Prediction Method of Dust Concentration in High Concentration Dust Instrument Calibration Device

At present, there is little research on dust concentration prediction of dust instrument verification device, and the research on dust concentration prediction of dust instrument verification device is still in its infancy. It is necessary to further explore and optimize relevant algorithms and applications in the future to meet the needs of dust concentration monitoring and control in the workplace. In the process of dust concentration monitoring, in order to meet the metrological verification requirements of dust sensors with different ranges, it is necessary to adjust the parameters to change the dust concentration in the detection area. In order to solve this problem, a dust concentration prediction method based on long-term and short-term memory network (LSTM) and gated cycle unit (GRU) is proposed to predict the dust concentration in the detection area. Under the condition that the dust mass flow rate and wind speed are constant, other parameters are appropriately adjusted 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. The measurement error of dust concentration prediction method is less than that of LSTM method and GRU method, which shows that this method is more suitable for the field of dust concentration prediction and has better applicability.