Research of the control strategy of burning system of industrial oil-poor boiler based on self-learning fuzzy neural network

The primary fuel of an oil-poor boiler is residuum oil or drop-off oil. Based on the discrete parameter of thermal technology the burning process is influenced by factors such as quality, pressure, temperature of oil and fluctuation of load, etc. The burning system of an oil-poor boiler is a complex controlled structure featuring nonlinear, multivariable, large detention, and strong disturbance. An accurate mathematical model is difficult to construct and the required control effect is hard to achieve with the routine control strategy. In this paper, a self-learning fuzzy neural network control strategy for the burning system of an oil-poor boiler is presented, and the steam pressure, steam load, oxide content and structured MIMO system fabric model are considered in detail. Experiment result obtained shows that this burning system control strategy is robust with improved stability.