基于自适应权重粒子群算法的脱硝系统建模
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摘要
随着燃煤机组对NO_X排放控制要求的提升,传统PID控制器已无法有效控制大迟延、大惯性、非线性、时变的选择性非催化还原技术(SNCR)脱硝系统。因此,建立了基于自适应权重粒子群算法的脱硝系统模型。以某配有2×705 t/h循环流化床锅炉的2×200 MW供热汽轮发电机组为试验机组,进行了SNCR脱硝系统分析。采用带有自适应权重的粒子群算法,分别对工况为140 MW、170 MW、200 MW下的SNCR脱硝系统中尿素流量与烟囱出口NO_X浓度之间的关系进行建模,为SNCR脱硝系统的自动控制提供过程模型。应用现场实际数据验证所建模型。结果表明:所建模型的输出与实际运行数据误差在允许范围之内,验证了模型的有效性。该成果为粒子群算法在SNCR脱硝系统建模开辟了新路径,同时推动了智能算法在其他工业过程中的应用。
In view of the improvement of NO_X emission control requirements for coal-fired units,traditional PID controllers have been unable to effectively control large delays,large inertia,nonlinear,time-varying selective non-catalytic reduction technology(SNCR) denitration systems.Thus,a model of denitration system based on adaptive weighted particle swarm optimization is established.A 2×200 MW extraction steam condensing steam turbine generation unit with 2×705 t/h circulating fluidized bed boiler is used as the test unit,and the SNCR denitration system is analyzed.By adopting the adaptive weight particle swarm optimization algorithm,modeling for relationship between urea flow and the NO_X concentration at the stack outlet in the SNCR denitration system under 140 MW,170 MW and 200 MW operating conditions is carried out; and provides a process model for the automatic control of the SNCR denitration system.The model established is verified by the actual data in the field.The results show that the error between the output of the model and the actual running data is within the allowable range,which verifies the validity of the model.This result opens up a new path for the particle swarm optimization algorithm to model the SNCR denitration system; and promotes the application of intelligent algorithms in other industrial processes.
In view of the improvement of NO_X emission control requirements for coal-fired units,traditional PID controllers have been unable to effectively control large delays,large inertia,nonlinear,time-varying selective non-catalytic reduction technology(SNCR) denitration systems.Thus,a model of denitration system based on adaptive weighted particle swarm optimization is established.A 2×200 MW extraction steam condensing steam turbine generation unit with 2×705 t/h circulating fluidized bed boiler is used as the test unit,and the SNCR denitration system is analyzed.By adopting the adaptive weight particle swarm optimization algorithm,modeling for relationship between urea flow and the NO_X concentration at the stack outlet in the SNCR denitration system under 140 MW,170 MW and 200 MW operating conditions is carried out; and provides a process model for the automatic control of the SNCR denitration system.The model established is verified by the actual data in the field.The results show that the error between the output of the model and the actual running data is within the allowable range,which verifies the validity of the model.This result opens up a new path for the particle swarm optimization algorithm to model the SNCR denitration system; and promotes the application of intelligent algorithms in other industrial processes.
引文
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[11]OLAWOYIN R.Application of back propagation artificial neural network prediction model for the bioremediation of polluted soil[J].Chemosphere,2016(161):145- 150.
[12]李明磊,张海龙,杨明强.循环流化床锅炉SNCR反应机理与脱硝特性数值模拟[J].洁净煤技术,2018,24(3):96- 102.
[2] 白建云,朱竹军,张培华.基于BP神经网络的循环流化床锅炉生成NOX质量浓度在线软测量[J].热力发电,2016,45(12):78- 83.
[3] 朱竹军,白建云,刘林仙.专家模糊控制在SNCR脱硝系统中的研究及应用[J].自动化仪表,2018,39(7):34- 38.
[4] 秦天牧,刘吉臻,杨婷婷,等.火电厂SCR烟气脱硝系统建模与运行优化仿真[J].中国电机工程学报,2016,36(10):2699- 2703.
[5] 方贤,铁治欣,崔仕文,等.基于粒子群优化双支持向量机的SCR烟气脱硝效率预测模型[J].热力发电,2018,47(1):53- 58.
[6] 张友卫,曹硕硕,魏威,等.基于UKF- LSSVM的燃煤机组NOX排放浓度预测方法[J].自动化仪表,2018,39(12):13- 17.
[7] 韩璞.现代工程控制论[M].北京:中国电力出版社,2017.
[8] 黄太安,生佳根,徐红洋,等.一种改进的简化粒子群算法[J].计算机仿真,2013,30(2):327- 330+335.
[9] ZHU C X ,YING J Z,ZHOU H.The application of SNCR+SCR united flue gas denitration technique in power generation boilers[J].Electric Power,2016,49(2):164- 169.
[10]谢风林.300MW循环流化床锅炉中的SNCR脱硝研究[D].广州:华南理工大学,2015.
[11]OLAWOYIN R.Application of back propagation artificial neural network prediction model for the bioremediation of polluted soil[J].Chemosphere,2016(161):145- 150.
[12]李明磊,张海龙,杨明强.循环流化床锅炉SNCR反应机理与脱硝特性数值模拟[J].洁净煤技术,2018,24(3):96- 102.
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