Abstract
This paper studies on researching the method of reducing NOx production and coal consumption of coal-fired power station boiler. It takes a power plant 600MW subcritical boiler as the research object, from the power plant Supervisory Information System (SIS) it gets the historical operation data as experimental data. Based on GA-GRNN (generalized regression neural network based on genetic optimization), a predictive model of boiler combustion system with 39 variables such as inlet and output of coal consumption and NOx production was established. Finally, coal consumption and NOx production were optimized based on the neural network model of boiler combustion system. In this paper, 29 adjustable thermal parameters of boiler combustion system model input are selected as optimization variables and the improved NSGA-II (non-dominated sorting genetic algorithm) is used to optimize multiple objective variables. The optimization study was carried out under the actual operating condition of 349.21 MW. After optimization, the coal consumption of power supply was reduced by 5.67% and the NOx production was reduced by 50%. Therefore, the optimization results provide guidance for adjusting the combustion of utility boilers.
Highlights
Entering the 21st century, the installed power capacity in China has developed rapidly
Based on the current status of thermal power generation, power plants need to improve the efficiency of generating units, as far as possible to make the work of generating units tend to the optimal working conditions and reduce coal consumption
This paper is based on the historical operation data of a 600MW subcritical boiler in a power plant, the prediction model of Generalized regression neural networks (GA-GRNN) based on genetic optimization is obtained, by using generalized regression neural network (GRNN) to establish a combustion system prediction model, and utilize genetic algorithm (GA) to optimize the SPREAD value of the extended constant of the radial basis function of the neural network mode
Summary
Entering the 21st century, the installed power capacity in China has developed rapidly. This paper is based on the historical operation data of a 600MW subcritical boiler in a power plant, the prediction model of Generalized regression neural networks (GA-GRNN) based on genetic optimization is obtained, by using generalized regression neural network (GRNN) to establish a combustion system prediction model, and utilize genetic algorithm (GA) to optimize the SPREAD value of the extended constant of the radial basis function of the neural network mode Based on this model, the advanced non dominated sorting genetic algorithm (NSGA-II) is adjustable for multi-objective optimization of boiler operation parameters[2,3]
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