Screening is an important process in mineral industry. In this paper, a study has been made to simulate the screening process based on a high-performance MATLAB/Simulink software, with an example of simulating the sie...Screening is an important process in mineral industry. In this paper, a study has been made to simulate the screening process based on a high-performance MATLAB/Simulink software, with an example of simulating the sieving process of a vibrating screen. A simulation model of the sieving process with a vibrating screen （SMSPVS） was proposed, using correlative mathematical models and Simulink blocks. The results show that the simulation data was very close to the actual data, The minimum errors of size distribution of oversize and undersize are 0.65% and 0.20%, resoectivelv. The sieving orocess can be accurately simulated by the SMSPVS.展开更多
The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established...The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.展开更多
文摘Screening is an important process in mineral industry. In this paper, a study has been made to simulate the screening process based on a high-performance MATLAB/Simulink software, with an example of simulating the sieving process of a vibrating screen. A simulation model of the sieving process with a vibrating screen （SMSPVS） was proposed, using correlative mathematical models and Simulink blocks. The results show that the simulation data was very close to the actual data, The minimum errors of size distribution of oversize and undersize are 0.65% and 0.20%, resoectivelv. The sieving orocess can be accurately simulated by the SMSPVS.
基金supported by the National Natural Science Foundation of China(grant No.52375247)Natural Science Foundation of Jiangsu Province(grant No.BK20201421)+3 种基金Graduate Research and Innovation Projects of Jiangsu Province(grant No.KYCX21-3380)Jiangsu Agricultural Science and Technology Independent Innovation Fund(grant No.CX(22)3090)Taizhou Science and Technology Project(grant No.TN202101)a Project Funded by the Priority Academic Program Development of Jiangsu Higher。
文摘The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.
