Abstract
The control of flue gas emission in thermal power plants has been a topic of concern. Selective catalytic reduction technology has been widely used as an effective flue gas treatment technology. However, precisely controlling the amount of ammonia injected remains a challenge. Too much ammonia not only causes secondary pollution but also corrodes the reactor equipment, while too little ammonia does not effectively reduce the NOx content. In recent years, deep reinforcement learning has achieved better results than traditional methods in decision making and control, which provides new methods for better control of selective catalytic reduction systems. The purpose of this research is to design an intelligent controller using reinforcement learning technology, which can accurately control ammonia injection, and achieve higher denitrification effect and less secondary pollution. To train the deep reinforcement learning controller, a high-precision virtual denitration environment is first constructed. In order to make the virtual environment more realistic, this virtual environment was designed as a special structure with two decoders and a unique approach was used in fitting the virtual environment. A deep deterministic policy agent is used as an intelligent controller to control the amount of injected ammonia. To make the intelligent controller more stable, the actor-critic framework and the experience pool approach were adopted. The results show that the intelligent controller can control the emissions of nitrogen oxides and ammonia at the outlet of the reactor after training in virtual environment.
Highlights
AND BACKGROUNDIn China, thermal power generation is still the main way of generating electricity (Tang et al, 2018)
The results show that the intelligent controller can control the emissions of nitrogen oxides and ammonia at the outlet of the reactor after training in virtual environment
In the experiments to train the intelligent controllers, they were designed with different structures and a soft update approach in order to avoid coupling between the actor network and the critic network
Summary
AND BACKGROUNDIn China, thermal power generation is still the main way of generating electricity (Tang et al, 2018). Artificial Intelligent Ammonia Injection Control principle of SCR technology is to reduce nitrogen oxides to nitrogen and oxygen by spraying a reductant such as ammonia in the flue gas denitrification reactor. Ammonia is used as a reducing agent in the SCR reaction, which has some negative effects while eliminating nitrogen oxides. These negative effects include ammonia escape, corrosion of equipment and cost increase. It is difficult to strike a delicate balance between denitrification and side reactions This is mainly due to fluctuations in NOx concentrations caused by frequent load adjustments in power plants, where the adjustment of the ammonia quantity is significantly slower than the actual demand due to the delayed characteristics of the measurement control system
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