Impulse-Cyclone Airflow Drying Process For Wood Fiber And Optimization Of Impulse Tube Transition Angle

The purpose of this study is to study the establishment of a mathematical prediction model of impulse-cyclone air-drying and optimization of the impulse tube transition angle of a drying device.The RSM was used to design an experimental scheme to examine the influence of main process parameters on the final moisture content during the impulse-cyclone airflow drying process of poplar fiber.BP neural network prediction model was built with the help of the Python programming language,and the mapping of the two models was completed.The relationship is compared;the transition angle of the impulse tube of the impulse-cyclone drying device is optimized by Fluent,and the internal flow and heat and mass transfer laws of the device during the drying process are accurately grasped while the impulse tube transition in the impulse-cyclone drying device is completed.The comparison results of BP neural network and RSM provide new ideas for the determination of the mapping relationship between process parameters and final moisture content in the Impulse-Cyclone drying process of wood fiber.The final and final moisture content of wood fiber is predicted intelligently.The simulation and optimization of the transition angle of the tube can help related production companies to improve the drying efficiency of materials,reduce the risk of equipment damage,and bring invisible economic benefits to the enterprises producing drying equipment.The research results are aimed at enriching the theoretical system of wood fiber air drying and its Industrial application provides technical support and theoretical basis.The main research contents and conclusions are as follows:(1)By optimizing the response surface of the impulse-cyclone air drying process,Design Expert software was used to analyze the relationship between the parameters of the impulse-cyclone air drying process and the final moisture content of the poplar fiber,and a multiple regression model analysis of variance was obtained.The results show that there is a highly significant relationship between the process factors of the regression model and the final moisture content of the poplar fiber.The error between the predicted value of the model and the actual value of the test is small.The model has good adaptability and can be well used to explore the effects of impulse-cyclone drying process on the final moisture content of wood fibers.Three of the four influencing factors in the model are significant,and the order of significance is:initial moisture content>inlet air temperature>feed speed>inlet air speed,and the quadratic regression equation of the response surface is obtained by software fitting.(2)Through the neural network modeling of the impulse-cyclone airflow drying process,the TensorFlow framework is used to construct the BP neural network with the help of the Python programming language,and the initial moisture content of the poplar fiber in the impulse-cyclone airflow drying process and the drying device are developed.Mathematical prediction model of the mapping relationship between the four process parameters of wind temperature,inlet speed and feed speed and final moisture content.A total of 29 groups of sample data were used in the RSM test.21 groups were selected as training data and 8 groups were used as test data to construct the first neural network.Expand the sample data to 92 groups,and build a neural network model again through training.The fitted effect diagram is obtained.The two neural network iteration results clarify that the sample data capacity is expanded.After sufficient sample data reflects the regular characteristics,the optimization effect of the model is predicted.Be improved.Comparing the optimization effect of RSM and BP neural network model,the root mean square error of RSM is lower than that of neural network model,and R2 value is higher than that of neural network model,which indicates that the optimization level of RSM is better when the amount of sample data is limited.(3)Using Fluent can analysis the flow situation of the drying-device’s gas-solid two-phase flow.The experiment was performed by changing the diameter of the impulse tube in the air flow part of the drying device and comparing the 21° and 30° transition angle drying devices.Internal velocity field,pressure field and wall shear conditions,analyze and compare the distribution of the flow field state during the drying process,and accurately grasp the operating changes in the device during the drying process,completing the impulse tube of the impulse-cyclone air dry ing device exploration of transition angle optimization.The simulation results show that there is no high-speed flow zone at the junction of the impulse tube and the narrow straight tube of the 30° transition angle dryer,and the overall velocity field of the impulse tube is evenly distributed,which is conducive to the smooth movement of the drying particles.The wall surface shear pressure generated by the 30° transition angle drying device is an order of magnitude lower than the original drying device,which is conducive to reducing erosion inside the pipeline and greatly reducing the risk of equipment damage;The pressure field distribution inside the angle drying device.The design of the 30° transition angle enables a small range of turbulent air flow to form near the inner wall of the transition section of the pulse tube,resulting in the cost of energy consumption,leading to an increase in the unnecessary energy consumption in the drying process.The turbulent airflow generated by the 30°transition angle drying device is within the acceptance range.Based on all simulation results,the overall drying effect of the 30° transition angle drying device is better.