Aiming at limitation of routine temperature measurement mode applied to microreactor and requirements of accurate measurement of temperature

a temperature measurement method based on back propagation neural network（ BPNN） is put forward by applying difference of the predicted values and measured values of target temperature to process and eliminate effect of additional temperature caused by white noises. The method is studied for temperature measurement of steady condition

unsteady condition and temperature distributions in micoreactor respectively. The results show that the predicted value and measurement value fitness is high

difference is controlled within ± 0. 05

and the wave range is ± 0. 01 ℃ at 25 ℃

± 0. 02 ℃ at 40 ℃under steady condition

far superior to fluctuation range of ± 0. 05 ℃ and ± 0. 2 ℃ of target

temperature measurement value

under unsteady condition

catalase reaction

the concentration of hydrogen peroxide which can distinguish temperature signal peak reduce from 0. 01% to 0. 001%

temperature measurement level has an improvement of about one order of magnitude. Radial dynamic temperature distribution verify that with high resolution of temperature can accurately reflect heat and mass transfer properties in microreactor. The method can improve the signal-to-noise ratio

resolution and precision of temperature measurement in microreactor

meanwhile because it lower requirements of heat insulation and reduce the cost

it has application value.