Use of neural networks in the analysis of particle size distributions by laser diffraction

The use of techniques based on laser diffraction for measuring particle size distributions has become increasingly popular in recent years. However, the models used to compute the results from light scattering data still do not enable the accurate application of such techniques in highly concentrated suspensions of particles in fluids, as well as in cases of non-spherical particles. In this paper a neural network model is applied to light scattering data obtained with particles in liquid suspensions at different solid concentrations and compared with a conventional algorithm based on Fraunhofer diffraction. The results show that neural network models can be successfully used to compute particle size distributions from laser diffraction measurements and that this approach may expand the range of application of such techniques.