Ultrasound assisted synthesis of water-in-oil nanoemulsions: Parametric optimization using hybrid ANN-GA approach

Abstract Present study deals with the newly investigated CEMNSE (combined energy mixed surfactant nanoemulsion) method for optimizing the operating parameters concerned with the formation of water-in-oil nanoemulsions (W/O NE). The formulation process was intensified by optimizing the operating parameters of CEMNSE method by minimizing functions of two response variables viz. avg. droplet size (nm) and kinematic viscosity (mm2.s-1). A combined approach of ultrasonic cavitation and isothermal dilution method is used in formulating W/O NE. Optimization is carried out with an integral hybrid genetic algorithm (GA) with back propagation artificial neural network (BPANN) and response surface methodology (RSM) based on rotatable central composite design (RCCD). Combined approach process parameters as input to the proposed models are water fraction (0.05-0.11, w/w), surfactant fraction (0.10-0.020, w/w), power density (21.25-46.75, W. cm-2), and ultrasonication time (4-10, min.). Hybrid GA model predicted optimum values of avg. droplet size and kinematic viscosity as 53.54 nm and 1.459 mm2.s-1, respectively, with errors