Dynamic Control Simulation On Spraying Ammonia And Structure Optimization Of SCR Flue Gas Denitrification In 320MW Coal.fired Boiler

China is a developing country, with the rapid development of economy, energy consumption will be greatly increased and the coal consumption will increase. A large amount of coal need to be consumed at the power generation process in the power plant, and then A large amounts of nitrogen oxides will be produced at the coal combustion. Along with our country's requirements on environmental protection policy is increasing year by year, the flue gas denitration should be installed in the coal.fired power plants to reduce emissions of nitrogen oxides.In this paper, based on the basic principle of selective catalytic reduction(SCR),according to the SCR flue gas denitration system of 320 MW coal.fired boiler, the structure of flue gas denitration reactor is optimized by numerical simulation analysis,and dynamic control of ammonia injection was carried out. The following conclusion are gained:(1) The SCR denitration reactor of 320 MW boiler in Gansu Jingyuan power plant is numerically studied and the structure of SCR denitration reactor is optimized. The mathematical and physical models are established in order to numerical simulation of denitrification system. The numerical simulation results show that: adding three blocks straight with uniform distribution at the inside and outside of the entranced side and changing the outer baffle height at the entrance diameter section can enhance the mixing uniformity of ammonia and flue gas, but also for the guide plates are optimized for the other position in SCR denitration reactor. The optimum scheme of flue gas denitrification reactor is gained.(2) The traditional PID control technology is widely used in the control technology of ammonia spray, but the conventional PID tuning has the disadvantages of large overshoot and long time. In this paper, the genetic algorithm is used to set the PID parameters to achieve the optimal control of ammonia injection quantity. The simulation results show that the control based on genetic algorithm can rapidly reach a stable, thereby greatly reducing the blindness of the initial optimization and savingcomputation. The control based on the genetic algorithm has a good ability to adapt to change conditions and save the cost of denitration.