Research On Intelligent Speed Control Technology Of Mining Belt Conveyor

The belt conveyor is the core equipment of the coal mine underground transportation system.At present,most mining belt conveyors operate at the constant speed corresponding to full load in actual production.They are often in the operation state of "a big horse pulling trolley" under light load or no load,which not only causes power consumption but also increases equipment loss.How to realize the efficient and intelligent operation of belt conveyors is one of the key research topics for scientific and technological personnel in the coal industry.Based on the field data and operation status of the main transportation system of Xiangshan mine of Shaanxi coal Hancheng Mining Co.,Ltd.,a profound study on the coal volume prediction method and speed regulation control strategy of the belt conveyor is carried out in this paper,to obtain the real-time coal load,establish the matching relationship between the load and the belt speed,and use the control algorithm to adjust the operation speed to realize the efficient and stable operation of the equipment.Because of the problems of long detection time and limited sensor installation position in conventional coal quantity detection methods,the operation characteristics of the belt conveyor transmission system are studied,and a coal quantity prediction method based on a fully connected neural network under the TensorFlow framework is proposed.The phased load current is used to predict the real-time coal load,and the prediction model is simulated and verified.The results show that the loss function value between the predicted calculated value of the model and the actual carrying capacity is 5.88,and the average relative error is 0.41%,which indicates that the prediction accuracy is high for the measurement level of coal load.Aiming at the problem of too slow speed increase or too large speed reduction in the speed regulation process of belt conveyor,the speed setting strategy is determined by using the real-time auxiliary coal load information of load current change and fuzzy decision reasoning.In addition,to ensure smooth speed regulation,an artificial bee colony optimization PID belt speed control algorithm is designed.The step response and anti disturbance simulation experiments are carried out with overshoot,response time,and rise time as indicators.The results show that compared with the traditional PID control,the rapidity and anti-disturbance performance of the proposed control algorithm are significantly improved,and the overshoot is smaller in the speed regulation control of belt conveyor faster adjustment speed,and better stability.On the basis of the above,the hardware and software design of the belt conveyor speed control system is completed.The monitoring interface of the upper computer is designed based on the KingView development platform.The coal quantity prediction model call and artificial bee colony algorithm optimization are realized in the host computer software,and the speed regulation control strategy is downloaded to the PLC controller for a field test.The test results show that the coal quantity prediction method and speed regulation strategy algorithm proposed in this paper are feasible and effective,and the intelligent control and energy-saving operation of belt conveyors are realized,which has a certain reference significance for the scientific research work in the same industry.