin.J.Chem.Eng., 2012, 20(6): 1107-1112.
[5]	BAO Zhejing, PI Daoying, SUN Youxian. Nonlinear model predictive control based on support vector machine with multi-kernel [J]. , 2007, 15(5): 691-697.
[6]	HUANG Dexian, JN Yihui, ZHANG Jie, A. J. Morris. Non-linear Chemical Process Modelling and Application in Epichlorhydrine Production Plant Using Wavelet Networks [J]. , 2002, 10(4): 435-443.