| Coal mill is an important auxiliary equipment of thermal power plant.The operation status of the units directly affects the stability,economy and environment of the units,and is also of great significance for ensuring the safety of power production,reducing the coal consumption cost and increasing the power generation efficiency.At present,most of the domestic large-scale thermal power units use the medium speed coal mill system,which belongs to a multivariable system.In the actual operation of the thermal power plant,in order to simplify the difficulty of control,it is often designed for each loop separately controller.However,the coupling between variables in a multivariable system tends to be very serious,and the method of designing the controller alone tends to have poor control quality.In order to improve the control quality of the pulverizing system and improve the economic efficiency of the unit,the characteristics of the medium-speed pulverizer need to be analyzed in depth from the mechanism,and the mechanism model of the full-speed pulverizer is established,and the optimal operation and control of the variable system is realized.In this paper,MPS medium-speed coal mill are analyzed.Based on the mass balance theorem and the energy balance theorem,the actual working process of the medium speed coal mill is analyzed in depth.At the same time,the influence of the variation of the raw coal moisture on the system model is also considered and the dynamic model of-the MPS medium speed coal mill is established.Based on the historical operation data collected from the SIS system,the parameters of the model are obtained by stepwise identification using genetic algorithm.Comparing the model output with the field data,we can find that the error between the two is within the allowable range,which verifies that the established MPS medium-speed coal mill model has higher accuracy and can better characterize the actual object.Based on the state space equation of the established coal mill model,the generalized predictive control(GPC)expression is deduced and the controller is designed under each typical operating condition of the coal mill.A multi-model predictive control scheme is proposed,which solves the problem of more serious coupling among the variables of the milling system and improves the control quality of the system.In order to further realize the energy saving optimization control of the coal mill system,an optimization scheme of the setting of outlet temperature of coal mill is put forward,and an algorithm for disturbance rejection of pulverizing system is designed.Based on the dynamic mathematical model of coal mill,pulverized coal moisture is used as a feed-forward signal,and a feed-forward compensation controller is designed to optimize the outlet temperature of coal mill.The simulation results show that the designed scheme can reduce the latent heat of combustion of pulverized coal in the furnace and ensure the economical combustion of the furnace under the premise of ensuring the safe operation of the coal mill system.Considering the predicted output and historical output of pulverizing system and combining the idea of PID control,an improved multi model predictive control algorithm named GPC-PID is proposed which introduces the proportion,integral and differential effect into the original objective function of predictive control algorithm.The simulation results show that the improved control algorithm can show better dynamic adjustment performance when overcoming the raw coal moisture disturbance and coping with the large-scale changing of the pulverizing system,and the regulation of the controlled variables is more stable,which is of great significance for the fine control of the output of the pulverizing system. |
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