STUDIES ON THE STABILITY OF OIL-WATER EMULSIONS USING ANIONIC AND NON-IONIC SURFACTANTS

Abstract

This work reports stability of oil in water macro-emulsions with diesel oil dispersed in a continuous water phase and stabilized either by anionic (Sodium Dodecyl Benzene Sulphonate (SDBS)) or nonionic (Polysorbate80 (Tween80)) surfactants. Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to develop mathematical (empirical) models which relate the amount of emulsion that remains stable after a storage period of 24 hours (Emulsion Stability Index) and turbidity of the emulsion to five parameters viz. oil concentration, surfactant concentration, stirring intensity, agitation time and pH. The predicted models show that both oil and surfactant concentrations have a far more significant effect on the stability of diesel in water emulsions prepared using SDBS, whereas, for emulsions prepared using Tween80, the stability is most affected by oil concentration followed by stirring intensity with surfactant concentration being the least significant. To maximize the emulsion stability the various parameters (oil concentration, surfactant concentration, stirring intensity, agitation time and pH) were optimized using Steepest Ascent Method (SAM) and Genetic Algorithm (GA). The optimum conditions predicted by former for SDBS were: oil concentration (49.33 vol%), surfactant concentration (1.89 wt%), stirring intensity (2450 rpm) and agitation Time (9.05 min) while for Tween80: oil concentration (50 vol%), surfactant concentration (8.2 vol%), stirring intensity (6000 rpm), agitation Time (18.26 min) and pH(11.79). The optimum conditions predicted by latter for SDBS were: oil concentration (50 vol%), surfactant conc. (2 wt%), stirring intensity (5691.33 rpm) and sgitation Time (5 min) while for Tween80: oil concentration (50 vol%), surfactant conc. (5.571vol%), stirring intensity (6000 rpm), agitation Time (5.97 min) and pH (12). The critical micelle concentration (CMC) for SDBS was also determined as a function of oil concentration and it increased with an increase in oil concentration with the CMC at higher oil concentration lying outside the range of surfactant concentration used to predict the model. The validation results demonstrated that the proposed RSM-BBD coupled with BPANN-GA model is useful for the optimization of process parameters for emulsion stability analysis