Study Of Charging And Motion Characteristics Of Plastics In High Voltage Electrostatic Fields

With the rapid development of plastic industry,the amount of plastic waste increases.Research on recycling of waste and miscellaneous plastic resources can reduce the waste of resources and achieve sustainable development.High voltage electrostatic separation is a kind of green and friendly technology,which is very suitable for the separation of insulating and conductive plastics with large electrical differences.This paper provides guidance for the selection of separation parameters of insulating and conductive plastics in high voltage electrostatic field,studies the influence of plastic charge and electrode structure on high voltage electrostatic field,and determines the optimal electrode structure.The influence of each parameter on the trajectory of plastic motion was studied,and the optimal separation parameters were determined by regression analysis,variance analysis and response surface analysis.Orthogonal optimization of separation process of insulating and conductive plastics was carried out to verify the plastic particle motion trajectory model.At the same time,the prediction model of separation efficiency based on BP neural network is established to further guide the parameter selection.(1)The charging mechanism of plastic particles and the influence of electric separation parameters on charge and electric field were studied,and the charging model of plastic particles was established.It is found that the charge of plastics is mainly field charge,and the charge is proportional to particle radius,voltage and relative dielectric constant.The range of electric field intensity is proportional to corona electrode voltage,corona electrode number and static electrode radius.The electric field intensity on the surface of the ground roller is directly proportional to the corona electrode voltage,the number of corona electrodes and the radius of the static electrode,and inversely proportional to the corona electrode distance and the static electrode distance.Compared with the electrode configuration,the optimal configuration of five corona electrode-single static electrode is determined.(2)The plastic particles were studied in charge,force and motion,and the trajectory model of plastic particles was established.Taking insulating plastic PP and conductive plastic PA as examples,the influence of voltage,rotational speed,corona electrode distance,static electrode distance and other factors on the motion trajectory in the process of electrical separation was investigated.Through factor analysis,it is found that the order of influencing factors on PP throw distance is voltage >rotational speed > static electrode distance > corona electrode distance,and voltage and rotational speed has a significant effect.The order of influencing factors on PP escape angle is rotational speed > voltage >static electrode distance > corona electrode distance,and the rotational speed has a significant effect.The order of influencing factors on PA throw distance is rotational speed > static electrode distance > corona electrode distance > voltage,and rotational speed has a very significant effect.The order of influencing factors on PA escape angle is rotational speed > static electrode distance > corona electrode distance > voltage,and rotational speed has a very significant influence.The optimal separation parameters were determined by response surface method,which provided feasibility analysis for the separation of insulating and conductive plastics and guided the selection of separation parameters.(3)The separation process of insulating PP and conductive PA,insulating ABS and conductive POM,insulating PC and conductive PPO mixed plastics was studied.The single factor and orthogonal optimization experiments were carried out respectively to determine the influence of each factor on the separation effect.Under the optimal separation parameters of three groups of mixed plastics,the trajectory model of plastic particles was verified.At the same time,based on the data set,a prediction model of plastic separation efficiency with 10×10×1 layer BP neural network was established,and the model hit ratio was 95.8 %,which further guided the selection of separation parameters.