Combustion optimization model for NOx reduction with an improved particle swarm optimization

Abstract: This paper focuses on the combustion optimization to cut down NOxemission with a new strategy. Firstly, orthogonal experimental design (OED) and chaotic sequences are introduced to improve the performance of particle swarm optimization (PSO). Then, a predicting model for NOxemission is established on support vector machine (SVM) whose parameters are optimized by the improved PSO. Afterwards, a new optimization model considering coal quantity and air quantity along with the traditional optimization variables is established. At last, the operating parameters are optimized by the improved PSO to cut down the NOxemission. An application on 600MW unit shows that the new optimization model can cut down NOxemission effectively and maintain the load balance well. The NOxemission optimized by the improved PSO is lowest among some state-of-the-art intelligent algorithms. This study can provide important guides for the low NOxcombustion in the power plant. © 2016, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.