Reserch On Coal-Fired Power Plants Boiler Combustion Optimization

Facing the fierce competition environment of the power market and the increasingly stringent environment protection.policy, How to improve the operating efficiency of the boiler and reduce NOx emissions,thus achieving economic and environmental operation of the boiler as well as enhancing their competiveness is a practical prblem which needs to solve.In this thesis, the optimization of boiler combusition is studied. The main contents and results are as follows:The problems exciting in the conventional boiler efficiency caculation method are analyzed and the caculation method of nominal boiler efficiency is proposed which only takes the heat loss of the exit gas, carbon loss and chemical incomplete combustion loss into consideration. Meanwhile this method replaces the outlet flue gas parameters of air preheater with the outlet flue gas parameters of the economizer and calculates the heat losses. By this means, we exclude the influences of non combustion operation parameters such as desulfurization, denitrification and air leakage of air preheater on the boiler efficiency and combusition optimization. This method uses the average air temperature, average water temperature and average coal parameters of all conditions as reference values.In this way, the efficiency of the boiler correction is more reasonable. By an example,the rationality of nominal boiler efficiency is verified.The combustion adjustment test conditions are designed for a 300MW unit boiler in some power plant. According to the data of the test, the main factors affecting the boiler efficiency, NOx emission and carbon content of ashes and fume discharge temperature of the boiler are analyzed. The optimized combustion mode under two specific loads is recommended. And it provides a basis for the reliability analysis of the combustion optimization based on the model.Based on intelligent computing, we set a reserch on combustion optimization. According to the boiler combustion adjustment test data and historical data of the boiler, we establish combustion optimization model by RBF neural network. This model uses nominal boiler efficiency as the model output which avoids the effects of non combustion network variables effectively. We aim the optimization target at improving the boiler efficiency and reducing NOx emissions. Based on the combustion optimization neural network model, we use genetic algorithm to optimize the adjustable combustion operating parameters of the boiler. The optimization results are compared with the experimental results. The comparison result shows that the model optimization is reasonable. The performance evaluation test shows that the combustion efficiency of the boiler is improved obviously under the condition that the NOx emission is kept constant which demonstrates that the method is effective.