考虑输入变量时滞的NO_x生成量动态建模
|
摘要
为提高选择性催化还原(SCR)脱硝系统的控制品质与经济性,需要建立精确的SCR脱硝反应器入口NO_x生成量动态模型。本文基于模糊树模型建立了SCR反应器入口NO_x生成量动态模型,提出采用复相关系数的时滞联合估计方法,对影响NO_x生成量的模型输入变量进行时滞估计。将该方法应用于某600 MW燃煤机组的运行数据,并对输入变量采用相关系数法和复相关系数法进行时滞估计对比。结果表明:模糊树模型的建模精度较高、泛化能力强;在模型输入变量进行时滞估计时,相较于相关系数法,采用复相关系数法对模型精度和泛化能力提升程度更高。
In order to improve the control quality and economy of selective catalytic reduction(SCR) denitration system, it is necessary to establish an accurate dynamic model of NO_x production at inlet of the SCR denitrification reactor. On the basis of fuzzy tree model, this paper builds up a dynamic model for NO_x production at the SCR reactor inlet, and proposes using a joint time-delay estimation method based on multiple correlation coefficients to estimate the time-delay of the input variables which affect the NO_x production. The method was applied to the operation data of a 600 MW coal-fired unit, and the time-delay of input variables were estimated by correlation coefficient method and multiple correlation coefficient method. The experimental results show that,the fuzzy-tree model has high accuracy and generalization ability, the time-delay estimation of input variables based on multiple correlation coefficient method improves more accurate and generalization ability for the model than the correlation coefficient method.
In order to improve the control quality and economy of selective catalytic reduction(SCR) denitration system, it is necessary to establish an accurate dynamic model of NO_x production at inlet of the SCR denitrification reactor. On the basis of fuzzy tree model, this paper builds up a dynamic model for NO_x production at the SCR reactor inlet, and proposes using a joint time-delay estimation method based on multiple correlation coefficients to estimate the time-delay of the input variables which affect the NO_x production. The method was applied to the operation data of a 600 MW coal-fired unit, and the time-delay of input variables were estimated by correlation coefficient method and multiple correlation coefficient method. The experimental results show that,the fuzzy-tree model has high accuracy and generalization ability, the time-delay estimation of input variables based on multiple correlation coefficient method improves more accurate and generalization ability for the model than the correlation coefficient method.
引文
[1]火电厂大气污染物排放标准:GB 13223-2011[S].北京:中国环境科学出版社,2011.Emission standard of air pollutants for thermal power plants:GB 13223-2011[S].Beijing:China Environ-mental Science Press,2011.
[2]张东辉,庄烨,朱润儒,等.燃煤烟气污染物超低排放技术及经济分析[J].电力建设,2015,36(5):125-130.ZHANG Donghui,ZHUANG Ye,ZHU Runru,et al.Ultra-low air pollutant control technologies for coal-fired flue gas and its economic analysis[J].Electric Power Construction,2015,36(5):125-130.
[3]伏甲琪,郭为民,刘延泉,等.SCR烟气脱硝系统Smith预估条件积分型滑模控制[J].热力发电,2017,46(4):93-98.FU Jiaqi,GUO Weimin,LIU Yanquan,et al.Conditional integral sliding mode control for SCR denitrification system based on Smith predictor[J].Thermal Power Generation,2017,46(4):93-98.
[4]FORTUNA L,GRAZIANI S,XIBILIA M G.Soft sensors for product quality monitoring in debutanizer distillation columns[J].Control Engineering Practice,2005,13(4):499-508.
[5]GOVINDAN R B,RAETHJEN J,KOPPER F,et al.Estimation of time delay by coherence analysis[J].Physica A Statistical Mechanics&Its Applications,2004,350(2):277-295.
[6]LOU H,SU H,XIE L,et al.Inferential model for industrial polypropylene melt index prediction with embedded priori knowledge and delay estimation[J].Industrial&Engineering Chemistry Research,2012,51(25):8510-8525.
[7]李妍君,熊伟丽,徐保国.一种带过程变量时滞估计的在线软测量建模方法[J].信息与控制,2016,45(6):641-646.LI Yanjun,XIONG Weili,XU Baoguo.An online soft-sensor modeling method including process variable time-delay estimation[J].Information and Control,2016,45(6):641-646.
[8]阮宏镁,田学民,王平.基于联合互信息的动态软测量方法[J].化工学报,2014,65(11):4497-4502.RUAN Hongmei,TIAN Xuemin,WANG Ping.Dynamic soft sensor method based on joint mutual information[J].Journal of Chemical Industry and Engineering(China),2014,65(11):4497-4502.
[9]王钧炎,黄德先.基于混合差分进化算法的软测量时延参数估计[J].化工学报,2008,59(8):2058-2064.WANG Junyan,HUANG Dexian.Time delay estimation of soft-sensor model based on hybrid differential evolution[J].Journal of Chemical Industry and Engineering(China),2008,59(8):2058-2064.
[10]管燕,吴和成,黄舜.基于改进DEA的江苏省科技资源配置效率研究[J].科研管理,2011,32(2):145-150.GUAN Yan,WU Hecheng,HUANG Shun.The efficiency of science and technology resource allocation in Jiangsu Province based on an improved DEA model[J].Science Research Management,2011,32(2):145-150.
[11]范德成,李昊,刘贇.基于改进DEA--以复相关系数为基准的滞后期的我国产业结构演化效率评价[J].运筹与管理,2016,25(3):195-203.FAN Decheng,LI Hao,LIU Yun.A modified DEAmodel:with the lag periods based on multiple correlation coefficients to evaluate the efficiency of industriai structure evolution in china[J].Operations Research and Management Science,2016,25(3):195-203.
[12]许童羽,马艺铭,曹英丽,等.基于主成分分析和遗传优化BP神经网络的光伏输出功率短期预测[J].电力系统保护与控制,2016,44(22):90-95.XU Tongyu,MA Yiming,CAO Yingli,et al.Short term forecasting of photovoltaic output power based on principal component analysis and genetic optimization of BP neural network[J].Power System Protection and Control,2016,44(22):90-95.
[13]王宁,谢敏,邓佳梁,等.基于支持向量机回归组合模型的中长期降温负荷预测[J].电力系统保护与控制,2016,44(3):92-97.WANG Ning,XIE Min,DENG Jialiang,et al.Mid-long term temperature-lowering load forecasting based on combination of support vector machine and multiple regression[J].Power System Protection and Control,2016,44(3):92-97.
[14]MAO J,ZHANG J,YUE Y,et al.Adaptive-tree-structurebased fuzzy inference system[J].IEEE Transactions on Fuzzy Systems,2005,13(1):1-12.
[15]ZHANG W,MAO J.Robust intelligent modeling for giant magnetostrictive actuators with rate-dependent hysteresis[J].International Journal of Intelligent Computing&Cybernetics,2012,5(4):500-514.
[16]ZHANG W,ZHANG Y,SUN Y,et al.Condition monitoring of limestone and soft sensing model of SO2emissions in a circulating fluidized bed boiler[J].Energy&Fuels,2016,30(4):3519-3528.
[17]ZHANG W,ZHANG Y,BAI X,et al.A robust fuzzy tree method with outlier detection for combustion models and optimization[J].Chemometrics&Intelligent Laboratory Systems,2016,158:130-137.
[18]吕游,刘吉臻,杨婷婷,等.[J].仪器仪表学报,2013,34(11):2418-2424.LV You,LIU Jizhen,YANG Tingting,et al.NOx emission characteristic modeling based on feature extraction using PLS and LS-SVM[J].Chinese Journal of Scientific Instrument,2013,34(11):2418-2424.
[2]张东辉,庄烨,朱润儒,等.燃煤烟气污染物超低排放技术及经济分析[J].电力建设,2015,36(5):125-130.ZHANG Donghui,ZHUANG Ye,ZHU Runru,et al.Ultra-low air pollutant control technologies for coal-fired flue gas and its economic analysis[J].Electric Power Construction,2015,36(5):125-130.
[3]伏甲琪,郭为民,刘延泉,等.SCR烟气脱硝系统Smith预估条件积分型滑模控制[J].热力发电,2017,46(4):93-98.FU Jiaqi,GUO Weimin,LIU Yanquan,et al.Conditional integral sliding mode control for SCR denitrification system based on Smith predictor[J].Thermal Power Generation,2017,46(4):93-98.
[4]FORTUNA L,GRAZIANI S,XIBILIA M G.Soft sensors for product quality monitoring in debutanizer distillation columns[J].Control Engineering Practice,2005,13(4):499-508.
[5]GOVINDAN R B,RAETHJEN J,KOPPER F,et al.Estimation of time delay by coherence analysis[J].Physica A Statistical Mechanics&Its Applications,2004,350(2):277-295.
[6]LOU H,SU H,XIE L,et al.Inferential model for industrial polypropylene melt index prediction with embedded priori knowledge and delay estimation[J].Industrial&Engineering Chemistry Research,2012,51(25):8510-8525.
[7]李妍君,熊伟丽,徐保国.一种带过程变量时滞估计的在线软测量建模方法[J].信息与控制,2016,45(6):641-646.LI Yanjun,XIONG Weili,XU Baoguo.An online soft-sensor modeling method including process variable time-delay estimation[J].Information and Control,2016,45(6):641-646.
[8]阮宏镁,田学民,王平.基于联合互信息的动态软测量方法[J].化工学报,2014,65(11):4497-4502.RUAN Hongmei,TIAN Xuemin,WANG Ping.Dynamic soft sensor method based on joint mutual information[J].Journal of Chemical Industry and Engineering(China),2014,65(11):4497-4502.
[9]王钧炎,黄德先.基于混合差分进化算法的软测量时延参数估计[J].化工学报,2008,59(8):2058-2064.WANG Junyan,HUANG Dexian.Time delay estimation of soft-sensor model based on hybrid differential evolution[J].Journal of Chemical Industry and Engineering(China),2008,59(8):2058-2064.
[10]管燕,吴和成,黄舜.基于改进DEA的江苏省科技资源配置效率研究[J].科研管理,2011,32(2):145-150.GUAN Yan,WU Hecheng,HUANG Shun.The efficiency of science and technology resource allocation in Jiangsu Province based on an improved DEA model[J].Science Research Management,2011,32(2):145-150.
[11]范德成,李昊,刘贇.基于改进DEA--以复相关系数为基准的滞后期的我国产业结构演化效率评价[J].运筹与管理,2016,25(3):195-203.FAN Decheng,LI Hao,LIU Yun.A modified DEAmodel:with the lag periods based on multiple correlation coefficients to evaluate the efficiency of industriai structure evolution in china[J].Operations Research and Management Science,2016,25(3):195-203.
[12]许童羽,马艺铭,曹英丽,等.基于主成分分析和遗传优化BP神经网络的光伏输出功率短期预测[J].电力系统保护与控制,2016,44(22):90-95.XU Tongyu,MA Yiming,CAO Yingli,et al.Short term forecasting of photovoltaic output power based on principal component analysis and genetic optimization of BP neural network[J].Power System Protection and Control,2016,44(22):90-95.
[13]王宁,谢敏,邓佳梁,等.基于支持向量机回归组合模型的中长期降温负荷预测[J].电力系统保护与控制,2016,44(3):92-97.WANG Ning,XIE Min,DENG Jialiang,et al.Mid-long term temperature-lowering load forecasting based on combination of support vector machine and multiple regression[J].Power System Protection and Control,2016,44(3):92-97.
[14]MAO J,ZHANG J,YUE Y,et al.Adaptive-tree-structurebased fuzzy inference system[J].IEEE Transactions on Fuzzy Systems,2005,13(1):1-12.
[15]ZHANG W,MAO J.Robust intelligent modeling for giant magnetostrictive actuators with rate-dependent hysteresis[J].International Journal of Intelligent Computing&Cybernetics,2012,5(4):500-514.
[16]ZHANG W,ZHANG Y,SUN Y,et al.Condition monitoring of limestone and soft sensing model of SO2emissions in a circulating fluidized bed boiler[J].Energy&Fuels,2016,30(4):3519-3528.
[17]ZHANG W,ZHANG Y,BAI X,et al.A robust fuzzy tree method with outlier detection for combustion models and optimization[J].Chemometrics&Intelligent Laboratory Systems,2016,158:130-137.
[18]吕游,刘吉臻,杨婷婷,等.[J].仪器仪表学报,2013,34(11):2418-2424.LV You,LIU Jizhen,YANG Tingting,et al.NOx emission characteristic modeling based on feature extraction using PLS and LS-SVM[J].Chinese Journal of Scientific Instrument,2013,34(11):2418-2424.