Rapid Determination of Emulsion Stability Using Turbidity Measurement Incorporating Artificial Neural Network (ANN): Experimental Validation Using Video/Optical Microscopy and Kinetic Modeling

Determination of emulsion stability has important applications in crude oil production, separation, and transportation. The turbidimetry method offers advantage of rapid determination of stability at a relatively low cost with good accuracy. In this study, the stability of an oil-in-water (O/W) emulsion prepared by dispersing heavy oil particles in the aqueous solution containing poly(vinyl alcohol) (PVA) has been determined using turbidity measurements. The turbidimetry theory of emulsion stability has been validated using experimental data of turbidity at different wavelengths (350-800 nm) and storage times (0-300 min). The artificial neural network (ANN) has been found to give good predictive performance of the turbidity data. The characteristic change in turbidity has been supported using particle size and distribution analyses performed using optical/video microscopy. The results obtained from the turbidimetry correlation show that the emulsion destabilization rate constant (kappa', min(-1)) is in the range of 0.01-0.04 min(-1) (at wavelengths between 350 and 800 nm, respectively). The rate constant remains unchanged (kappa' = 0.02 min(-1)) between the wavelength of 375 and 650 nm. In addition, the demulsification rate constant (kappa' = 0.015 min(-1)) obtained from kinetic modeling using the bottle test is in close agreement with this value. The overall findings ultimately revealed that the turbidimetry method could be used to determine stability of typical O/W emulsions with an acceptable level of accuracy.