| With the acceleration of my country’s economic and social development,in recent years,for our lives,electricity has become more and more important,and people cannot do without it.So far,the main source of electricity is thermal power plants that burn coal through boilers.The pollutants such as NOX,PM particles and SO2in the exhaust gas produced by burning coal have caused great pollution to the atmosphere,and also have a great impact on our lives.In response to these problems,the ultra-low emission system of coal-fired boilers removes a variety of pollutants.A typical ultra-low emission system of coal-fired boilers is mainly composed of dry and wet electric dust removal devices,denitrification devices,and desulfurization devices.There is a cooperative removal relationship between the devices.operating cost of coal-fired boilers,and there are few results aimed at optimizing ultra-low emission systems.This shows that this research has important practical significance.The following will introduce in detail:（1）The main research of this paper is to optimize the ultra-low emission system of coal-fired boilers using the non-dominated sorting genetic algorithm with elite strategy（NSGA2）.This process is a process of multi-objective optimization.A mathematical model for objective optimization.The objective functions of this model are the overall operating cost of the ultra-low emission system of coal-fired boilers and the sum of the concentration of various pollutants in the boiler flue gas.After the construction of the multi-objective optimization model is completed.In this research,some series of simulation experiments were conducted to verify the research results.（2）The ultra-low emission system of coal-fired boiler is a multi-disciplinary complex coupling system.This paper uses the collaborative optimization method in the multi-disciplinary design optimization method and combines NSGA2 to perform multi-objective optimization and collaborative optimization of the ultra-low emission system.The method is a two-level optimization framework,and a multi-objective optimization model is constructed at the system level.The subject level is divided into three sub-disciplines:denitrification,desulfurization,and dust removal.Collaborative optimization is carried out between the system level and the subject level.Finally,the feasibility of the multi-objective optimization algorithm based on collaborative optimization is verified through simulation experiments,and a set of Pareto non-inferior solution sets can be better obtained at the system level. |
