Study On Hybrid Model Of BBD Ball Mill Based On Neural Network

The milling system is the most important part of the whole coal. fired power plant. Ball mill as the milling system of power plant, whether the operation of the ball mill is good will not only directly impact on the safety, economic and stability of the operation of the boiler, but also directly relate to the safety, economic and stability of the operation for the entire unit of the plant. With the Power of the increasing demands of energy saving, a BBD ball mill has low energy consumption, high efficiency, wide range of grinding coal, etc. The BBD ball mills is commonly used in thermal power plant milling system BBD ball mills equipment, safety, economic operation on the premise that their effective control, while the BBD ball mill must have a valid basis to do high. Precision models, the establishment of the precise parameter model of BBD ball mill system is to be used to solve the problem.In this paper, high accuracy is difficult to establish precise parameter BBD ball mill, first introduced the basic structure of the mill, working principle and operating characteristics, operating parameters under the curve and influence of its factors were analyzed.BBD ball mill is a nonlinear system of strong coupling, large time delay and multi input output. Its features of the work will change with the change of operating conditions. In this paper, a hybrid modeling method is presented to establish mechanism and Mill network models and to obtain the hybrid model through the weighted fusion of these two models. Under neural network model, the location of optimal Radial Basis Function (RBF) neural network hidden layer centre is determined using Immune Clustering and the accuracy of the model is improved by implementing Particle Swarm Optimization (PSO) to optimize weight of the neural network. The proposed method achieves excellent effect of global modeling and takes System's operating characteristics into account at the same time.Finally, using the above hybrid model based on neural networks and Mechanism models study on the historical data at Fuxin BBD.4360 ball mill. Simulation results show that the hybrid model precisely reflects the relationship between input and output parameters of this Double Ball Mill milling process, and has high accuracy. Can be available basis for well optimizing control algorithms.