摘要
对在役选择性催化还原(SCR)烟气脱硝系统催化剂的健康状况和剩余寿命进行预测,成为燃煤电厂科学维护和安全使用烟气脱硝系统的迫切需要。针对催化剂活性与服役时间的样本数据具有灰色的特点,本文基于在电厂服役催化剂的实测数据,建立了灰色预测模型和多种曲线拟合模型,并按照数据是否等时距分类,对催化剂的相对活性进行预测。结果表明:当催化剂活性测量数据满足等时距要求时,采用GM(1,1)模型预测效果较好,模型精度较高;当不满足等时距测量要求时,采用二阶多项式模型预测效果较好。利用该方法对催化剂寿命进行预测结果较为准确,可为燃煤电厂SCR脱硝催化剂的寿命管理提供依据。
The prediction of health status and remaining life of in-service SCR catalysts is urgent for scientific maintenance management and safe use of the de-NOx system. According to the grey characteristics of sample data of the catalyst activity and service time, a gray forecasting model and various curve fitting model were established based on activity data of in-service catalysts in thermal power plants. Moreover, relative catalyst activity was then predicted depending on whether the data were in equidistant time distribution. The results show that, when the catalyst activity measurement time met the equidistant time requirement, using the GM（1,1） model could achieve good prediction result and high model precision. When the measurement time didn＇t meet the equidistant time requirement, the second order polynomial model was effective. Overall, the established models possessed accurate capabilities in predicting the in-service catalyst life, which provided references for the life management of SCR catalysts in thermal power plants.
作者
傅玉
陆强
庄柯
王琦
李慧
董长青
FU Yu LU Qiang ZHUANG Ke WANG Qi LI Hui DONG Changqing(National Engineering Laboratory for Biomass Power Generation Equipment, North China Electric Power University, Beijing 102206, China State Power Environmental Protection Research Institute, Nanjing 210031, China)
出处
《热力发电》
CAS
北大核心
2017年第7期60-65,78,共7页
Thermal Power Generation
基金
国家重点基础研究发展计划项目(973计划)(2015CB251501)
中央高校基本科研业务专项资金资助项目(2015ZZD02
2016YQ05)~~
关键词
烟气脱硝
SCR催化剂
相对活性
寿命预测
灰色预测模型
曲线拟合
最小二乘法
等时距
flue gas denitr-ation, SCR catalyst, relative catalyst activity, life prediction, grey model, curve fitting,least square method, equal time intreval
作者简介
傅玉（1992-），女，硕士研究生，主要研究方向为SCR脱硝催化剂的寿命预测与管理，fuyu5255@163．com。
陆强（1982-），男，博士，副教授，硕士生导师，主要研究方向为固体燃料热化学转化与烟气污染物治理，qianglu@mail．ustc．edu．cn。
