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超临界W火焰锅炉性能研究与实践总结 被引量:1

Research and Operation Summary of Supercritical Down-fired Boiler
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摘要 本文对已投产的5台超临界W火焰锅炉的运行情况进行分析,并对水冷壁易出现的超温拉裂受损的现象进行了研究,针对水冷壁存在的问题,提出切实有效的解决措施,对超临界W火焰锅炉的安全经济运行具有重要意义。 The operation characteristics of five supercritical down-fired boilers in Huadian Group were analyzed,and waterwall prone to over-temperature tensile fracture damage had been researched.Aim at the problem of waterwall,effective solutions have been proposed.It is of great significance for safe and economic operation of supercritical down-fired flame boiler units.
作者 石尚强
出处 《发电与空调》 2013年第3期9-12,共4页
关键词 超临界W火焰锅炉 设计优化 水冷壁 超温 拉裂 supercritical down-fired flame boiler units design optimization waterwall over-temperature tensile fracture damage
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  • 1张海,吕俊复,徐秀清,曾瑞良,岳光溪.W型火焰锅炉燃烧问题的分析和解决方法[J].动力工程,2005,25(5):628-632. 被引量:27
  • 2锅炉[J].动力工程:英文版,2005(5):17-20. 被引量:5
  • 3王春林,周昊,周樟华,凌忠钱,李国能,岑可法.基于支持向量机的大型电厂锅炉飞灰含碳量建模[J].中国电机工程学报,2005,25(20):72-76. 被引量:97
  • 4许昌,吕剑虹,郑源,冯晓琼.以效率和低NO_x排放为目标的锅炉燃烧整体优化[J].中国电机工程学报,2006,26(4):46-50. 被引量:37
  • 5郭辉,刘贺平,王玲.最小二乘支持向量机参数选择方法及其应用研究[J].系统仿真学报,2006,18(7):2033-2036. 被引量:103
  • 6Zheng Ligang, Hao Zhou, Cen Kefa, et al. A comparative study of optimization algorithms for low NO combustion modification at a coal-firedutilityboiler[J]. Expert Systems with Applications, 2009, 36(2): 2780-2793.
  • 7Zheng L G, Zhou H, Wang C L, et al. Combining support vector regression and ant colony optimization to reduce NOx emissions in coal-fired utility boilers[J]. Energy & Fuels, 2008, 22(2): 1034-1040.
  • 8Zhou H, Cen K F, Fan J R. Modeling and optimization of the NOx emission characteristics of a tangentially ftred boiler with artificial neural networks[J]. Energy, 2004, 29(1): 167-183.
  • 9Anderson S R, Kadirkamanathan V, Chipperfield A, et al. Multi- objective optimization of operational variables in a waste incineration plant[J]. Computers & Chemical Engineering, 2005, 29(5): 1121-1130.
  • 10Zhou H, Cen K F, Fan J R. Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms[J]. Intemational Journal of Energy Research, 2005, 29(6):499-510.

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