• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2014, Vol. 35 ›› Issue (1): 93-97.doi: 10.3969/j.issn.1000-7229.2014.01.018

• 发电技术 • 上一篇    下一篇

燃煤电厂脱硝还原剂的模糊综合性评判

张翔1,2,许红胜2,赵龙生2   

  1. 1.东南大学能源与环境学院,南京市 210018;2.东南大学热电工程设计研究所,南京市 210096
  • 出版日期:2014-01-01
  • 作者简介:张翔(1989),男,硕士研究生,研究方向为电厂NOx排放物控制及减排,E-mail:zx1989tc@163.com; 许红胜(1963),男,博士,教授,东南大学建筑设计研究院副院长,东南大学热电工程设计研究所所长,研究方向为电厂污染物减排,电厂智能控制; 赵龙生(1965),男,硕士,高级工程师,研究方向为电厂脱硫脱硝及优化。

Fuzzy Comprehensive Evaluation for Reducing Agents for Flue Gas Denitration Systems in Coal-Fired Power Plants

ZHANG Xiang1,2, XU Hongsheng2, ZHAO Longsheng2   

  1. 1. College of Energy and Environment,Southeast University, Nanjing 210018, China;2. Thermoelectric Engineering and Research Institute, Southeast University, Nanjing 210096, China
  • Online:2014-01-01

摘要:

目前燃煤电厂烟气脱硝系统采用的还原剂主要是液氨、氨水和尿素,实际工程中还原剂的选取是一个难题。为此,提出了基于模糊数学的综合评判法和隶属度理论,将脱硝还原剂各评判指标作定量分析,根据最大隶属度原则得出评价结果。根据实际情况分析了各因素权重对最终结果所带来的影响,从而得出了液氨为最佳的评判结果。在大机组选择性催化还原法脱硝系统工艺的还原剂选取过程中,若液氨、氨水和尿素都可取,由于液氨经济性突出,技术性能相比稍优,综合来说属于最佳选择。

关键词: 燃煤电厂, 烟气脱硝, 还原剂, 模糊综合评判

Abstract:

The reducing agents for flue gas denitration systems in coal-fired power plants mainly include liquid ammonia, ammonia water and urea. However, the selection of reducing agents in practical project is always a troublesome question. Based on fuzzy comprehensive evaluation method and membership degree principle, the quantitative analysis of evaluation index were carried out for reducing agent, and the evaluation results were obtained according to the principle of maximum membership degree. Then, this paper analyzed the impact of factor weights on the final results according to the actual situation, and obtained the result that liquid ammonia was best. Liquid ammonia, ammonia water and urea all can be used as reducing agent for the SCR denitration system in large units, but liquid ammonia is the best because of its prominent economy characteristics and slightly better technical performance.

Key words: coal-fired power plant, flue gas denitration, reducing agent, fuzzy comprehensive evaluation