Standard Heat Consumption Modelling Calculation and Operation Optimization of Boiler Steam Temperature System

Abstract

Abstract The combination of neural network and genetic algorithm not only can improve the operating efficiency and economy of the boiler, but also give the recommended values of the operating parameters. Based on the power plant, the number of hidden layers, nodes and learning rate of the neural network modelling calculation are determined according to the data experiments. Taking the standard heat consumption as the target value, the adjustable and non-adjustable quantities of the input parameters are analysed, the parameters such as the crossover rate, the mutation rate and the GGAP suitable for the research object are selected, and the genetic algorithm is used for optimization. The results show that the standard heat consumptions of all the 100 groups of working conditions are reduced. The average reduction is 153.93 kJ/(kW•h). This indicates that by modelling and optimizing the parameters such as the superheater desuperheating water flow, it can provide operational guidance for actual production.