MEMS Thermal Mass Flow Sensor Response Time Optimization

Abstract

To solve the problem of long dynamic response time of MEMS thermal mass flow sensor in real production process, this article investigates the heat transfer process between the heater and the gas during the flow rate changes. A prediction model based on the time constant is proposed to predict the steady-state mass flow rate after a flow rate change in advance. For the prediction accuracy of the model, this article analyses the influence mechanism of the gas temperature change of the MEMS thermal mass flow sensor. The functional relationship between the fitting flow rate, gas temperature and actual flow rate is constructed using a binary regression equation, which eliminates the measurement error caused by gas temperature change. Meanwhile, the improved Kalman filter algorithm is utilized to process the raw measurement data of the MEMS thermal sensor in real time to eliminate the noise error caused by the input signal and the external circuit. Finally, the experimental results show that when the mass flow rate varies from 0 to 100 kg/h, the response time is reduced by 50%–70%, and the prediction accuracy of the model reaches more than 98.5%.