基于改进MVO和WLSSVM的燃煤锅炉NO_(x)排放优化  

Optimization of NO_(x) emissions from coal-fired boilers based on improved MVO and WLSSVM

作　　者：梁涛 靳云杰 姜文 刘子豪 LIANG Tao;JIN Yunjie;JIANG Wen;LIU Zihao(College of Artificial Intelligence and Data Science,Hebei University of Technology,Tianjin 300130,China;Hebei Jiantou Energy Investment Co.,Ltd.,Shijiazhuang 050011,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300130 [2]河北建投能源投资股份有限公司,河北石家庄050011

出　　处：《中国测试》2021年第10期148-154,共7页China Measurement & Test

基　　金：河北省科技支撑计划资助项目(19210108D,19214501D,20314501D)。

摘　　要：为降低电厂燃煤锅炉的NO_(x)排放浓度,提出一种基于改进多元宇宙优化算法(improved multi-verse optimizer algorithm,IMVO)和加权最小二乘支持向量机(weighted least squares support vector machine,WLSSVM)的锅炉NO_(x)排放优化方法。首先,针对多元宇宙优化算法TDR值下降速度较慢而导致旅行距离增加的问题,提出一种改进的多元宇宙算法;然后,采用IMVO算法对WLSSVM模型参数进行寻优,建立基于IMVO-WLSSVM的NO_(x)排放量预测模型;最后,基于所建预测模型,采用IMVO算法对锅炉运行可调参数进行寻优来降低NO_(x)排放浓度。采用某330 MW机组燃煤锅炉的运行数据对模型进行验证,结果表明:所建预测模型的平均绝对百分比误差为1.09%,相对于其他几种预测模型具有更高的预测精度,改进的多元宇宙优化算法可以使优化后的NO_(x)排放浓度更低,具有更好的寻优效果。In order to reduce the NO_(x)emission concentration of coal-fired boilers in power plants,a method for boiler NO_(x)emission optimization based on improved multi-verse optimizer algorithm(IMVO)and weighted least squares support vector machine(WLSSVM)is proposed.First of all,to solve the problem that the TDR value of the multi-verse optimizer algorithm decreases slowly and the travel distance increases,an improved multi-verse optimizer algorithm is proposed.Then,the IMVO algorithm is used to optimize the WLSSVM model parameters,and the NO_(x)emission prediction model based on IMVO-WLSSVM is established.Finally,based on the built prediction model,the IMVO algorithm is used to optimize the adjustable parameters of the boiler operation to reduce the NO_(x)emission concentration.The model was verified with the operating data of a 330 MW unit coal-fired boiler,and the results showed that the average absolute percentage error of the built prediction model is 1.09%,which has a higher prediction accuracy than other prediction models.And the improved multi-verse optimization algorithm can make the optimized NO_(x)emission concentration lower and have a better optimization effect.

关 键 词：燃煤锅炉 NO_(x)排放量预测 燃烧优化 多元宇宙优化算法 加权最小二乘支持向量机 

分 类 号：TM621.2[电气工程—电力系统及自动化] TP391[自动化与计算机技术—计算机应用技术]