The crushing process has the characteristics of long process, many equipments and dispersion, manual operation and equipment management difficulty. The control target needs to improve the crushing efficiency and reduce the energy consumption per unit of ore crushing on the basis of ensuring the safe production of equipment and process.

The detection instrument of the crushing process is fully mature. Generally, a large-range radar level gauge is used to detect the material level of the mine bin. The electronic belt scale or the nuclear scale is used to detect the transport belt mineral amount, and the microwave flow switch is used to detect the blockage of the material discharge port; On this basis, the operation information of the main equipment such as jaw crusher , cone crusher , belt conveyor and dust collector is integrated to realize the interlock control of one-button start-stop and open-stop control and equipment protection, which can optimize the number of operating positions and reduce the labor of workers. the amount.

In the foreign control and optimization of the crushing process, Metso's DNA control system aims to improve the operability and safety of the production process. The cone crusher control system represented by the Metso HP series usually selects the main drive motor power and crushing. The two parameters of the machine discharge port size are used as the controlled variables. The size of the discharge port and the feed rate are dynamically adjusted by detecting the amount of ore, pressure, power, oil temperature, and size of the discharge port. The target function is the discharge port. The smallest size and the largest amount of minerals. All control actions of the system are approaching these two goals. A more typical control strategy is to fill the mine. JKMR's MOSHGBAR proposes an adaptive algorithm to adjust the venting opening to compensate for the wear of the lining and to achieve optimal control of the crusher. Sweden's Erik Hulthén proposed a state machine algorithm to adjust the discharge port in real time based on the equipment load. It has also been reported that GÖTEBORG has developed an algorithm for converting process data into the required set value of the tight side discharge port. The field application results show that the algorithm can automatically provide the set value of the tight side discharge port for the adjustment system. Compared with the fixed value control system, the yield of qualified products can be increased by 3.5%. Dipl.-Ing. of Germany proposed an algorithm for load driving to control the entire fracture circuit. The method rationally adjusts the feeding frequency of the crusher by comprehensively considering the load conditions of the crusher, the screen and the belt in the whole crushing process, and controls the upper limit level of the load in the entire crushing circuit under the current working conditions as much as possible. Doubled production.

On the basis of successful logic control, the domestic research and application of the full feed and constant power control of cone crusher and high pressure roller mill have been carried out in China. The Sandvik CH420 cone has been independently developed based on PLC and touch screen control system. The automatic control system of the crusher is similar to the ASRi control system. The function is similar, and the cost of the transformation is saved by 80%. The intelligent control system of the hydraulic cone crusher with the single chip as the core is developed. The fuzzy parameter self-tuning PID control and improved type are designed. The intelligent controller of the fuzzy Smith predictor effectively reduces the influence of mathematical model mismatch on the system, and achieves the constant power control of the crusher. The fuzzy control of the crusher discharge port based on S7-300PLC and WinCC is developed. The system realizes the automatic adjustment and power maximization of the crusher discharge port, and the system runs stably. The application of the above control algorithm improves the crushing efficiency and reduces the energy consumption per unit ore.

On the basis of the above, the domestic preliminary exploration of the intelligent fault diagnosis of the crushing process is carried out. The diagnostic system is supported by online detection data. The combination of causal reasoning, principal component analysis and machine learning technology can detect potential production abnormalities in time. Or fault, eliminate the existence of abnormality or fault by means of alarm, modification operation or control, or even shutdown processing.

In recent years, domestic scholars have studied the application of fuzzy PID control technology and Smith prediction method in the optimization control of crushing process. Because fuzzy control does not rely on accurate mathematical models, it can overcome the influence of nonlinear factors and has strong robustness. Generally, the combination of fuzzy control and PID control is used. Wu Yuping proposed a fuzzy genetic algorithm based optimization algorithm for the ore mining system. The controller adopts fuzzy adaptive PID control algorithm and uses genetic algorithm. The controller parameters are optimized to improve the online optimization of parameters. Kim Xia any of the fuzzy PID control method based on Neural Network and Smith Predictor the simulation control effect has been significantly improved, and the adaptive control method of combining fuzzy PID, is applied to the ore cone crusher Quantity control system. Yang Lirong introduced the fuzzy control theory into the constant power control of the crusher. The fuzzy control does not depend on the accurate model of the object, and can better overcome the interference effects of the power nonlinearity and time-varying of the main motor of the crusher, dynamic and static performance and resistance. The interference is superior to the traditional manual adjustment control and PID control, and has strong robustness. Xiao Chengyong designed a set of automatic adjustment system for the discharge port of hydraulic cone crusher by fuzzy control method, continuously monitors the actual load and running state inside the crusher, and realizes the automatic adjustment of the discharge port. Based on the fuzzy control theory, it is provided by MATLAB. The simulation tool SIMUl ink optimizes the parameters of the fuzzy controller control rules. Through the comparison of the parameter response effects, the fuzzy control system obtains the fastest step response and obtains the optimized fuzzy control rule table. Li Ailian designed the intelligent controller of fuzzy parameter self-tuning PID control and improved fuzzy Smith predictor, and simulated the constant power control of the crusher. Yue Feng uses a modified Smith predictor to adjust the time parameters of the first-order inertia link by fuzzy control. Finally, the fuzzy self-tuning PID control and the fuzzy adaptive Smith prediction link are combined to form a fuzzy Smith intelligent control scheme. For the cone crusher control system with uncertain large pure lag problem, the Smith predictor is usually introduced on the basis of PID feedback control to improve the control quality of the system, but because the Smith predictor is too dependent on the mathematical model of the controlled object, PID -Smith control is difficult to achieve satisfactory control results. Combining fuzzy control with Smith predictive control can improve the performance of the system, but the steady-state performance of fuzzy control is poor, and the ideal control effect is still not achieved. Gao Hongyan integrates fuzzy control, integral control and Smith predictive control. The fuzzy composite control is composed and applied to the cone crusher control system.

According to the application of artificial neural network method, Liu Da quantified the working performance of the crusher according to the working parameters of the crusher's particle size characteristic curve and the average particle size and particle size variance of the crusher, and established the crusher based on BP neural network. Based on the particle size data of crushed products, a model of crushing process with particle size distribution optimization was established. Based on the profit, a fracture optimization model based on BP neural network and error minimum principle was established and solved by genetic algorithm. The electric vibration of the cone crusher changes with the particle size and dry humidity of the feed, and the characteristics of the actuator are non-linear links with variable time lag. The larger the particle size, the greater the humidity, the electric vibration machine brings The greater the time lag, the general control system design uses the electro-vibration machine as a pure proportional inertia link or is simplified to a pure proportional link, thus making the characteristics of the designed system and the actual system larger and larger. Ren Jinxia proposed a neural-based The fuzzy estimation strategy of network tuning is simulated by using neural network to adjust PID parameters.

Model prediction and other methods, the main process parameters control of high-pressure roller closed-circuit crushing system include stable feed control, treatment volume control and product particle size control. The particle size control of the product is generally achieved by closed-circuit screening process. When the particle size becomes coarser, the amount of return on the sieve increases, that is, the circulation amount increases, which causes the throughput of the high-pressure roller mill to increase, and the power consumption increases. Since the new ore amount does not change, the increased power consumption is also used for the ore. Fineness control of granularity. According to the change trend of the cyclic load obtained by the belt, Zhang Yong studies whether the ore property changes, and decides whether it is necessary to adjust the set pressure of the high-pressure roller mill to make the working parameters of the equipment more reasonable. Dong Gang established a productivity calculation model for the curved cavity cone crusher. Based on the overall balance theory, the stratification of the crusher cavity of the cone crusher was analyzed. The empirical model of the fractal shape of the crushed product was combined with the particle size prediction model to establish a basis. The particle size distribution prediction model of the fragmentation rate of the crushed product and the cone crusher optimization design model based on the quality control prediction mechanism are used for the design of the efficient new cone crusher. Yang Guoliang proposed an artificial intelligence control method based on the brain emotional learning model (BEL), which is a computational model based on the transmission of information between the amygdala and the sacral cortex in the brain, referred to as the AO system model, based on the brain emotional learning model. The new intelligent controller should be in the cone crusher control system, with good response speed and strong robustness.

In addition, in order to achieve the crushing process, the crusher is controlled by the actual current value and the set deviation signal as the input signal of the PI regulator, and the speed of the crusher feeding belt is changed by adjusting the frequency of the frequency converter. In order to change the amount of ore supplied to the crusher, the automatic filling of the crusher is achieved. Chen Xisong provided an automatic control device for the cone crusher to fill the ore and its method, which automatically adjusts the equipment so that the crusher is in the optimal load operation state while overcoming the shortage of manual operation of the workers. According to the crusher machine The height of the cavity level is automatically adjusted to the ore to automate the filling of the ore.

In comparison, the control system of the foreign cone crusher selects the motor power rate and the size of the main drive crusher discharge port as the main control parameters, and detects changes in pressure, power, ore amount, etc. to dynamically adjust the size of the discharge port and give Mine rate. All control actions of the system approach the target with the smallest size of the discharge port and the largest amount of ore.

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