Research On Cement Grinding Load Control Strategy And Software Design

Cement grinding is one of the three major steps in cement production.In the cement grinding production process,the ball mill is an important equipment,and the running condition of the ball mill directly affects the quality of cement grinding;Therefore,the control of ball mill load is the core control problem in the production control of cement grinding system.At the same time,it is of great significance to adopt effective load control strategy in the load control of cement mill.However,the ball mill system has nonlinear,strong coupling and other characteristics,which increase the difficulty of cement mill load control.At present,the domestic cement grinding system mainly adopts the method of manually adjusting the production parameters to control the cement grinding load.However,the control precision of the cement grinding load is difficult to guarantee in this way,and a large amount of energy consumption is generated in the production process.In order to achieve stable production,energy saving and consumption reduction,this paper combines the actual situation of cement grinding production site,analyzes and studies the convolutional neural network algorithm,particle swarm optimization algorithm and fuzzy control theory,and establishes cement grinding based on convolutional neural network.The consumption prediction model and the particle swarm optimization algorithm based on the particle swarm optimization algorithm are designed,and the fuzzy controller based on BFCM algorithm is designed to realize the control of cement grinding load and the design and implementation of the cement grinding load control software system.The main research contents of this paper are as follows:Firstly,the in-depth analysis of the cement mill system process,the control mechanism of cement mill load control,the control difficulties and the factors affecting the cement grinding load and the characterization parameters of the cement grinding load;according to the analysis results combined with the convolutional neural network algorithm,A cement grinding power consumption prediction model suitable for on-site working conditions was established to predict the power consumption of cement mill.At the same time,based on the established power consumption prediction model,a cement particle load operation index decision algorithm model based on particle swarm optimization was designed and implemented.Optimized solution for cement mill load control objectives.Secondly,the fuzzy control theory is studied.Based on the fuzzy clustering algorithm(BFCM algorithm)with trust degree,the rule extraction method which can extract the fuzzy data from the cement mill production site data is designed.Obtained fuzzy rules between cement grinding load and control parameters;at the same time,based on the extracted fuzzy rules,the design of the cement grinding load fuzzy controller is carried out,and the fuzzy control of the cement grinding load is realized.Finally,according to the above established model and related theoretical analysis,the cement grinding load control software system is designed by using C# programming language and SQL Server 2014 database;The control experiment was carried out in the field by software system.The effectiveness of the control strategy was verified by experimental data analysis.