Optimization And Control Of Waste Printed Circuit Boards Grinding And Classification System

Waste Printed circuit boards (PCBs) are recognized as an important part of waste electronic products, it contains a lot of valuable metals, such as copper, aluminum, iron, nickel, lead and tin, zinc, gold, silver, palladium, rhodium and others,so it has important recovery value. At the same time, it also contains heavy metals, organic and other toxic or harmful substance, the treatment process contains environmental risk. Therefore, the recycle and disposal of waste PCBs have momentous significance from the perspective of resources and environment. Chemical and mechanical methods are two traditional recycling processes for waste PCBs. The whole technology of waste PCBs physical recycling industry line contains four parts: multiple scarping, material screening, multiple-roll corona electrostatic separator, and dust precipitation. Although many problems in the industrialization of technology were solved, there are still some problems blocking the integration of different technologies. If the grinding and classification cyclic system is not well controlled, the whole process will face problems such as metals and nonmetals insufficient dissociation, particles over-pulverizing, incomplete comminuting, material plugging and equipment fever.Therefore,this paper studies the kinetics characteristics of waste PCBs impact crushing, and material parameters on crushing effect was analyzed with experiment. According to the actual conditions of production line, crushing and classification cyclic model and neural network size prediction model were established, it provides a new method for optimization and control of the industrial application of waste PCBs production line.With crushing matrix method, a grinding and classification matrix model for waste PCBs automatic production line was built. By contrast with actual production line, the validity of the model was verified. With software of MATLAB,the model was applied for steady-state analysis of the production line, it was found that only 3 cycles are needed for the equilibrium of the production line, which indicated that the production line have a strong adaptability. The established model was applied for analysis according to different weight of feeding material, material flow in each part presented a linear relationship with the feeding material. The model provides a good foundation for automatic process control of the production line.According to waste PCBs crushing and classification granularity controlling,analysis the principle of neural network theory,and BP neural network is choose as modeling type, MATLAB is used to design and train the BP neural network, and the convergence of the network is proved. The result of simulation error analysis shows that the neural network model can simulate the practical production process, which laid a foundation for the controller design.According to crushed PCBs particles, different distribution model are applied to analysis distribution characteristics, it is found that R-R distribution can more accurately describe the waste PCBs impact crushing regularity distribution, it illustrate that the model can be applied to predict the impact crushing waste PCBs distribution characteristics. According to the crushing process and particle size distribution, first level crushing dynamic model with high accuracy is established, which has certain directive significance to waste PCBs crushing production process. Factors influence the PCBs crushing performance were analyzed, and got the main influence factors. The feeding material weight and particle size on the impact crushing characteristics were experimentally analyzed, and got c, m value change rule of R-R model which describe the waste PCBs particles impact crushing distribution.