Research On Intelligent Control System Of Belt Conveyor Based On Self-adaptive Speed Regulation

As the most important part of the coal transportation system,the belt conveyor undertakes the main task of mining and coal transportation.With the continuous optimization and upgrading of industrial manufacturing and intelligent control technology,belt conveyors are gradually developing towards the direction of intelligence,information and energy saving.This paper studies the underground belt conveyor of Hemei No.9 Mine,and devises a set of intelligent control system for the belt conveyor based on adaptive speed regulation to solve the prominent problems of low operating efficiency,poor safety performance and serious power loss,which can help to meet the stability and sustainability needs for modern production and construction in mine industry.The main research work is as follows:(1)Starting from the elementary structure and operating principle of the belt conveyor,the factors that influence its power consumption are analyzed.In order to ensure a smooth and impact-free start of the belt conveyor,combined with four common acceleration curves for comparative analysis,an “S” type start-up curve is proposed,which can effectively reduce the load and impact that the equipment receives during the startup process.In view of energy waste and low operating efficiency caused by the mismatch of the belt speed and transport volume,the reasonable matching belt speed and transport volume is obtained by constructing an optimized model,which can meet the design requirements for safe and efficient coal mine production.(2)Most of the belt conveyors operate at a constant speed during transportation,which causes the conveyor to be in a light load or no-load state for a long time.However,the motor is still running at the rated power in this case,causing severe waste of electric energy.In order to cope with this problem,this paper proposes an adaptive speed control strategy based on APSO-T-S fuzzy neural network,which combines fuzzy control with artificial neural network.Meanwhile,the strategy uses adaptive particle swarm optimization to optimize the fuzzy neural network,which improves the optimization accuracy and efficiency,and reduces the time required for iterative optimization of the algorithm.The simulation results show that this strategy can achieve the goal of high efficiency and energy saving.(3)To analyze the causes of the unbalanced driving power of the multi-motor in the belt conveyor,a control method based on the current average value as the reference for power adjustment is proposed.This strategy combines the difference between the motor current and the average value,thereby achieving a reasonable distribution of output power.The simulation results show that this strategy has significant effect of energy-saving optimization.(4)Based on the transport status and control requirements of Hemei No.9 Mine,intelligently reform the hardware and software systems of the mine belt conveyor.The hardware system takes PLC as the control core to obtain the functions of centralized control,safety protection,video monitoring for the belt conveyor.The software system adopts i FIX configuration software to obtain the functions of data collection,information processing and malfunction alarm for the belt conveyor.The on-site joint test structure demonstrates that this system can effectively improve the Intelligent management level of underground belt conveyors.This thesis has 46 figures,14 tables and 82 references.