Estimating droplet size distribution of emulsions using turbidity measurements: A soft sensor based on artificial neural network

Abstract

Ability to characterise droplet size distribution (DSD) of emulsions in real-time is essential for on-demand production of customised emulsions. In this work, for the first time, we demonstrate a possibility of estimating full DSD of oil in water emulsions from turbidity measurements using a single wavelength light source. We used recently published data of DSD and turbidity measurements of rapeseed oil in water emulsions (oil volume fractions of 0.15, 0.3 and 0.45) produced with vortex based hydrodynamic cavitation device. Measured DSDs are represented by weighted sum of three log-normal distributions. We developed an approach and a methodology based on artificial neural network (ANN) to estimate DSD from a single measurement of turbidity. A mathematical model is developed to simulate measured turbidity profiles using the known DSDs. The validated model was then used to generate simulated data of turbidity and oil volume fraction pairs (105 pairs). This synthetic data was used to train ANN which used turbidity and volume fraction as input and eight parameters of DSD as output. The developed ANN was able to capture the experimentally measured characteristic diameters and DSDs very well for three oil volume fractions and four different number of passes. The presented methodology and results will be useful for developing an in-line soft sensor based on turbidity measurements for real time estimation of full DSDs of emulsions.