基于灰色预测模型和曲线拟合模型的SCR烟气脱硝催化剂寿命预测

摘要

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.%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.