Fuzzy optimization control for NOx emissions from power plant boilers based on nonlinear optimization

Combustion optimization adjustment can effectively suppress NOx emissions from power plant boilers. Current combustion optimization adjustment methods involve nonlinear optimization based on the boiler combustion model, such as optimization by a genetic algorithm or particle swarm algorithm. The computational complexity of these methods results in poor real-time performance, which limits their practical applications. To solve this problem, a fuzzy optimization control method with better real-time performance is proposed. First, the space of the disturbance variables (DV), which are the input variables that combustion systems cannot adjust, is divided into a certain number of sub-spaces. Each sub-space center is then obtained using the corresponding optimal combustion mode by offline nonlinear optimization, thereby forming a complete expert rule base. The corresponding optimal manipulated variables (MV), which are the input variables that combustion systems can adjust, are then quickly obtained online by means of fuzzy inference for each inputted DV. The fuzzy optimization control of boiler combustion adjustment is then determined. Simulation has shown that both the fuzzy optimization control method and the nonlinear optimization method can achieve a consistent control effect. However, the fuzzy optimization control method has a better real-time performance.