基于入侵杂草的混合粒子群优化燃煤电站锅炉燃烧约束优化

Huan Zhao, Pei-hong Wang, Xianyong Peng, Jin Qian, Quan Wang
{"title":"基于入侵杂草的混合粒子群优化燃煤电站锅炉燃烧约束优化","authors":"Huan Zhao, Pei-hong Wang, Xianyong Peng, Jin Qian, Quan Wang","doi":"10.1109/ICEET.2009.143","DOIUrl":null,"url":null,"abstract":"In order to meet the requirement of high efficiency and low NOx emission combustion of coal-fired boiler, two constrained optimization objectives were designed based on the practical requirement, the one was that maximization of boiler efficiency under NOx emission constraint, and the other one was that minimization of NOx emission under a good boiler performance. Considering the complexity of response characteristics modeling about efficiency and NOx emission, a hybrid particle swarm optimization with invasive weed (IW-PSO) was proposed to optimize the constrained objective functions. In IW-PSO, particle swarm optimization (PSO) and invasive weed optimization (IWO) were integrated in parallel form, and after some iteration, IWO was considered to assist PSO. And in the process of optimizing, variational optimization objective values were tracked by inspection of the results conducted previously. The optimized results indicate that the proposed method can effectively control NOx emission and improve boiler efficiency for different constrained objectives.","PeriodicalId":6348,"journal":{"name":"2009 International Conference on Energy and Environment Technology","volume":"90 1","pages":"564-567"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Constrained Optimization of Combustion at a Coal-Fired Utility Boiler Using Hybrid Particle Swarm Optimization with Invasive Weed\",\"authors\":\"Huan Zhao, Pei-hong Wang, Xianyong Peng, Jin Qian, Quan Wang\",\"doi\":\"10.1109/ICEET.2009.143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to meet the requirement of high efficiency and low NOx emission combustion of coal-fired boiler, two constrained optimization objectives were designed based on the practical requirement, the one was that maximization of boiler efficiency under NOx emission constraint, and the other one was that minimization of NOx emission under a good boiler performance. Considering the complexity of response characteristics modeling about efficiency and NOx emission, a hybrid particle swarm optimization with invasive weed (IW-PSO) was proposed to optimize the constrained objective functions. In IW-PSO, particle swarm optimization (PSO) and invasive weed optimization (IWO) were integrated in parallel form, and after some iteration, IWO was considered to assist PSO. And in the process of optimizing, variational optimization objective values were tracked by inspection of the results conducted previously. The optimized results indicate that the proposed method can effectively control NOx emission and improve boiler efficiency for different constrained objectives.\",\"PeriodicalId\":6348,\"journal\":{\"name\":\"2009 International Conference on Energy and Environment Technology\",\"volume\":\"90 1\",\"pages\":\"564-567\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Energy and Environment Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEET.2009.143\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Energy and Environment Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEET.2009.143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

为了满足燃煤锅炉高效低NOx排放燃烧的要求,根据实际需要设计了两个约束优化目标,即在NOx排放约束下锅炉效率最大化和在良好的锅炉性能下使NOx排放最小化。考虑效率和NOx排放响应特性建模的复杂性,提出了一种带有入侵杂草的混合粒子群优化算法(IW-PSO)来优化约束目标函数。在IW-PSO中,粒子群优化(PSO)和入侵杂草优化(IWO)以并行的形式集成,经过一定的迭代,IWO被认为是对PSO的辅助。在优化过程中,通过对之前结果的检验,跟踪变分优化目标值。优化结果表明,在不同约束目标下,该方法能有效控制NOx排放,提高锅炉效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constrained Optimization of Combustion at a Coal-Fired Utility Boiler Using Hybrid Particle Swarm Optimization with Invasive Weed
In order to meet the requirement of high efficiency and low NOx emission combustion of coal-fired boiler, two constrained optimization objectives were designed based on the practical requirement, the one was that maximization of boiler efficiency under NOx emission constraint, and the other one was that minimization of NOx emission under a good boiler performance. Considering the complexity of response characteristics modeling about efficiency and NOx emission, a hybrid particle swarm optimization with invasive weed (IW-PSO) was proposed to optimize the constrained objective functions. In IW-PSO, particle swarm optimization (PSO) and invasive weed optimization (IWO) were integrated in parallel form, and after some iteration, IWO was considered to assist PSO. And in the process of optimizing, variational optimization objective values were tracked by inspection of the results conducted previously. The optimized results indicate that the proposed method can effectively control NOx emission and improve boiler efficiency for different constrained objectives.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信