Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler

This paper focuses on NOx emission prediction and operating parameters optimization for coal-fired boilers. Support Vector Regression (SVR) model based on CGA (Conventional Genetic Algorithm) was proposed to model the relationship between the operating parameters and the concentration of NOx emission. Then CGA and two modified algorithms, the Quantum Genetic Algorithm (QGA) and SAGA (Simulated Annealing Genetic Algorithm), were employed to optimize the operating parameters of the coal-fired boiler to reduce NOx emission. The results showed that the proposed SVR model was more accurate than the widely used Artificial Neural Network (ANN) model when employed to predict the concentration of NOx emission. The mean relative error and correlation coefficient calculated by the proposed SVR model were 2.08% and 0.95, respectively. Among the three optimization algorithms implemented in this paper, the SAGA showed superiority to the other two algorithms considering the quality of solution within a given computing time. The SVR plus SAGA method was preferable to predict the concentration of NOx emission and further to optimize the operating parameters to achieve low NOx emission for coal-fired boilers.