|Table of Contents|

[1] Li Qingwei, Liu Zhi, He Qifeng,. Comparative study of low NOx combustion optimizationof a coal-fired utility boiler based on OBLPSO and GOBLPSO [J]. Journal of Southeast University (English Edition), 2021, 37 (3): 285-289. [doi:10.3969/j.issn.1003-7985.2021.03.008]
Copy

Comparative study of low NOx combustion optimizationof a coal-fired utility boiler based on OBLPSO and GOBLPSO()
Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
37
Issue:
2021 3
Page:
285-289
Research Field:
Energy and Power Engineering
Publishing date:
2021-09-20

Info

Title:
Comparative study of low NOx combustion optimizationof a coal-fired utility boiler based on OBLPSO and GOBLPSO
Author(s):
Li Qingwei;  Liu Zhi;  He Qifeng
College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Keywords:
NOx emissions;  combustion optimization;  particle swarm optimization;  opposition-based learning;  generalized opposition-based learning
PACS:
TK323
DOI:
10.3969/j.issn.1003-7985.2021.03.008
Abstract:
To reduce NOx emissions of coal-fired power plant boilers, this study introduced particle swarm optimization employing opposition-based learning(OBLPSO)and particle swarm optimization employing generalized opposition-based learning(GOBLPSO)to a low NOx combustion optimization area. Thermal adjustment tests under different ground conditions, variable oxygen conditions, variable operation modes of coal pulverizer conditions, and variable first air pressure conditions were carried out on a 660 MW boiler to obtain samples of combustion optimization. The adaptability of PSO, differential evolution algorithm(DE), OBLPSO, and GOBLPSO was compared and analyzed. Results of 51 times independently optimized experiments show that PSO is better than DE, while the performance of the GOBLPSO algorithm is generally better than that of the PSO and OBLPSO. The median-optimized NOx emission by GOBLPSO is up to 15.8 mg/m3 lower than that obtained by PSO. The generalized opposition-based learning can effectively utilize the information of the current search space and enhance the adaptability of PSO to the low NOx combustion optimization of the studied boiler.

References:

[1] Rahat A A M, Wang C L, Everson R M, et al. Data-driven multi-objective optimisation of coal-fired boiler combustion systems [J].Applied Energy, 2018, 229: 446-458. DOI: 10.1016/j.apenergy.2018.07.101.
[2] Li Q W, Yao G H. Improved coal combustion optimization model based on load balance and coal qualities [J]. Energy, 2017, 132: 204-212. DOI: 10.1016/j.energy.2017.05.068.
[3] Safdarnejad S M, Tuttle J F, Powell K M. Dynamic modeling and optimization of a coal-fired utility boiler to forecast and minimize NOx and CO emissions simultaneously [J]. Computers and Chemical Engineering, 2019, 124: 62-79. DOI: 10.1016/j.compchemeng.2019.02.001.
[4] Tang Z H, Wu X Y, Cao S X. Modeling of the boiler NOx emission with a data driven algorithm [J]. Journal of Chemical Engineering of Japan, 2018, 51(8): 695-703. DOI: 10.1252/jcej.17we335.
[5] Tang Z H, Wu X Y, Cao S X, et al. Modeling of the boiler NOx emission with a data driven algorithm [J]. Journal of Chemical Engineering of Japan, 2018, 51(8): 695-703. DOI: 10.1252/jcej.17we335.
[6] Zheng L G, Zhang Y G, Yu S J, et al. Use of differential evolution in low NOx combustion optimization of a coal-fired boiler [C]// 2010 Sixth International Conference on Natural Computation. Yantai, China, 2010: 4395-4399. DOI: 10.1109/ICNC.2010.5583524.
[7] Li X, Niu P F, Liu J P. Combustion optimization of a boiler based on the chaos and Lévy flight vortex search algorithm [J]. Applied Mathematical Modelling, 2018, 58: 3-18. DOI: 10.1016/j.apm.2018.01.043.
[8] Ilamathi P, Selladurai V, Balamurugan K, et al. ANN-GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler [J]. Clean Technologies and Environmental Policy, 2013, 15: 125-131. DOI: 10.1007/s10098-012-0490-5.
[9] Zhou H, Zheng L G, Cen K F. Computational intelligence approach for NOx emissions minimization in a coal-fired utility boiler [J]. Energy Conversion and Management, 2010, 51: 580-586. DOI: 10.1016/j.enconman.2009.11.002.
[10] Kennedy J, Eberhart R. Particle swarm optimization [C]// International Conference on Neural Networks. Perth, WA, Australia, 1995: 1942-1948. DOI: 10.1109/ICNN.1995.488968.
[11] Han H G, Lu W, Hou Y, et al. An adaptive-PSO-based self-organizing RBF neural network [J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 104-117. DOI: 10.1109/TNNLS.2016.2616413.
[12] Jiang F, Xia H Y, Tran Q A, et al. A new binary hybrid particle swarm optimization with wavelet mutation [J]. Knowledge-Based Systems, 2017, 130: 90-101. DOI: 10.1016/j.knosys.2017.03.032.
[13] Wang F, Zhang H, Li K S, et al. A hybrid particle swarm optimization algorithm using adaptive learning strategy [J]. Information Sciences, 2018, 436-437: 162-177. DOI: 10.1016/j.ins.2018.01.027.
[14] Xia X W, Gui L, Zhan Z H. A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting [J]. Applied Soft Computing, 2018, 67: 126-140. DOI:10.1016/j.asoc.2018.02.042.
[15] Liu Z, Qin Z W, Zhu P, et al. An adaptive switchover hybrid particle swarm optimization algorithm with local search strategy for constrained optimization problems [J]. Engineering Applications of Artificial Intelligence, 2020, 95: 103771. DOI: 10.1016/j.engappai.2020.103771.
[16] Rahnamayan S, Tizhoosh H R, Salama M M A. Opposition-based differential evolution [J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 64-79. DOI: 10.1109/TEVC.2007.894200.
[17] Lin H, He X. A novel opposition-based particle swarm optimization for noisy problems [C]// Proceedings of International Conference on Natural Computation. Haikou, China, 2007: 624-629. DOI: 10.1109/ICNC.2007.119.
[18] Wang H, Li H, Liu Y, et al. Opposition-based particle swarm algorithm with cauchy mutation [C]// IEEE Congress on Evolutionary Computation. Singapore, 2007: 4750-4756. DOI: 10.1109/CEC.2007.4425095.
[19] Wang H, Wu Z J, Rahnamayan S, et al. Enhancing particle swarm optimization using generalized opposition-based learning [J]. Information Sciences, 2011, 181: 4699-4714. DOI: 10.1016/j.ins.2011.03.016.
[20] Tizhoosh H R. Opposition-based learning: A new scheme for machine intelligence [C]// Proceedings of International Conference on Computational Intelligence for Modeling Control and Automation. Vienna, Austria, 2005: 695-701. DOI: 10.1109/CIMCA.2005.1631345.
[21] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications [J]. Neurocomputing, 2006, 70: 489-501. DOI: 10.1016/j.neucom.2005.12.126.
[22] Storn R, Price K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces [J]. Journal of Global Optimization, 1997, 11: 341-359. DOI: 10.1023/A:1008202821328.
[23] Bao Z Y, Yu J Z.Intelligent optimization algorithm and its MATLAB example [M]. Beijing: Publishing House of Electronics Industry, 2016:39-42.(in Chinese)

Memo

Memo:
Biography: Li Qingwei(1987—), male, doctor, lecturer, liqingweish@163.com.
Foundation item: The Shanghai Sailing Program(No. 18YF1409000).
Citation: Li Qingwei, Liu Zhi, He Qifeng.Comparative study of low NOx combustion optimization of a coal-fired utility boiler based on OBLPSO and GOBLPSO[J].Journal of Southeast University(English Edition), 2021, 37(3):285-289.DOI:10.3969/j.issn.1003-7985.2021.03.008.
Last Update: 2021-09-20