Boiler Combustion Optimization Based on Bayesian Neural Network and Genetic Algorithm

Authors

Haiquan Fang, Beijing Institute of Information Control, Beijing 100048, China;
Huifeng Xue, Beijing Institute of Information Control, Beijing 100048, China;
Li Ning, Beijing Institute of Information Control, Beijing 100048, China;
Fei Xi, Beijing Institute of Information Control, Beijing 100048, China;

Abstract

Abstract: Neural network and genetic algorithm have been extensively used in boiler combustion optimization problems. But the traditional Back Propagation neural network's generalization ability is poor. The Bayesian regularization can improve the neural network's generalization ability. A boiler combustion multi-objective optimization method combining Bayesian regularization BP neural network and genetic algorithm （Bayes NN-GA）was researched. A number of field test data from a boiler was used to simulate the Bayesian neural network model. The results show that the thermal efficiency and NOx emissions predicted by the Bayesian neural network model show good agreement with the measured, and the optimal results show that hybrid algorithm by combining Bayesian regularization BP neural network and genetic algorithm can be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion, which provide a theory guidance for the boiler operation in an economic and environmental way.

Recommended Citation

Fang, Haiquan; Xue, Huifeng; Ning, Li; and Xi, Fei (2020) "Boiler Combustion Optimization Based on Bayesian Neural Network and Genetic Algorithm," Journal of System Simulation: Vol. 27: Iss. 8, Article 20.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss8/20

First Page

1790

Revised Date

2015-07-06

DOI Link

https://doi.org/

Last Page

1795

CLC

TP183;TP301.6

Recommended Citation

Fang Haiquan, Xue Huifeng, Li Ning, Fei Xi. Boiler Combustion Optimization Based on Bayesian Neural Network and Genetic Algorithm[J]. Journal of System Simulation, 2015, 27(8): 1790-1795.