Neural network prediction method based on boiler combustion characteristics

Abstract

According to the neural network prediction method based on the boiler combustion characteristics, the boiler combustion prediction model is established based on the two-layer genetic algorithm and the BP neural network, the group elite is reserved through the first-layer genetic algorithm, convergence can be ensured, the second-layer genetic algorithm adds a new population to the elite group reserved by the first-layer genetic algorithm, and the prediction accuracy is improved. The population diversity of the genetic algorithm can be ensured, and the population is prevented from being converged on individual minority solutions; in the first-layer genetic algorithm, a global optimal point can be quickly found by taking a larger value of the crossover probability in the earlier stage of iteration; the crossover probability is small in the later stage of iteration, and the situation that convergence cannot be achieved due to the fact that the structure of the found optimal point is damaged can be avoided; the variation probability in the first-layer genetic algorithm adopts a variable probability method, so that the genetic algorithm can be prevented from falling into a local optimal point; the second-layer genetic algorithm selects an individual with the highest fitness and directly carries out decoding operation, crossover and mutation operation is not needed, and it can be guaranteed that the genetic algorithm converges to an extreme point as soon as possible.

Description

technical field [0001] The invention relates to the technical field of automatic control, in particular to a neural network prediction method based on boiler combustion characteristics. Background technique [0002] In order to reduce NO x The low-oxygen combustion method adopted for the emissions, although it can effectively suppress NO x However, it will also cause a sharp increase in CO concentration and fly ash content, and at the same time reduce the combustion efficiency of the boiler; when increasing the oxygen content of the flue gas, although the boiler operating efficiency and furnace temperature are improved, it will also cause NO x At the same time, due to the increase of air volume, the heat loss of exhaust smoke will also increase. Theoretically, there is an optimal point that can balance the contradiction between the two. At present, the BP neural network is mostly used to establish the boiler combustion prediction model, and the output of the BP neural ne...

AI Technical Summary

Problems solved by technology

However, due to the use of a genetic algorithm for optimization in the above method, there is a problem that the initial population range is too large, resulting in a long time for the convergence of the genetic algorithm.

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