Particle characterization using optical measurements and neural networks

Abstract

We present a novel optical particle sensor technique using artificial neural networks. This method relies on observations of light scattering and extinction by particles as input features to a trained neural network, which provides relevant particle distribution and representative shape for an integrated particle mass flow estimation. The models are trained on artificial data, generated for particles that the sensor is likely to encounter. The feasibility of our method is demonstrated through an experimental measurement of solid sand particles injected into a high-speed wind tunnel. The results show accurate estimations of the injected sand mass flow and particle size statistics, with a sand mass flow root-mean-square error of 0.28 g min−1 or 4.1 from the monitored rate using a precision scale. This measurement framework paves the way for sensor applications in harsh operating environments with limited optical access.