Modeling of SNCR denitration system based on adaptive weight particle swarm optimization

Abstract

In view of the improvement of NOX emission control requirements for coal-fired units, traditional PID controllers have been unable to effectively control large delay, large inertia, nonlinear, time-varying SNCR denitration systems, and most advanced control algorithms are often object-based models. Therefore, a model of SNCR denitration system based on adaptive weight particle swarm optimization algorithm is established. A 2×200MW heating steam turbine generator set equipped with a 2×705t/h circulating fluidized bed boiler was used as the test unit, and the selective non-catalytic reduction technology denitration system was analysed. The particle swarm optimization algorithm with adaptive weights was used to model the relationship between the urea flow rate and the NOX concentration at the chimney outlet of the SNCR denitration system under working conditions of 140 MW, 170 MW and 200 MW, respectively, to provide a process for the automatic control of the SNCR denitration systemmodel. Apply the actual data from the field to verify the model. The results show that the error between the output of the model and the actual operational 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 process, and promotes the application of intelligent algorithms in other industrial processes.