Neural Network Prediction Method Based on Boiler Combustion Characteristics

A boiler combustion and neural network technology, which is applied in the field of neural network prediction based on boiler combustion characteristics, can solve the problems of long convergence time of genetic algorithm and too large initial population range, and overcomes the defects of initial connection weight and threshold of the network, guarantees Group diversity, the effect of increasing the learning rate

Active Publication Date: 2022-06-21
武汉博赛环保能源科技有限公司
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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.

Method used

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  • Neural Network Prediction Method Based on Boiler Combustion Characteristics
  • Neural Network Prediction Method Based on Boiler Combustion Characteristics

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Experimental program
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Effect test

Embodiment 1

[0039] At present, the BP neural network is mostly used to establish the boiler combustion prediction model, and the output of the BP neural network is comprehensively optimized based on the genetic algorithm to find the best boiler efficiency setting value and the best NO. x Emissions set point, by adjusting the input of the BP neural network, so that the boiler efficiency and NO x Emissions are optimized. 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, which leads to a long convergence time of the genetic algorithm. Therefore, in order to solve the above problem, such as figure 1 and figure 2 As shown, this embodiment provides a neural network prediction method based on boiler combustion characteristics, which includes the following steps:

[0040] S1. Take the gas volume and flue gas oxygen content as the input of the BP neural network, and take the boiler efficiency ...

Embodiment 2

[0062] Since the BP neural network algorithm is a method of adjusting the connection weights and thresholds based on gradient descent, the initial connection weights and thresholds of the network structure are randomly set before training. Once the initial parameter settings are unreasonable, it will easily fall into among the smallest local defects. Genetic Algorithm (GA) has the ability of global search and is not easily restricted by local optimum in the search process. Combining the large-scale nonlinear mapping ability of the BP neural network algorithm with the global optimization characteristics of the GA algorithm can overcome the defect of the traditional BP neural network algorithm that randomly generates the initial connection weight and threshold of the network, and improve the learning rate of the BP neural network algorithm. and linear approximation capabilities. Therefore, on the basis of Embodiment 1, this embodiment provides an initial connection weight and t...

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Abstract

The present invention proposes a neural network prediction method based on boiler combustion characteristics, establishes a boiler combustion prediction model based on a two-layer genetic algorithm and BP neural network, retains group elites through the first layer genetic algorithm, and can ensure convergence. The second layer genetic algorithm adds The new population is added to the elite group retained by the first-level genetic algorithm, which can ensure the group diversity of the genetic algorithm and prevent the group from converging on individual minority solutions; Quickly find the global optimal point; take a small value for the crossover probability in the later stage of the iteration, which can avoid destroying the structure of the optimal point that has been found and cause the failure to converge; the mutation probability in the first layer of genetic algorithm adopts the variable probability method to avoid genetic algorithm Trapped in a local optimum; the second-level genetic algorithm selects the individual with the highest fitness and directly performs decoding operations without crossover and mutation operations, which can ensure that the genetic algorithm converges to the 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] To reduce NO x The low-oxygen combustion method used in the emission of x However, it will also cause a sharp increase in CO concentration and fly ash content, and also reduce the combustion efficiency of the boiler; when the oxygen content of the flue gas is increased, although the operating efficiency of the boiler and the 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. In theory, 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 network is comprehensively optimized based on the genetic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G06N3/12
CPCG06F30/27G06N3/04G06N3/084G06N3/086G06N3/126
Inventor 李超邱丹姚睿龙格姝婷刘炘坤廖先刘莉杨勇徐建局
Owner 武汉博赛环保能源科技有限公司
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