Study on neural network algorithm for detecting respirable dust in photoacoustic cavity

The traditional photoacoustic cavity has the advantages of simple structure, low cost, and easy integration with optical cavity technology, so it has significant advantages in the measurement of the optical characteristics of respirable dust. In order to meet the demand of high-precision respirable dust measurements in practical applications, it is necessary to improve the measurement accuracy of respirable dust by traditional photoacoustic spectroscopy technology. Therefore, the structure size of the photoacoustic cavity was determined by theoretical and simulation analysis. A system for measuring respirable dust by photoacoustic spectroscopy was designed, which was applied to the atmospheric respirable dust detection simultaneously with the cavity ring-down spectroscopy system. The results showed that the correlation between the two systems was poor. Therefore, the three-layer back propagation neural network algorithm was used to correct the photoacoustic response values, and the measured value of the cavity ring-down spectroscopy system was used as the reference truth value. The calibration results showed that the output value of the neural network model was in good agreement with the reference true value: the slope was above 0.96. The results showed that the neural network algorithm could effectively improve the measurement accuracy of the photoacoustic spectroscopy system to respirable dust, improve the linearity, and reduce the detection error.