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[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]
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Comparative study of low NOx combustion optimizationof a coal-fired utility boiler based on OBLPSO and GOBLPSO()
面向燃煤锅炉低氮燃烧优化的OBLPSO算法 和GOBLPSO算法比较
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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
面向燃煤锅炉低氮燃烧优化的OBLPSO算法 和GOBLPSO算法比较
Author(s):
Li Qingwei;  Liu Zhi;  He Qifeng
College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
李庆伟;  刘智;  贺奇峰
上海电力大学能源与机械工程学院, 上海 200090
Keywords:
NOx emissions;  combustion optimization;  particle swarm optimization;  opposition-based learning;  generalized opposition-based learning
NOx排放;  燃烧优化;  粒子群优化;  相反学习;  广义相反学习
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.
为进一步降低燃煤电站锅炉的NOx排放量, 在低氮燃烧优化中引入相反学习粒子群算法(OBLPSO)和广义相反学习粒子群算法(GOBLPSO).在某660 MW燃煤机组锅炉中进行了摸底工况、变氧量工况、变磨煤机投运方式工况、变风压工况等燃烧调整试验, 得到燃烧优化的样本, 进而比较分析了粒子群算法(PSO)、标准差分进化算法(DE)、OBLPSO算法和GOBLPSO算法的适应性.51次独立重复优化试验结果表明:PSO算法性能优于DE算法, 而GOBLPSO算法性能总体优于PSO算法和OBLPSO算法.GOBLPSO算法优化的NOx排放量中位数较PSO算法最多可低15.8 mg/m3.广义相反学习能有效利用当前搜索空间的信息, 提升粒子群在低氮燃烧优化中的适应性.

References:

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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