Optimization Control And Performance Evaluation Method For Ball Mill Pulverizing System

The ball mill pulverizing system is the core equipment with the characteristic of strong nonlinear and multivariable coupling for small and medium-sized thermal power plants. Based on the conventional control strategy of accurate linear model is hard to obtain satisfactory control effect on the object. In view of the above problems in this paper, we study new optimization control strategy and performance evaluation methods, in order to improve the automatic control level and economic benefits of thermal power plant.Firstly, this paper mainly introduces the working principle and working process of the ball mill coal pulverizing system, the work characteristic curve of the ball mill carried on the thorough analysis,discussed when the step disturbance happens, the influence of the key parameters of hot blast rate system, cold blast rate system and coal feed on system output.On this basis, based on bacterial foraging optimization algorithm of adaptive decoupling control has been proposed. Bacterial foraging algorithm has the advantage of strong ability of swarm intelligence search and easy to jump out of local minimal,combining the single neuron adaptive decoupling control with the ball mill pulverizing system as the object for the adaptive decoupling design and simulation prove that the optimization algorithm can overcome nonlinear and time delay of the system in a wide rang, it has stronger robustness and adaptability. According to some problems of strong coupling between control variables of the ball mill and imprecise mathematical model, we have used the fictitious reference iterative tuning method to optimize the repetitive control design. Through a closed-loop response direct experiment of directly optimization of controller parameters by data and under the controlled object model and unknown periodic signal characteristics, it indicated that performance is improved compared with the traditional controller. Against the factor of disturbance between controller and time delay that making the controller performance degradation, the ball mill pulverizing system controller were analyzed by the minimum variance control method. Finally, the performance assessment of detrended fluctuation analysis has proved the feasibility and the effectiveness of these three kinds of advanced control methods. In this paper, main achievements is that ball mill pulverizing system optimization control and performance evaluation of technology, the results of research in this area have a certain practical significance and reference value.