| As the main transportation equipment for large-scale bulk materials in industrial fields at home and abroad,belt conveyors are widely used in coal mines,seaports,transportation,petroleum and other fields due to their advantages of high eff-iciency,low maintenance and long distance transportation.With the advent of information technology and the continuous expansion of industrial production scale,conveyor belts play an important role in the transportation system of coal mining enterprises,and the power consumption during transportation is also increasing.Under the call of the country's active reduction of emissions,it is imperative to improve the operating efficiency of mining belt conveyors and thus reduce power consumption and enterprise costs.Conveyors usually operate at a constant belt speed in coal mining enterprises.Due to the randomness of coal mining face and some human factors,the amount of material on the belt conveyor is random,which causes the conveyor to be often half load or even empty.In the case of operation,the belt conveyor often does not reach its optimal operating point,resulting in great waste of resources.This paper studies the basic structural composition,resistance model,power consumption and international static design standards of mine conveyor system from the perspective of predicting the trend of short-term coal volume and adjusting belt speed,and proposes a new type of belt conveyor.The dynamic energy consumption model illustrates the feasibility of adjusting the belt conveyor speed to achieve energy saving.The SVM-based coal volume regression prediction model is proposed to predict the material volume chart on the belt conveyor in a short time,and the fuzzy information granulation is used to further divide the large fluctuation curve into different time series,so that the original curve and the prediction curve degree of relevance has been improved.In the fuzzy control speed regulation model,the software design of the fuzzy controller and the corresponding membership function parameters are set,and the appropriate fuzzy rules are set according to the structural parameters of the experimental obj ect.On the basis of analyzing and researching the specific algorithm model,the design of the overall scheme of the subject and the simulation optimization research are carried out.The simulation results show that the support vector machine regression prediction algorithm can predict the coal volume in a short time.The mean square error MSE is 0.000152563,which indicates that the deviation between the predicted data and the true value is small,and the correlation coefficient R is 99.7848%.It is further shown that there is a high degree of positive correlation between the original curve and the predicted curve,that is,the degree of fit between the two is good.The experimental data show that the short-term prediction of coal flow using support vector machine(SVM)regression prediction is feasible and has a good application prospect.The relationship between the input,output variables and fuzzy rules is displayed in the fuzzy ruled surface observation chart,which can be applied to the mining belt conveyor when the fuzzy rule is reflected.The Simulink block diagram was designed.Through the DSPACE real-time simulation system,the Simulink schematic was converted into program code and the experimental obj ect was seamlessly connected,and the experimental platform of the mine belt conveyor intelligent predictive speed control system was built.Use the Control Desk to design a human-computer interaction interface,monitor the real-time speed and material volume curve,and perform real?time recording and analysis adjustment.The fuzzy speed regulation is carried out for different real-time changes of coal flow.In the experiment,the system works in safe working conditions and the operation is stable,which indicates that the fuzzy control can handle the belt speed problem of the belt conveyor in operation. |
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