Study On Dust Concentration Control Method Of High Concentration Instrument Verification Device

In order to monitor dust concentration in environment,it is necessary to ensure the measuring accuracy of dust sensor.However,the dust sensor in the dust environment is easy to be polluted,the accuracy of the problem is reduced,so it needs to be regularly metrological verification.At present,the research on high concentration dust instrument verification device at home and abroad is not perfect,the existing high concentration dust instrument verification device has the following problems:1.How to select the detection area to ensure the most uniform distribution of dust concentration;2.How to improve the accuracy of dust concentration detection in the detection area;3.How to quickly control the dust concentration in the detection area to meet the requirements of dust sensor measurement and verification in different ranges.In view of the above problems,the main research content of this thesis is as follows:(1)Aiming at how to select the detection area to ensure the most uniform distribution of dust concentration,Fluent fluid simulation was introduced into the field of high-concentration dust instrument verification device in this thesis to study the migration law of dust particles in the device,set the dust concentration detection surface at different positions from the dust source,and compare the average error and average percentage error of dust concentration distribution.Finally,it is determined that under the wind tunnel model in this thesis,when the wind speed is 2m/s,the dust concentration distribution on the detection surface 5m away from the emission source is the most uniform.(2)Aiming at how to improve the accuracy of dust concentration detection in the detection area,this thesis chooses to use the Kalman filter algorithm for data optimization.Due to the nonlinear characteristics of dust concentration data,a dust concentration data optimization method based on the fusion of extended Kalman filter and weighted average is proposed in this thesis.Based on the existing extended Kalman filter algorithm,sensor array is used to introduce weighted average algorithm to improve the dust concentration detection accuracy.Experimental results show that the mean absolute error and root mean square error of dust concentration data optimization method proposed in this thesis are 1.648mg/m~3(32.8%)and 1.938mg/m~3(33.2%)lower than that of traditional extended Kalman filter algorithm.In addition,dust concentration detection algorithm also provides a reference for verifying the accuracy of dust concentration prediction algorithm.(3)In order to meet the measurement and verification requirements of dust sensors with different ranges,the parameters need to be adjusted to change the dust concentration in the detection area.However,after the adjustment of parameters,the device takes a long time to form a stable detection area.Traditionally,parameters are adjusted according to experience,which greatly increases the burden of measurement work.Aiming at how to quickly control the dust concentration in the detection area,a dust concentration prediction method based on the integrated neural network of long and short term memory network(LSTM)and gated circulation unit(GRU)was proposed in this thesis to predict the dust concentration in the detection area.Under the condition that the mass flow rate and wind speed of the control dust generation remain unchanged respectively,other parameters are adjusted appropriately to predict the dust concentration in the detection area,and the average percentage error and root mean square error of the three algorithms are compared.Experimental results show that the errors of the proposed methods are smaller than those of LSTM and GRU,and the proposed methods are more applicable in the field of dust concentration prediction.(4)Design test model,build high concentration dust instrument verification device,and dust concentration distribution uniformity test verification.