The Method Of Combustion Optimization Control Based On Fuzzy Association Rules

With the rapid economic development, energy saving and effective using has become a top priority. The electric power enterprise is a large family of energy consumption, thus it has great significance to improve the unit of the economy and reduce the cost and adopt real-time monitoring unit and optimize the production process. According to the characteristic of fossil power plants, this thesis focus on the key issues about modeling, optimization and control of boiler combustion systems. The simulation results proved that the research can implement the synthesis optimization control of boiler combustion, improve the running level of boiler instruments to optimum and make it efficient and cleaner. The main work and contributions of the thesis are as follows:(1)The steady state model of the boiler combustion system is established using neural network. The simulation results show that this method has high modeling precision about boiler efficiency and NO_x emission.(2)Based on the neural network model, a genetic algorithm is used to find the the optimal typical combustion conditions, and the initial expert rule base is established.(3)The method of combustion optimization control based on fuzzy association rules is proposed. The simulation results show that the method has the same optimization results and faster optimization speed, and solves the problem of poor real-time performance of combustion optimization..