东南大学学报（英文版）2021，Vol.37Issue(3)：285-289,5.DOI:10.3969/j.issn.1003-7985.2021.03.008
面向燃煤锅炉低氮燃烧优化的OBLPSO算法和GOBLPSO算法比较
Comparative study of low NOx combustion optimization of a coal-fired utility boiler based on OBLPSO and GOBLPSO
摘要
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 algorithin 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.关键词
NOx排放/燃烧优化/粒子群优化/相反学习/广义相反学习Key words
NOx emissions/combustion optimization/particle swarm optimization/opposition-based learning/generalized opposition-based learning分类
能源科技引用本文复制引用
李庆伟,刘智,贺奇峰..面向燃煤锅炉低氮燃烧优化的OBLPSO算法和GOBLPSO算法比较[J].东南大学学报（英文版）,2021,37(3):285-289,5.基金项目
The Shanghai Sailing Program (No.18YF1409000). （No.18YF1409000）
