Estimation of the Particle Size Distribution of a Latex using a General Regression Neural Network

Abstract This paper presents a neural-based model for estimating the particle size distribution (PSD) of a polymer latex, which is an important physical characteristic that determines some end-use properties of the material (e.g., when it is used as an adhesive, a coating, or an ink). The PSD of a dilute latex is estimated from combined DLS (dynamic light scattering) and ELS (elastic light scattering) measurements, taken at several angles. To this effect, a neural network approach is used as a tool for solving the involved inverse problem. The method utilizes a general regression neural network (GRNN), which is able to estimate the PSD on the basis of both the average intensity of the scattered light in the ELS experiments, and the average diameters calculated from the DLS measurements. The GRNN was trained with a large set of measurements simulated from typical asymmetric PSDs, represented by unimodal normal-logarithmic distributions of variable geometric mean diameters and variances. The proposed approach was successfully evaluated on the basis of both simulated and experimental examples.