With the completion of ultra-low emission transformation of thermal power plants

problems such as increased costs and excessive ammonia injection have arisen. Modeling and optimization of power plant operation data through machine learning has become an important means to solve the above problems. This article reviews the commonly used machine learning algorithms and their application scenarios in reducing nitrogen oxides. In terms of algorithm

the main algorithms of data preprocessing

modeling prediction and parameter optimization and their applicability to nitrogen oxides removal are summarized. The research directions of multi-operating condition data preprocessing method and the construction method of the objective function in multi-objective optimization are proposed. For the application level of the machine learning methods

such as low nitrogen combustion in the furnace

optimization of SCR denitration system

and comprehensive energy saving and consumption reduction of the whole system

the implementation methods and corresponding effects are summarized. The future research directions of long-period dynamic modeling control and multi-power plant joint modeling have prospected.