【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。更多还原

【Abstract】 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.更多还原